Large language models (LLMs) and sentiment-aware machine learning are examples of technological developments that have influenced algorithmic trading using non-traditional data sources such as social media. The unpredictable, non-stationary nature of financial data makes stock market prediction difficult. Machine learning has emerged as a powerful paradigm for dynamic market adaptation. The machine learning model’s ability to reduce trading risk and maximise profits has been limited by the current methodologies’ frequent lack of regard for structured risk management. In this paper, we propose a hybrid model combining sentiment analysis and technical indicators along with market data to predict the trend in the market. We also explore the returns obtained from the model and compare them with our risk strategy and traditional buy-and-hold (B&H) strategy. The results show that machine learning can decisively outperform traditional methods if paired with efficient risk management, which contributes to our understanding of machine learning-driven trading.

Vision Large Language Models (VLLMs) demonstrate significant potential for analysing complex geospatial and time-series data in food prediction; however, their reliability is frequently compromised by factual hallucinations, hindering practical utility in dynamic, real-time scenarios. This study introduces RAFT-VLLM, a hybrid framework designed to enhance the factual grounding and predictive reliability of VLLMs for short-term food risk assessment, specifically targeting the LLaVA-1.6 (7B) architecture. The framework uniquely combines targeted VLLM fine-tuning with a Retrieval Augmented Generation (RAG) pipeline. Fine-tuning is performed using a novel, domain-specific multimodal dataset meticulously engineered from ERA5 hourly reanalysis data, creating visual-textual pairs that link meteorological and hydrological states to food risk levels and justifications. Concurrently, the RAG system grounds the VLLM by dynamically injecting relevant historical meteorological data, pre-processed contextual information, and summarised current conditions—primarily derived from offline ERA5 archives—into the model’s prompt. This dual approach equips the VLLM with both specialised domain knowledge through fine-tuning and up-to-date, verifiable context through RAG. By pre-loading and injecting verified, up-to-date information, akin to emerging LLM-based interfaces in national weather services, this framework aims to significantly reduce hallucination rates, enhance the expert-level justification of predictions, and improve overall reliability in interpreting spatial weather plots for food risk forecasting. The study benchmarks this approach against VLLM baselines lacking these augmentations, demonstrating improved accuracy in food risk assessment.

This research work utilizes the University of New South Wales Network Based 2015 (UNSW-NB15) dataset to investigate the dynamic nature of cyber threats, departing from the obsolete Knowledge Discovery and Data Mining competition 1999 (KDD Cup99) dataset. The data preparation pipeline consists of essential procedures aimed at ensuring the integrity and appropriateness of the data for analysis. The method begins by removing null values, thereafter, applying one-hot encoding to categorical features, min-max scaling for data normalization, and label encoding for efficient management of binary labels. The process of feature selection is conducted utilizing the Pearson coefficient correlation. An exhaustive evaluation is conducted on six machine learning models for the purpose of binary classification. The evaluation takes into account key performance measures like accuracy, precision, recall, and F1 score. The Random Forest model demonstrated exceptional performance, with a remarkable accuracy of 99% and a robust F1 score of 98%. Additionally, it exhibited a well-balanced precision and recall at 98%. The Support Vector Machine, Gradient Boosting, Logistic Regression, Decision Tree, and K-Nearest Neighbors models exhibit notable performance, achieving accuracy and F1 scores around at the 98% level. During our investigation into multi-class classification research, we thoroughly examined numerous machine learning models, all of which exhibited robust performance, with accuracy rates ranging from 97% to 98%. The aforementioned results highlight the efficacy of these models in accurately classifying data, regularly achieving high levels of precision, recall, and F1 scores for positive case predictions. This study offers a current viewpoint on the identification of cyber threats and emphasizes the appropriateness of several machine learning models in this rapidly changing field.

Chromosome straightening plays an important role in karyotype analysis. Common straightening methods usually adopt geometric algorithms, which tend to affect the chromosome banding patterns in the process of straightening, resulting in feature changes, loss of details, and poor generalization. To solve these problems, this paper proposes a novel straightening method based on disentanglement representation learning. Our method consists of two main components: the Disentanglement Representation Encoder (DRE) and the Straightening Generator (SG), where DRE discovers and disentangles the bent representation and the content representation in the latent space, while SG is used to generate the straightened chromosome images based on the disentangled representations. Leveraging the bent representation and the content representation disentangled by DRE, our method produces the straightened representation by reducing the bent representation while keeping the content representation unchanged, making straightening chromosome without changing its banding patterns possible. Evaluation results on both the Frechet Initiation Distance (FID) and the Downstream Classification Accuracy (DCA) metrics show that our method achieves good performance.

Skin diseases are a significant global health concern, impacting millions worldwide. Severe diseases like psoriasis and dermatitis can coexist with more benign skin issues like acne and eczema. Primary care physicians in tropical areas often treat patients with skin issues in locations where onchocerciasis and tinea imbricate are prominent, such infections might even take center stage. Usually, skin illnesses are disregarded medically and considered cosmetic, but they can have serious psychosocial effects, especially at an early age, and very few global studies have attempted to quantify the frequency of skin diseases. Nevertheless, the ability to make an accurate diagnosis at an early stage is crucial for successful treatment of complex diseases. However, Skin disease identification is a complex process. We introduce a state-of-the-art approach that uses Mask R-CNN in conjunction with an augmented dataset from the HAM10000 from the Harvard University Open Data Repository to achieve close to 80% accuracy in skin disease classification. We provide an in-depth analysis of our approach, covering data preprocessing, model architecture, training, and evaluation, along with detailed tables presenting training and testing results and associated hyperparameters.

In the global transformation path of our society defined by the ever-evolving environment of AI based tools, applications, approaches, and techniques researchers and professionals in varied domains...

Chromosome instance segmentation plays a crucial role in chromosomal karyotype analysis. However, the overlapping of chromosome instances and their individual morphological differences make accurate chromosome instance segmentation a challenging task. Especially in handling overlapping chromosome instances, traditional segmentation methods tend to confuse instances with one another. To solve these problems, this paper proposes an innovative method named Adaptive2Former. It builds upon our novel devised Adaptive Query Decoder (AQD) module to enhance segmentation precision. The AQD effectively utilizes the [cls] token from the backbone network to dynamically generate adaptive query vectors instead of using fixed queries. This new design leverages the semantic information inside the input images, producing representations more conducive to subsequent segmentation module, thereby improving the model’s segmentation performance. Experiments conducted on our dataset demonstrate that the proposed Adaptive2Former significantly enhances the performance of chromosome instance segmentation compared to Mask2Former and other existing models, achieving results of 97.65% mAP75 and 0.21 Dice Loss.

Several security concerns and efforts to breach system security and prompt safety concerns have been brought to light as a result of the expanding use of LLMs. These vulnerabilities are evident and LLM models have been showing many signs of hallucination, repetitive content generation, and biases, which makes them vulnerable to malicious prompts that raise substantial concerns in regard to the dependability and efficiency of such models. It is vital to have a complete grasp of the complex behaviours of malicious attackers in order to build effective strategies for protecting modern artificial intelligence (AI) systems through the development of effective tactics. The purpose of this study is to look into some of these aspects and propose a method for preventing devastating possibilities and protecting LLMs from potential threats that attackers may pose. Vigil is an open-source LLM prompt security scanner, that is accessible as a Python library and REST API, specifically to solve these problems by employing a sophisticated adversarial machine-learning algorithm. The entire objective of this study is to make use of Vigil as a security scanner. and asses its efficiency. In this case study, we shed some light on Vigil, which effectively recognises and helps LLM prompts by identifying two varieties of threats: malicious and benign.

Digital twin (DT) is an enabling technology that integrates cyber and physical spaces. It is well-fitted for manufacturing setup since it can support digitalized assets and data analytics for product and process control. Conventional manufacturing setups are still widely used all around the world for the fabrication of large-scale production. This article proposes a general DT implementation architecture for engineering objects/artifacts used in conventional manufacturing. It will empower manufacturers to leverage DT for real-time decision-making, control, and prediction for efficient production. An application scenario of Decisional-DNA based anomaly detection for conventional manufacturing tools is demonstrated as a case study to explain the architecture.

This guest edition of Cybernetics and Systems is a broadening continuation of our last year edition titled “Intelligence Augmentation and Amplification: Approaches, Tools, and Case Studies” (Szczer...

Human chromosomes carry genetic information about our life. Chromosome classification is crucial for karyotype analysis. Existing chromosome classification methods do not take into account reasoning, such as: analyzing the relationship between variables, modeling uncertainty, and performing causal reasoning. In this paper, we introduce a knowledge engine for reasoning-based human chromosome classification that stores knowledge of chromosomes via a novel representation structure, the Chromosome Part Description (CPD), and reasons over CPDs by utilizing the probability tree model (PTM) for classification. Each CPD keeps information on a particular feature of chromosomes, while the PTM provides causal reasoning capability taking CPDs as nodes and dependencies between CPDs and types as edges. Experimental results show that the proposed knowledge engine’s performance increases when providing more CPDs and achieves 100% classification accuracy with more than three CPDs.

Karyotyping requires chromosome instances to be segmented and classified from the metaphase images. One of the difficulties in chromosome segmentation is that the chromosomes are randomly positioned in the image, and there is a great chance for chromosomes to be touched or overlap with others. It is always much easier for operators and automatic programs to tackle images without overlapping chromosomes than ones with largely overlapped chromosomes. In order to reduce the processing difficulty, adding a smart image selection procedure ahead of segmentation is practical and necessary. In this paper, we introduce the Smart Karyotyping Image Selection (SKIS) based on Commonsense Knowledge Reasoning. The initial experiment demonstrates that the proposed approach can select the expected images based on reasoning and benefit following karyotyping processes.

This article proposes a mask refinement method for chromosome instance segmentation. The proposed method exploits the knowledge representation capability of Neural Knowledge DNA (NK-DNA) to capture the semantics of the chromosome’s shape, texture, and key points, and then it uses the captured knowledge to improve the accuracy and smoothness of the masks. We validate the method’s effectiveness on our latest high-resolution chromosome image dataset. The experimental results show that our proposed method’s mask average precision (MaskAP) is 3.66% higher than Mask R-CNN and outperforms advanced Cascade Mask R-CNN by 1.35%.

In Agile IT projects, promoting effective knowledge sharing is essential not only for achieving success but also for supporting Sustainable Development Goals (SDGs). However, Companies using virtual teams may face challenges in coordinating work, particularly when teams are distributed across different time zones, ultimately hindering their ability to consistently share knowledge. This can lead to delays and inefficiencies, ultimately impacting the project outcomes and the organization's profitability. To ensure sustainable knowledge sharing, a comprehensive framework is necessary that addresses the environmental, social, economic, and political aspects of the project. This paper proposes a framework that combines the Follow-the-Sun (FTS) technique and the Sustainable Knowledge Sharing Model, enabling 24-hour knowledge sharing in virtual teams and benefiting IT agile projects.

The Semantic Web concept is proposing a future concept of the WorldWideWeb (WWW) where both humans and man-made systems are able to interconnect and exchange knowledge.One of the challenges of Semantic Web is smart and trusted accommodation of knowledge in artificial systems so it can be unified, enhanced, reused, shared, communicated and distributed with added aptitude.Our research represents an important component of addressing the above challenge and exciting, cutting-edge exploration trend in the general area of developing tool for intelligence augmentation.

In order to overcome work environment challenges and remain competitive in the market, organisations must adapt. An organisation's competitiveness can be improved through knowledge sharing; however, improvement without responsibility can have a negative impact on the sociotechnical environment which people cannot fully comprehend. According to researchers, business involvement in sustainable development goals remains minimal [51]. As a result, a sustainable consciousness is crucial to improve the business. In the project profession, sustainability is a business approach that balances the environmental, social, economic, and political aspects of project-based work to fulfil the demands of stakeholders without jeopardising or overburdening the availability of natural resources for future generations [33]. This study goes a step further and proposes a theoretical design that aims to ensure sustainable development during conceptual knowledge transfer in Agile IT projects.

Intelligence amplification systems and technologies have gained significant interest from academia and industry during the past few decades. One of the main reasons behind this trend is the fact that most experts agree that truly intelligent artificial system is yet to be developed. The question increasing often asked is this: Is full replication of human intelligence desirable key aim in intelligence related technology and research? In this context, the concept of Augmented Intelligence, also known as Cognitive Augmentation or Intelligence Amplification (IA) comes into play. One of the main reasons behind this interest in this concept is the potential of such technologies to revolutionize human life as they intend to work under complex decision-making environments, adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior. The combination of these properties aims to enhance human capabilities and create more intelligent and efficient human-centered environments. In this paper we discuss how Decisional DNA (DDNA), a multi-domain knowledge structure that has the Set of Experience Knowledge Structure (SOEKS) at its core can be utilized as an experience-based intelligence augmentation in a number of different domains.

Smart Virtual Product Development (SVPD) system provides effective use of information, knowledge, and experience in industry during the process of product development in Industry 4.0 scenario. This system comprises of three primary modules, each of which has been developed to cater to a need for digital knowledge capture for smart manufacturing in the areas of product design, production planning, and inspection planning. Manufacturing Capability Analysis and Process Planning (MCAPP) module is an important module of the SVPD system, and it involves the provision of manufacturing knowledge to experts working on product development at the early stages of the product lifecycle. In this research, we firstly describe the structure and working mechanism of the SVPD system’s MCAPP module. This is followed by validation of the MCAPP module’s Manufacturing Process Planning (MPP) sub-module against the key performance indicators (KPIs) by using our threading tap case study. Our results verify the feasibility of our approach and show how manufacturing knowledge relating to features and functions can be used to enhance the manufacturing process across similar products during the early stages of product development. An analysis of the basic concepts and methods of implementation show that this is an expert system capable of supporting smart manufacturing which can play a vital role in the establishment of Industry 4.0.

Organizations must adapt their resources to meet the challenges associated with changes in the work environment in order to remain competitive in the information era. Several research findings identify knowledge sharing as a means for an organization to improve its competitiveness. Knowledge sharing can be defined in a variety of ways, but it essentially refers to the exchange of knowledge from an information giver to an information receiver. This is a purposeful activity that adds value to the client organization, particularly in IT system that employs Agile methodology. For the scope of this paper, we are going to consider only Agile knowledge transfer in IT projects that occurs in two angles: business knowledge transfers from client to consultant; and IT technical knowledge transfers from consultant to client. However, when interdisciplinary teams are involved in Agile IT projects, the knowledge transfer mentioned before remains inefficient once the knowledge loss persists throughout the project life cycle. The conversion of conceptual knowledge, which only exists in the brains and minds of individuals, into explicit knowledge is essential for organizations to gain and maintain competitive advantages over its competitor. This study proposes an alternative conceptual framework to address conceptual knowledge transfer in IT projects that use Agile methodology.

In this paper, we propose a knowledge sharing oriented approach to enable a robot to reuse other robots' knowledge by adapting itself to the inverse dynamics model of the knowledge-sharing robot. The purpose of this work is to remove the heavy fine-tuning procedure required before using a new robot for a task via reusing other robots' knowledge. We use the Neural Knowledge DNA (NK-DNA) to help robots gain empirical knowledge and introduce a Knowledge Adaption Module (KAM) utilizing the deep neural networks (DNN) for knowledge reuse. The initial experiment shows that the target robot can adapt to the inverse dynamic model of the source robot via our KAM and reuse the knowledge shared by the source robot.

Cognition in computer sciences refers to the ability of a system to learn at scale, reason with purpose, and naturally interact with humans and other smart systems, such as humans do. To enhance intelligence, as well as to introduce cognitive functions into machines, recent studies have brought humans into the loop, turning the system into a human–AI hybrid. To effectively integrate and manipulate hybrid knowledge, suitable technologies and guidelines are required to sustain the human–AI interface so that communication can occur. However, traditional Knowledge Management (KM) and Knowledge Engineering (KE) approaches encounter problems when dealing with cutting-edge technologies, imposing impediments for the use of traditional methods in cognitive systems (CS). This paper presents a brief overview of the Smart Knowledge Engineering for Cognitive Systems (SKECS), which is based on methods, technologies, and procedures that bring innovations to the fields of KE, KM, and CS. The goal is to bridge the gap in the hybrid cognitive interface by the combination of experience-based knowledge representation with the use of emerging technologies such as deep learning, context-aware indexing/retrieval, active learning with a human-in-the-loop, and stream reasoning. In this work Set of Experience Knowledge Structure (SOEKS) and Decision DNA (DDNA) is extended to the visual domain and utilized for knowledge capture, representation, reuse, and evolution. These technologies are examined throughout the layers of SKECS for applications in knowledge acquisition, formalization, storage/retrieval, learning, and reasoning, with the final goal of achieving knowledge augmentation (wisdom) in CS. Features of the SKECS and their practical implementation is discussed through a case study—the Cognitive Vision Platform for Hazard Control (CVP-HC)—suggesting that methods, techniques and procedures comprising the SKECS are suitable for advancing systems toward augmented cognition.

The entire manufacturing spectrum is transforming with the advent of Industry 4.0. The features of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) were utilized for developing Virtual Engineering Objects (VEO), Virtual Engineering Process (VEP) and Virtual Engineering Factory (VEF), which in turn facilitate the creation of smart factories. In this study, DDNA based Machine Monitoring for Total Maintenance in Industry 4.0 framework is demonstrated. The concept of VEO is used for the Tool and Equipment Monitoring, while for the Plants Operations Monitoring and Quality Monitoring, VEP and VEF are employed. Query extraction feature of DDNA is exploited for Adaptive Control. This study shows that Machine Efficiency (ME) can be monitored along with analysis of machine KPI’s like breakdown time, setting time, and other losses. Moreover, reports can be generated efficiency-wise, breakdown-wise, operator-wise. The data of these reports is used to predict and make future decisions related to machine maintenance.

Most experts agree that truly intelligent artificial system is yet to be developed. The main issue that still remains a challenge is imposing trust and explainability into such systems. However, is...

This paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine our approach through an initial case study. The experiment results show that the proposed method can transform the implicit knowledge stored in the NK-DNA into explicitly represented decision trees bringing fair interpretability to neural network-based intelligent systems.

Machine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) is applied to facilitate Machine Learning. For effective and efficient decision-making in Machine Learning, the environment’s own experience is captured, stored and reused using the DDNA technique. The proposed approach is implemented on practical test cases like a Chatbot. Decisional DNA gathers explicit experiential knowledge based on formal decision events and uses this knowledge to support decision-making processes. The experimental test and results of the presented implementation of Decisional DNA Chatbot case studies support it as a technology that can improve and be applied to the technology, enhancing intelligence by predicting capabilities and facilitating knowledge engineering processes.

In this paper we describe how our Smart Virtual Product Development (SVPD) system can be used to enhance product inspection planning. The SVPD system is comprised of three main modules, these being the design knowledge management (DKM) module, the manufacturing capability and process planning (MCAPP) module, and the product inspection planning (PIP) module. Experiential knowledge relating to formal decisional events is collected, stored and used by the system in the form of set of experiences (SOEs). Here we discuss the working mechanism of the PIP module and show how experiential knowledge relating to the inspection of products that have features and functions in common can be used to enhance product inspection planning during early stages of product development. Our discussion commences with an introduction to fundamental concepts and a general system overview. We then describe the development of our SVPD system’s PIP module, and a case study we undertook for validation purposes. Results of the case study show that our system is capable of supporting product inspection planning in smart manufacturing, and thus has a vital role to play in Industry 4.0.

The fourth industrial revolution known as Industry 4.0 is reshaping and evolving the way industries produce products and individuals live and work therefore, gaining massive attraction from academia, business, and politics. The manufacturing industries are optimistic regarding the opportunities that Industry 4.0 may offer such as improved efficiency, productivity and customization. The present research contributes to the Industry 4.0 literature by identifying, modeling, analyzing, and prioritizing the challenges in implementing Industry 4.0 in manufacturing industries. In doing so, the article first introduces the interpretive structural modeling (ISM) to develop the hierarchical relationships among the challenges and analyzes their mutual interactions. Further, “Matrice d’Impacts Croises Multiplication Appliquee aun Classement” (MICMAC) analysis is used to categorize the challenges into four categories, namely autonomous, driver, dependent, and linkage based on their driving power and dependence power. Moreover, fuzzy analytic hierarchy process (F-AHP) methodology is used to prioritize the challenges based on three criteria: driving power, dependence power, and change management. The hierarchical model developed through ISM methodology shows that “lack of vision and leadership from top management (C12), lack of skills training program and education (C2), and uncertainty of return on investment (C9)” are the major challenges in implementing Industry 4.0 in manufacturing industries. The findings of F-AHP analysis suggest that “lack of vision and leadership from top management (C12), lack of skilled workforce (C3), lack of skills training program and education (C2), and uncertainty of return on investment (C9)” are some of the major challenges of implementing Industry 4.0. Finally, the obtained results show how challenges affect other so that to uncover the root cause triggering the other challenges. The industrial practitioners and managers can then take advantage of these analyses to know which challenge acts as the main barrier in implementing Industry 4.0 and to be focused first in order to reach a solution.

Knowledge Management (KM) research outputs have been expanding exponentially in the past years, generating diversified topics, which lack integration and classification. It has been challenging for experts to classify KM because of its versatile open fields, and in our view, it contributes to the technocratic approach remaining behind the organizational approach. This paper highlights a way to classify KM publications through a pattern that will support technocratic developments representing knowledge in a more explicit form. This study uses a classification method that uses a template in a taxonomy shape, executing some procedures and allowing an accurate identification and organization of KM research outputs. The proposed taxonomy method is proven on a set of 150 different KM publications from the last 15 years. This scheme is grouped into two main categories: Conceptual and Empirical which could enable academics and practitioners alike to better understand the current gaps that are prevalent in KM.

In this paper we propose a novel recommendation approach using past news click data and the Neural Knowledge DNA (NK-DNA). The Neural Knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for news recommendation tasks on the MIND benchmark dataset. By taking advantages of NK-DNA, deep learning, and the SOEKS technologies, our approach can learn from users’ past behaviors to form reading preference of the user, and reuse learned knowledge for improving the recommendation performance.

Segmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on a smart, automated, and robust segmentation approach for brain tumor using a modified 3D U-Net architecture. The pre-operative multimodal 3D-MRI scans of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) are used as data. Our proposed approach solves the problem of memory and system resource constraints by robustly applying dense network training on image patches of 3D volumes. It improves the border region artifact detection by applying convolutions at an appropriate phase in the proposed neural network. Multi-class imbalance data are handled by using Categorical Cross Entropy (CCE) loss developed by combining the Weighted Cross Entropy (WCE) with Weighted Multi-class Dice Loss (WMDL) functions, which enables the network to perform smart segmentation of the smaller tumorous regions. The proposed approach is tested and evaluated for the challenge datasets of multimodal MRI volumes of tumor patients. Experiments are performed to compute the average dice scores on BraTS-2019 and BraTS-2020 datasets for the whole tumor region.

The aim of this Guest Edition of Cybernetics and Systems is to present a wide-ranging scale of concepts and processes being currently researched, developed, and evaluated in real life settings in a...

This paper presents the idea of supporting product inspection planning process during the early stages of product life cycle for the experts working on product development. Aim of this research is to assist a collaborative product development process by using Smart Virtual Product Development (SVPD) system, which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The proposed system is developed to support three key aspects of industrial product development i.e. design, manufacturing, and product inspection. Therefore, it comprises of three main modules; design knowledge management (DKM), manufacturing capability and process planning (MCAPP), and product inspection planning (PIP). It collects, stores, and uses experiential knowledge from formal decisional events in the form of set of experience (SOE). This research enlightens the working mechanism of the PIP module, and shows how experiential knowledge related to product inspection can be used during the early stages of product development process. This experiential knowledge is extracted and stored from similar products having some common features and functions. First, the basic description and principles of the approach are introduced, then the prototype version of the system is developed and tested for product inspection planning (PIP) module for the case study, which verifies the feasibility of the proposed approach. The presented system successfully supports smart manufacturing and can play a vital role in Industry 4.0.

For the last few years, the fourth industrial revolution, known as Industry 4.0, has been a hot topic among academics. Industry 4.0 literature involves researches presenting studies related to its different aspects including challenges, opportunities, implementation and adoption. However, a detailed study of challenges and barriers towards the Industry 4.0 implementation in manufacturing industries is missing. Hence, this paper aims to study and analyze the Industry 4.0 implementation challenges in manufacturing industries based on the expert opinions and the Interpretive Structural Modelling (ISM). The ISM analysis investigates the challenges in a structural base, finds the relationships between these challenges and finally shows how challenges affect each other to uncover the root cause triggering the other challenges. The industrial practitioners and managers can then take advantage of this analysis to know which challenge acts as the main barrier towards Industry 4.0 implementation and to be focused first in order to reach a solution.

Current computer vision systems, especially those using machine learning techniques are data-hungry and frequently only perform well when dealing with patterns they have seen before. As an alternative, cognitive systems have become a focus of attention for applications that involve complex visual scenes, and in which conditions may vary. In theory, cognitive applications uses current machine learning algorithms, such as deep learning, combined with cognitive abilities that can broadly generalize to many tasks. However, in practice, perceiving the environment and adapting to unforeseen changes remains elusive, especially for real time applications that has to deal with high-dimensional data processing with strictly low latency. The challenge is not only to extract meaningful information from this data, but to gain knowledge and also to discover insight to optimize the performance of the system. We envision to tackle these difficulties by bringing together the best of machine learning and human cognitive capabilities in a collaborative way. For that, we propose an approach based on a combination of Human-in-the-Loop and Knowledge Discovery in which feedback is used to discover knowledge by enabling users to interactively explore and identify useful information so the system can be continuously trained to gain previously unknown knowledge and also generate new insights to improve human decisions.

This paper proposes the Smart Virtual Product Development (SVPD) system, which enhances the industrial product manufacturing processes. The proposed system comprises of three main modules: design knowledge management (DKM), manufacturing capability analysis and process planning (MCAPP), and product inspection planning (PIP). Smart virtual product development system collects, stores, and uses experiential knowledge from formal decisional events in the form of set of experience (SOE). This research explains the working mechanism of MCAPP module, and shows how manufacturing knowledge of similar products having some common features and functions is used to enrich the manufacturing process. First, the basic description and principles of the approach are introduced, then the prototype version of the system is developed and tested for manufacturing capability and process planning (MCAPP) module for the case study, which verifies the feasibility of the proposed approach. The presented system successfully supports smart manufacturing, and can play a vital role in Industry 4.0.

Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE).

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.

Artificial Intelligence (AI), or Augmented Intelligence (AI)? AI vs AI. Who is the winner? Increasingly often, we tend to agree that, at least at the current state of affairs, it is augmentation ra...

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet of Things that enables IoT to extract knowledge from past experiences, as well as to store, evolve, share, and reuse such knowledge aiming for smart functions. By catching decision events, this approach helps IoT gather its own daily operation experiences, and it uses such experiences for knowledge discovery with the support of machine learning technologies. An initial case study is presented at the end of this paper to demonstrate how this approach can help IoT applications become smart: the proposed approach is applied to fitness wristbands to enable human action recognition.

With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).

Together with the fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing HAR methods depend on exploratory case-based shallow feature learning architectures, which struggle with correct activity recognition when put into real-life practice. To tackle this problem, we propose a novel approach that utilizes the convolutional neural networks (CNNs) and the attention mechanism for HAR. In the presented method, the activity recognition accuracy is improved by incorporating attention into multihead CNNs for better feature extraction and selection. Proof of concept experiments are conducted on a publicly available data set from wireless sensor data mining (WISDM) lab. The results demonstrate a higher accuracy of our proposed approach in comparison with the current methods.

This paper presents a system capable of enhancing product development process for industrial manufactured products. The system is known as Smart Virtual Product Development (SVPD), and it helps in decision making by using explicit knowledge of formal decision events. It stores and reuses the past decisional events or set of experiences related to different activities involved in industrial product development process i.e. product design, manufacturing, and product inspection. This system can be potentially used in large enterprises manufacturing a range of similar products in mass production, or a group of small and medium enterprises (SMEs). This research explains that how product manufacturing can be enhanced by using SVPD in Industry 4.0, where cyber-physical systems have to play the key role. The analysis of basic concepts and implementation method proves that this is an expert system facilitating product manufacturing which can play a vital role towards Industry 4.0.

Cognitive Cyber Physical Systems (C-CPS) have gained significant attention from academia and industry during the past few years. One of the main reasons behind this interest is the potential of such technologies to revolutionize human life since they intend to work robustly under complex visual scenes, which environmental conditions may vary, adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior like predicting possible events based on cognitive capabilities that are able to sense, analyze, and act based on their analysis results. However, perceiving the environment and translating it into knowledge to be useful for the decision making process, still remains a challenge for real time applications due to the complexity of such process. In this paper, we present a multi-domain knowledge structure based on experience, which can be used as a comprehensive embedded knowledge representation for C-CPS, addressing the representation of visual content issue and facilitating its reuse. The implementation of such representation has been tested in a Cognitive Vision Platform for Hazard Control (CVP-HC) which aims to manage of workers’ exposure to risks in industrial environments, facilitating knowledge engineering processes through a flexible and adaptable implementation.

Industry 4.0 offers a comprehensive, interlinked, and holistic approach to manufacturing. It connects physical with digital and allows for better collaboration and access across departments, partners, vendors, product, and people. Consequently, it involves complex designing of highly specialized state of the art technologies. Thus, companies face formidable challenges in the adoption of these new technologies. In this paper, critical components of Industry 4.0, their significance and challenges as identified in the literature are presented. Furthermore, a test case framework for the implementation of Industry 4.0 is proposed. The system covers four layers: decision support, data processing, data acquisition and transmission and sensors. Condition monitoring data from machines and shop floor are captured, stored, organized and visualized in real time. Knowledge representation technique of SOEKS/DDNA is used for doing the semantic analysis of the data, Virtual Engineering Object (VEO), Virtual Engineering Process (VEP) and Virtual Engineering Factory (VEF) are used for creating virtual engineering objects, process and factory respectively, Python and its utility Bokeh is used for visualization. The proposed Industry 4.0 framework will make it possible to gather and analyze data across machines, processes and resources supporting faster, flexible, and more efficient control and production of higher-quality goods at reduced costs.

This paper presents the concept of smart virtual product development (SVPD) system capable of supporting industrial product development process. It enhances the decision making process during different stages and activities involved in product development i.e. product design, manufacturing, and its inspection planning. The enhancement is achieved by using the explicit knowledge of formal past decision events, which are captured, stored, and recalled in the form of set of experiences. The basic description and principle of the approach are introduced first, and then the porotype version of the system is developed and tested. Working of the design knowledge management module of the system is demonstrated with the case study, which verifies the feasibility of the proposed approach. The presented system successfully supports smart product design and it can play a vital role in Industry 4.0 development.

In this article, we introduce a novel security mechanism, the Smart Synergy Security (3S). The mechanism uses the Set of Experience Knowledge Structure (SOEKS) and the synergy of security methods in different domains to provide the global optimal security strategy. The proposed strategy is taking into account the characteristics of information security (i.e. confidentiality, integrity, availability, controllability, and reviewability) imposed in different domains in Internet of Vehicles (IoV). The SOEKS is used to represent knowledge, and is combined with the data flow in each domain. Initial experiments demonstrate that the proposed approach is able to find the optimal solution under different conditions for multi-domain security problems in IoV.

This work is part of an effort for the development of a Cognitive Vision Platform for Hazard Control (CVP-HC) for applications in industrial workplaces, adaptable to a wide range of environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment (PPE). Given the results of previous analysis of supervised techniques for the problem of classification of a few PPE (boots, hard hats, and gloves extracted from frames of low resolution videos), which found the Deep Learning (DL) methods as the most suitable ones to integrate our platform, the objective of this paper is to test two DL algorithms: Single Shot Detector (SSD) and Faster Region-based Convolutional Network (Faster R-CNN). The testing uses pretrained models on a second version of our PPE dataset (containing 11 classes of objects) and evaluates which of examined algorithms is more appropriate to compose our system reasoning.

In order to allocate resources effectively according to the production plan and to reduce disturbances, a framework for smart production performance analysis is proposed in this article. Decisional DNA based knowledge models of engineering objects, processes and factory are developed within the proposed framework. These models are the virtual representation of manufacturing resources, and with help of Internet of Things, are capable of capturing the past experience and formal decisions. A case study for the smart tool performance analysis is presented in which information of key tool parameters like tool life, surface integrity, tool forces and chip formation can be sensed in real-time, and predictions can be made according to specific requirements. This framework is capable of creating a cyber-physical conjoining of the bottom-level manufacturing resources and thus can work as a technological basis for smart factories and Industry 4.0.

Computer vision research and applications have their origins in 1960s. Limitations in computational resources inherent of that time, among other reasons, caused research to move away from artificial intelligence and generic recognition goals to accomplish simple tasks for constrained scenarios. In the past decades, the development in machine learning techniques has contributed to noteworthy progress in vision systems. However, most applications rely on purely bottom-up approaches that require large amounts of training data and are not able to generalize well for novel data. In this work, we survey knowledge associated to Computer Vision Systems developed in the last ten years. It is seen that the use of explicit knowledge has contributed to improve several computer vision tasks. The integration of explicit knowledge with image data enables the development of applications that operate on a joint bottom-up and top-down approach to visual learning, analogous to human vision. Knowledge associated to vision systems is shown to have less dependency on data, increased accuracy, and robustness.

The combination of vision and sensor data together with the resulting necessity for formal representations builds a central component of an autonomous Cyber Physical System for detection and tracking of laborers in workplaces environments. This system must be adaptable and perceive the environment as automatically as possible, performing in a variety of plants and scenes without the necessity of recoding the application for each specific use. But each recognition system has its own inherent limits, especially those which task is to work in unidentified environments and deal with unknown scenarios and specifications. The platform described in this paper takes this into account by connecting the probabilistic area of event detection with the logical area of formal reasoning in a Cognitive Vision Platform for Hazard Control (CVP-HC). In order to support formal reasoning, additional relational scene information is supplied to the recognition system. In this platform, the contextual knowledge is used to improve the recognition and interpretation of detected events. This relational data together with all collected information is represented explicitly as a Set of Experience Knowledge Structure (SOEKS), categorized and stored as a Decisional DNA (DDNA), a decisional safety fingerprint of a company. By these means, the systems assesses and addresses critical unsafe behaviors whilst gives support to an explicit long term culture change process. By the use of context the CVP-HC is capable adjust accordingly without the need of rewriting the application’s code every time conditions or specifications changes.

Knowledge and experience are important requirements for product development. The aim of this paper is to propose a systematic approach for industrial product development. This approach uses smart knowledge management system comprising of set of experience knowledge structure and decisional DNA (DDNA) along with virtual engineering tools (virtual engineering object, virtual engineering process, and virtual engineering factory). This system provides a new direction to researchers working on product development, especially designers and manufacturers. It will reduce their communication gap by allowing them to work on the same platform. The proposed system adopts an early consideration of manufacturing issues. Therefore, it can shorten product development cycle time, minimize overall development cost, and ensure a smooth transition into production. The proposed system is dynamic in nature because it updates itself after every time a new decision related to product development activity is made. Product development process can be performed systematically and efficiently using this system as it stores knowledge of experiences of different activities.

In this paper, we propose a novel intrusion detection approach using past driving experience and the neural knowledge DNA for in-vehicle information system security. The neural knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for classifying malicious vehicle control commands based on learning from past valid driving behavior data on a simulator.

This work is part of an effort to develop of a knowledge–vision integration platform for hazard control in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment. The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting experiences from a new scenario, querying the user, and using their feedback to retrain the system through a hierarchical recognition structure using convolutional neural network (CNN). Thereafter, in contrast to the random sampling, the concept of active learning based on pruning of redundant points is tested. Results obtained from both random sampling and active learning are compared with a rigid systems that is not capable to aggregate new experiences as it runs. From the results obtained, it can be concluded that the classification accuracy improves greatly by adding new experiences, which makes it possible to customize the service according to each scenario and application as it functions. In addition, the active learning approach was able to reduce the user query and slightly improve the overall classification performance, when compared with random sampling.

Computer integrated manufacturing (CIM) has enormous benefits as it increases the rate of production, reduces errors and production waste, and streamlines manufacturing sub-systems. However, there are some new challenges related to CIM operating in the Internet of Things/Internet of Data (IoT/IoD) scenarios associated with Industry 4.0 and cyber-physical systems. The main challenge is to deal with the massive volume of data flowing between various CIM components functioning in virtual settings of IoT. This paper proposes decisional DNA-based knowledge representation framework to manage the storage, analysis, and processing of data, information, and knowledge of a typical CIM. The framework utilizes the concept of virtual engineering object and virtual engineering process for developing knowledge models of various CIM components such as automatic storage and retrieval systems, automatic guided vehicles, robots, and numerically controlled machines. The proposed model is capable of capturing in real time the manufacturing data, information and knowledge at every stage of production, that is, at the object level, the process level, and at the factory level. The significance of this study is that it will support decision-making by reusing the experience, which will not only help in effective real-time data monitoring and processing, but also make CIM system intelligent and ready to function in the virtual Industry 4.0 environment.

Smart (intelligent) systems processing data, information, and knowledge became the primary area of research interest in our semantic society with Internet of Things (IoT) and Cyber Physical Systems...

Knowledge-based engineering systems are founded upon integration of knowledge into computer systems and are one of the core requirements for the future Industry 4.0. This paper presents a system called smart innovation engineering (SIE) capable of facilitating product innovation process semi-automatically. It enhances decision-making processes using the explicit knowledge of formal decision events. The SIE system carries the promise to support the innovation processes of manufactured products in a quick and efficient way. It stores and reuses past decisional events or sets of experiences related to innovation issues, which significantly enhances innovation progression. The analysis of basic concepts and implementation method proves that SIE system is an advanced form of cyber physical systems. It is flexible, systematic, fast, and supports customization. It can play a vital role toward Industry 4.0 development.

Acting over the last two decades as an editor and associate editor for a number of international journals in the general area of systems science with particular focus on smart systems, as well as c...

In this paper, we present the idea of Smart Innovation Engineering (SIE) System and its implementation methodology. The SIE system is semiautomatic system that helps in carrying the process of product innovation. It collects the experiential knowledge from the formal decisional events. This experiential knowledge is collected from the group of similar products having some common functions and features. The SIE system behaves like a group of experts in its domain as it collects, captures, and stores the experiential knowledge from similar products as well as reuses this experiential knowledge that ultimately enhances the innovation process of manufactured goods. Moreover, with SIE in hand, entrepreneurs and manufacturing organizations will be able to take proper, enhanced decisions and most importantly at appropriate time. The system gains expertise each time a decision is taken and stored in the form of set of experience that can be used in future for similar queries. Implementation of the SIE system using Set of Experience Knowledge Structure and Decisional DNA for case study suggests that the SIE system is capable of capturing and reusing the innovation-related experiences of the manufactured products. The case study confirmed that the SIE system can be beneficial for entrepreneurs and manufacturing organizations for efficient decision making in the product innovation process.

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade.The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allowing people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate standard knowledge sharing among different IoT systems are still open issues to be addressed. In this paper, we propose a novel approach, the Experience-Oriented Smart Things (EOST), that utilizes deep learning and knowledge representation concept called Decisional DNA to help IoT systems acquire, represent, and store knowledge, as well as share it amid various domains where it can be required to support decisions. Decisional DNA motivation stems from the role of deoxyribonucleic acid (DNA) in storing and sharing information and knowledge. We demonstrate our approach in a set of experiments, in which the IoT systems use knowledge gained from past experience to make decisions and predictions. The presented initial results show that the EOST is a very promising approach for knowledge capture, representation, sharing, and reusing in IoT systems.

In this paper, we propose a Neural Knowledge DNA (NK-DNA)-based framework that is capable of learning from the car’s daily operations and reusing such learned knowledge in future tasks. The NK-DNA is a novel knowledge representation and reasoning approach designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. We examine our framework for drivers’ classification based on their driving behaviors. The experimental data are collected via smartphone sensors. The initial results are presented, and the direction for our future research is defined.

This paper presents a framework for monitoring, analysing and decision making for a smart manufacturing environment. We maintain that this approach could play a vital role in developing an architecture and implementation of Industry 4.0. The proposed model has features like experience based knowledge representation and semantic analysis of engineering objects and manufacturing process. It is also capable of continuous real time visualization of key performance indicators (KPI's) and supports M2M communications over novel protocols like OPC-UA. Our model covers the industrial manufacturing cycle right from capturing raw data at machine level, converting it into useful information, doing semantics analysis and performs real time KPI visualization.

In recent times, traditional manufacturing is upgrading and adopting Industry 4.0, which supports computerization of manufacturing by round-the-clock connection and communication of engineering objects. Consequently, Decisional DNA-based knowledge representation of manufacturing objects, processes, and system is achieved by virtual engineering objects (VEO), virtual engineering processes (VEP), and virtual engineering factories (VEF), respectively. In this study, assimilation of VEO-VEP-VEF concept in the Cyber-physical system-based Industry 4.0 is proposed. The planned concept is implemented on a case study. Also, Decisional DNA features such as similarity identification and phenotyping are explored for validation. It is concluded that this approach can support Industry 4.0 and can facilitate in real time critical, creative, and effective decision making.

This guest edition follows research progress in some of the most promising study directions signaled in our past year’s Cybernetics and Systems (CBS) volume focused on experience and knowledge-rela...

Knowledge and experience are essential requirements for product innovation. The presented paper proposes a systematic approach for product innovation support using a Smart Knowledge Management System comprising a Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). This proposed system is dynamic in nature because it updates itself every time a new decision related to innovation is made. Through this system, the product innovation process can be performed semiautomatically and efficiently because it stores knowledge of past experiences of innovative decisions.

In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest neighbors), logistic regression, and AdaBoost in classification tasks, and the results show that our approach is very promising with regard to the enhancement of the accuracy of knowledge-based predictions required in complex decision-making problems.

This paper reviews the theories, parallels and variances between Virtual Engineering Object (VEO) / Virtual Engineering Process (VEP) and Cyber Physical System (CPS). VEO and VEP is an experience based knowledge representation of engineering objects and processes respectively. Cyber–physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. The analysis of basic concepts and implementation method proves that VEO/VEP is a specialized form of CPS and it can play a vital role in the structure building of Industry 4.0. Integration of the two models may result in intelligent machines and advanced analytics.

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.

In this article we propose the concept, its framework, and implementation methodology for Virtual Engineering Objects (VEO). A VEO is the knowledge representation of an engineering object that embodies its associated knowledge and experience. A VEO is capable of adding, storing, improving, and sharing knowledge through experience. Moreover, it is demonstrated that VEO is a specialization of a Cyber-Physical System (CPS). In this article, it is shown through test models how the concept of VEO can be implemented with the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The test model confirmed that the concept of VEO is able to capture and reuse the experience of engineering artifacts, which can be beneficial for efficient decision-making in industrial design and manufacturing.

The discipline of Knowledge Engineering (KE), with its subdiscipline Experience Engineering (EE), has become a critical element for people, machines, organizations, and other entities that need to ...

In this article, we present a novel approach utilizing Decisional DNA to help the Internet of Things capture decisional events and reuse them for decision making in future operations. The Decisional DNA is a domain-independent, standard and flexible knowledge representation structure that allows its domains to acquire, store, and share experiential knowledge and formal decision events in an explicit way. We apply this approach to our current work—SmartBike, a sensor-equipped bicycle built under the concept of Internet of Things. By using Decisional DNA and machine learning algorithms, the SmartBike is able to distinguish its user's patterns based on past riding data. The presented conceptual approach demonstrates how Decisional DNA can be applied to the Internet of Things and bring to them intelligence required by forthcoming semantic networks.

The paper presents the hybrid fuzzy- SOEKS approach to the Polish Internet mortgage market, which is treated as an example of a fast changing market. Firstly, the market and market problems are described. Then, the first approach to the market problems is presented: the complete fuzzy model which was built basing on the rules. The fuzzy model is presented on one real data case. Next, the new approach, called Set of Experience Knowledge Structure (SOEKS) is adopted and presented basing on the same data. Instead of using data stored in the rules (fuzzy model), the market is presented using the experience stored in SOEKS. Finally, both approaches are compared with regard to their respective advantages and disadvantages, and some future works are described.

This paper illustrates the idea of Virtual Engineering Object (VEO) powered by Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). A VEO is the knowledge representation of an engineering object, having embodiment of all its associated knowledge and experience within it. Moreover, VEO is a specialization of Cyber-Physical System (CPS) in terms of that its extension in knowledge gathering and reuse, whereas CPS is only aimed towards data and information management. The SOEKS/DDNA is a flexible and standard knowledge representation structure to acquire and store experiential knowledge. The article also presents a case study to demonstrate implementation of VEO in the manufacturing scenario. The decision making in industrial design and manufacturing will benefit from this approach, as it includes capturing, storage and reuse of experience and knowledge of an engineering artefact.

The Decisional DNA is an artificial intelligence system that uses prior experiences to shape future decisions. Decisional DNA is written in the Set Of Experience Knowledge Structure (SOEKS) and is capable of capturing and reusing a broad range of data. Decisional DNA has been implemented in several fields including Alzheimer's diagnosis, geothermal energy and smart TV. Decisional DNA is well suited to use in robotics due to the large amount of data available and the generally repetitive nature of the tasks robots perform. However, there is very little evidence about the system's performance in this application. This project aims to assess the viability of SOEKS in robotics. Several knowledge representation approaches were explored then coded in the Java programming language. A hardware platform was constructed from readily available electronics and set up to be compatible with the Java language. Codes were installed on the hardware platform and tested by conducting a series of feature mapping tasks. Success of this project could lead to the future use of SOEKS in robot control.

This article addresses the issues associated with using ever-increasing amounts of information and knowledge more effectively, and taking advantage of knowledge generated through experience. A hybrid structure, the Smart Experience-based Knowledge Analysis System (SEKAS), is put forward in this paper to address issues of knowledge management and use. SEKAS combines a set of experience knowledge structures (SOEKS) with multiple techniques to provide a comprehensive knowledge management approach capturing, discovering, reusing and storing knowledge for the users. The SEKAS integrates a novel Decisional DNA (DDNA) knowledge structure with the traditional web crawler technologies. DDNA, as a knowledge representation platform, can help deal with noisy and incomplete data, with learning from experience, and with making precise decisions and predictions in vague and fuzzy environments. The paper outlines the investigation of the combination of DDNA and feature selection algorithms to guarantee the future performance for prediction. The proposed approaches are general and extensible in terms of both designing novel algorithms, and in the application to other domains. The SEKAS integrates the evolutionary algorithm, NSGA-II, using experience that is derived from a former decision event, to improve the evolutionary algorithm's ability to find optimal solutions rapidly and efficiently. The SEKAS application to solve a travelling salesman's problem shows that this new proposed hybrid model can find optimal or close to true, Pareto-optimal solutions in a fast and efficient way.

This article reviews research work on set of experience knowledge structure (SOEKS)-decisional DNA (DDNA) done in the past, ongoing, and planned for the future. Firstly, the concept of the knowledge representation technique of SOEKS-DDNA is discussed, and then an attempt is made to organize the past research related with it in chronological order. This work focuses on the review on SOEKS-DDNA, its application in different domains, the various implementation platforms, as well as its benefits and its limitations. The second part of this article provides an idea of the SOEKS-DDNA-related research endeavors currently carried out by us and the last part is a sneak peek into our planned future work.

Cognitive systems are often defined as the set of know-hows that uses language processing and machine learning to enable people and machines to interact more naturally to enhance human expertise an...

Ontology processing is arguably a time-consuming process with high associated computational costs. Query actions constitute a crucial part of the reasoning process and are a primary source of time consumption. Reflexive ontologies (ROs) is a novel approach intended to reduce time consumption problems while providing a fast reaction from ontology-based applications. In this article we present the implementation of a knowledge-based clinical decision support system (CDSS) for the diagnosis of Alzheimer's disease, which was the benchmark used to evaluate the impact of RO in the overall performance of the system. The implementation details and the definition of the implementation methodology are exposed in this article, along with the results of the evaluation. Some novel techniques that aim to optimize the performance of ROs are also presented with highlights of the test application introduced in our previous work.

Autonomous market environments have been proposed in the literature as the future of electronic markets. The ability to delegate complex negotiation processes and obtain similar or better results than their human counterparts has generated a great interest in agent-based markets. More recently, such a paradigm has been applied in the field of knowledge management and, more specifically, to knowledge sharing and exchange; however, most of the knowledge market proposals in the literature fail to give details on a key component of their models: knowledge quality. This article presents a new proposal for an agent-based market environment that aims at filling the previously mentioned gap in research. The main contribution of our research is the integration of formal mechanisms for knowledge quality and quantity measurement and the use of these values to set a price for knowledge and select the most suitable agent for negotiation.

This article presents our continuing efforts to develop a model of the Internet-based mortgage market in Poland. It sums up modeling research done over the last 4 years, also showing the stages and the process of building and verifying the proposed model consisting of three submodels. The submodels are described for three market situations; that is, a stable market, financial crisis, and economic boom. The proposed model variables are analyzed statistically and the submodels are verified. The article presents the most current stage of model development and concludes with suggestions for further research in this area leading to the ultimate application of the proposed model in a real-life prognosis process.

New approaches are needed that could move us toward developing effective systems for problem solving and decision making, systems that can deal with complex and ill-structured situations, systems t...

This Special Edition follows Volume I (Current Research Advances and Implementations in Smart Knowledge-Based Systems: Part I) and contains carefully selected and reviewed papers that expand signif...

Set of experience knowledge structure (SOEKS) and decisional DNA (DDNA), as a knowledge representation, provide features such as learning from experience, dealing with noisy and incomplete data, making precise decisions, and supporting predictions. In this work, we investigate how the combination of DDNA and SOEKS with feature selection learning algorithm RELIEF-F can improve the quality of predictions. The proposed approach is general and extensible in terms of both designing enhanced learning algorithms and application to other domains.

In this article, we present a concept and case study of a novel approach that generates a television (TV) user's profile utilizing principles of fuzzy logic. A user profile refers to the user's basic information, such as gender, age, and profession. The generated profile has the potential to significantly improve Digital TV (DTV), making the service smarter and more user friendly. We apply the proposed approach to our previous work that introduced decisional DNA TV, which enables TV broadcasters to suggest program choices based upon the user's past viewing habits. Decisional DNA is a domain-independent, flexible, and standard experiential knowledge repository solution that allows for knowledge to be acquired, reused, evolved, and shared in an easy and portable way. The presented conceptual approach demonstrates how fuzzy logic methods can be deployed within DNA TV through an experimental implementation that generates a user profile by capturing viewing habits.

Business process modelling and simulation can be a way to improve efficiency in any formal business process support system. In this paper we discuss the underlying concepts and possible approaches that combines formal knowledge representation through past experiences stored in Set of Experience Knowledge structure with one of the popular simulation platforms iGrafx 2011.

Knowledge sharing practices have evolved from simple document archiving and retrieval, into more complex service-based environments supported by advances in Information Technology. As a result of this trend, market-based mechanisms have been proposed with the objective of fostering knowledge sharing, by setting reward mechanisms and motivate employees to share their know-how at a deeper level. Many of the reward schemes presented in literature consider quality as an attribute that gives value to knowledge, but do not provide details on how to measure it. This paper presents an agent-based market environment that measures quality and quantity in a semi-automatic way and uses these elements to set a value for organizational knowledge. This approach is part of the e-Decisional Community, an integrated knowledge sharing platform that aims at providing Knowledge as a Service (KaaS).

The paper introduces some challenges of the fast growing mortgage market in Poland, being also the next step of research presented in previous publications, showing the complete process of building and verifying three submodels for the internet mortgage market. The sub-models are described for three market situations, i.e. stable market, crisis, and boom, then the variables within the models are analyzed statistically (e.g. correlation level) and finally the models are verified. The paper presents the process of verification of submodel nr 3 (fast growing market) as an example of the verification process that was applied. The paper concludes with directions for further research in this area leading to the ultimate application of the proposed model in a real life prognosis process.

This plenary presentation covers a short history of experience based knowledge structure and representation, its development, implementations, and recent research directions and efforts leading to the idea of smart eResearch tools enhancing capture, storage, usage, and sharing of energy related laboratory research

The constant growth of information on the wide spread of the web is causing many difficulties in finding and extracting useful knowledge on the web. Set of Experience Knowledge Structure or Decisional DNA as knowledge representation provides features such as learning from experience, dealing with noisy and incomplete data, making precise decision and predicting. It can be a core component of a new structure with a feature selection model. This new structure is able to extract knowledge in DDNA structure by many different approaches such as web crawling and reading CVS files. Afterwards, the feature selection model is used to rank elements of set of experience structure for predicting purposes. This paper shows the application of feature selection models to enhance the Prognosis Macro process of Decisional DNA based web crawler for prediction.

This paper presents integration of the concept of Decisional DNA Digital TV with Smart TV. The integration provides the Digital TV viewer with smart assistance that helps to watch TV shows according to the viewer’s habit discovered through past viewing experience. Decisional DNA is a domainindependent, flexible, and standard experiential knowledge representation structure that allows its domains to acquire, reuse, evolve, and share knowledge in an easy and standard way. The presented approach demonstrates how the Decisional DNA-based systems can be integrated with Digital TV technique, and how it captures and reuses the TV viewer’s watching experience. Illustrative test of the suggested approach is presented in the paper with a set of experiments.

In recent years knowledge has been considered a critical organizational asset. As any other asset, knowledge provides value to organizations only when it conforms to a set of specifications and standards; in other words, when it is of good quality. Several proposals have addressed the issue of quality of knowledge, but there is not a widely accepted way of measuring such a concept. This article introduces a new approach to measure explicit knowledge in a semi-automatic way using software agents. The ideas described in this article are part of the e-Decisional Community concept, an agent-based platform for sharing experiential knowledge. The results of this research process show that it is possible to obtain a percentage of knowledge that represents an approximate measure of an individual's knowledge.

Organizations plan their projects and activities based on the availability of their assets, which often range from manufactured elements to computer services and infrastructure. In today's economy, a new asset comes into play: knowledge. Knowledge has become the most valuable resource for many organizations, and its proper use often determines the survival of enterprises in a competitive environment. However, determining how much knowledge is accessible is not as simple as counting how many units of a product are in inventory. Measuring knowledge quantity has been the focus of active research recently. This article presents an approach for knowledge quantification that offers a novel way of estimating the depth of an agent's knowledge in an automated way. The knowledge quantity measures described in this article are used in the e-decisional community, an integrated knowledge sharing platform that aims at the creation of markets where knowledge is provided as a service.

This article presents and discusses comprehensive development stages of an integrated, general model of the evolution of support organization enhancing information technology (IT) systems of a corporation. Both advantages and disadvantages of the proposed model are analyzed and future research directions communicated. This article signals one of the current efforts toward addressing the challenges of a very dynamic transformation of IT support in a real-life environment.

Knowledge and experience engineering techniques are becoming increasingly useful and popular components of hybrid integrated systems used to solve complex real-life problems in different disciplines. These techniques offer features such as learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this article, we present a number of different applications of a multidomain knowledge representation structure called decisional DNA that can be implemented and shared for the exploitation of embedded knowledge within different technologies.

How many formal, routine, automatic, and semi-automatic decisions are made each day? How many such decisional experiences are stored? What do we do with those that are stored and remembered? Are th...

It is believed that smart artificial systems, the development of which is currently researched very extensively by knowledge engineering research teams around the world, have enormous potential to ...

In this article we present an experience-based clinical decision support system (CDSS) for the diagnosis of Alzheimer's disease, which enables the discovery of new knowledge in the system and the generation of new rules that drive reasoning. In order to evolve an initial set of production rules given by medical experts we make use of the Set of Experience Knowledge Structure (SOEKS). An illustrative case of our system is also presented.

The use of engineering standards in virtual engineering and their potential as models for the specification of a given domain's ontology are arguably unexplored. The importance of domain modeling in virtual engineering deals directly with the potential benefits that the semantic technologies may bring, allowing to discover implicit knowledge that can be beneficial for engineers. This work presents a state-of-the-art review of the technologies used in our approach, a successful case study where our methodology was applied, and the description and results of an experiment designed to provide a quantitative validation of our methodology.

With very fast expansion of the Internet, several problems and challenges are created by the persistent growth of the amount of information in Web content. These challenges are related mainly to the difficulty of extracting potentially useful information and knowledge from Internet pages. To support Web content knowledge capture we propose a concept that integrates a novel decisional DNA knowledge structure with the traditional Web crawler technologies. Decisional DNA as a knowledge representation platform can be used to deal with noisy and incomplete data and can help to learn from experience and make precise decisions and predictions in vague and fuzzy environments. We illustrate our proposed concept with a set of experiments to prove its initial efficiency and effectiveness.

In this article, we explore an approach that integrates decisional DNA, a domain-independent, flexible, and standard knowledge repository, with digital TV in order to capture, reuse, and share viewers’ TV watching experience and preferences. Key issues in applying this approach include capturing of experience, storage and management of experience, and retrieval of experience from experience repository. We demonstrate our approach in a set of initial experiments, in which viewers’ movie watching experiences are captured and reused to support smart digital TV services.

Reinventing the wheel and making mistakes that have already been made by others are the obvious dangers when we embark on a new project. We can avoid these traps by smart use of past experience, which includes past mistakes, related to the area of our activities. The above creates two obvious challenges: first, experience capture and storage; and second, the ability to formalize it and use as a decision support tool. This article tries to address the two challenges by proposing an approach to smart experience management and illustrating this approach with a case involving the information technology (IT) workflow platform.

This special edition follows Volume I (Smart Modeling Support for Managing Complexities and Dynamics of Knowledge Based Systems – Part 1) and contains carefully selected and reviewed articles that ...

Today, a significant number of the approaches that enhance the performance of real-life systems are based on knowledge processing intelligent multi-agent methodologies. This is a new paradigm that ...

Organizations develop a growth strategy of their informational and decisional platform based on certain key drivers. This determines business plans and their future informational and analytical capacity. However, each solution has a different deployment depending upon the selected architecture and the management model. In the specific case of business intelligence (BI) infrastructures, this should be decided according to the speed of the decision-making processes, which are usually executed in real time. Therefore, they determine the flexibility rate at which the business can grow. Businesses grow but the key drivers can remain the same. This article analyzes the elements required for an optimal deployment of smart decision architectures.

Knowledge plays a major role in enterprises, given its importance as a significant organizational asset. In order to solve problems and support complex decision-making processes, knowledge and experience have to be transmitted between diverse individuals and organizations. Thus, knowledge-sharing can be considered a fundamental element in any knowledge-oriented process, because it fosters collaboration, and facilitates experiential knowledge discovery, distribution, and use. We present the E-Decisional Community, a proposal for an integrated knowledge-sharing platform where several entities are able to share experiential knowledge. Its main concern is to promote experiential knowledge evolution and sharing through generations of decision makers, aiming at the creation of a marketplace where knowledge is provided as a service.

Virtual organizations promote dynamic interaction between individuals, groups, and organizations, who share their capabilities and resources to pursue a common goal and maximize their benefits. Among these resources, knowledge is a critical one that requires special attention in order to support problem-solving activities and decision-making processes and provide strategic advantage. This article presents an initial proposal for the creation of dynamic knowledge-based virtual organizations, as a way to share knowledge in order to support problem-solving activities. This approach is based on behavioral elements, identified by other researchers, that affect group interactions; these items are represented inside the e-decisional community in a manner that allows software agents to interact similarly to their human counterparts. An initial model and a functional prototype have been developed and used to obtain a set of preliminary results, which show human-like behavior in our test system.

A desire to come to grips with modern knowledge-based systems has arisen both commercially and academically in recent years primarily as a consequence of the technological revolution. Our ability a...

This Special Edition of Cybernetics and Systems follows Volume I (Applied Intelligence and Knowledge Based Systems: Approaches and Case Studies – PART 1) and contains carefully selected and peer-re...

In this article, we explore an approach that integrates Decisional DNA, a domain-independent, flexible, and standard knowledge representation structure, with robots in order to test the usability and suitability of this novel knowledge representation structure. Core issues in using this Decisional DNA–based method include capturing of knowledge, storage and indexing of knowledge, organization of the knowledge base memory, and retrieval of knowledge from memory according to current problems. We demonstrate our approach in a set of experiments in which the robots capture knowledge from their tasks and are able to reuse such knowledge in subsequent tests.

This article is a brief communication of a concept for building an experience-related knowledge database on top of a workflow system. The presented solution has two important characteristics. First, it is based on minimal effort approach and thus is truly cost-efficient. The second advantage of the proposed approach is low risk related to introduction of new workflow features. The novel ideas of the approach are illustrated using well-known, open-source software and an ordinary relational database. The presented solution is universal and can be easily tailored to be possible to use with other real-life cases including commercially boxed software.

Knowledge-based systems, knowledge technologies, and knowledge management are the areas that have been gaining significant attention and importance in the recent process of the knowledge-based economy creation. The above relates not only to the national economy as a whole (macroeconomic level) but also to particular economy sectors and individual organizations. This article focuses on the role of knowledge management tools, technologies, and approaches in the process of dynamic development of Polish nongovernmental organization (NGO) sector. First, the current state of this role is examined and then a systemic model solution is proposed with an example of implementation supporting the enhancement of NGO operations and performance. The article concludes with the outline of further research in this area.

In this article, we introduce the necessary elements that must be integrated in order to achieve a decisional technology that is trustworthy. Thus, we refer to such technology as decisional trust. For us, decisional trust can be achieved through the use of elements such as the decisional DNA, reflexive ontologies, and security models; and therefore, we present in this article a framework that was used for the implementation of a decisional trust system.

When managers make decisions, they use previous, similar, or equal experiences to help themselves in a new decision-making situation. Thus, keeping record of previous decision events appears to be of the utmost importance as part of the decision making process. For us, every formal decision event has to be collected and stored as experienced knowledge, and any technology able to do this will allow us to improve the decision-making process by reducing decision time, as well as by avoiding duplication in the process. However, one of the most complicated issues about knowledge is its representation. Developing a knowledge structure that stores and administers experience from the day-to-day decision processes would improve decision-making quality and efficiency. We are proposing such a knowledge structure and have named it set of experience knowledge structure. A set of experience knowledge structure (SOEKS) is a combination of organized information obtained from a formal decision event. Fully applied, the set of experience knowledge structure would advance the notion of administering knowledge in the current decision-making environment.

Intelligent systems research, development, and application shows the potential of becoming the primary area of interest among academics and practitioners in our knowledge-based society of the 21st ...

It has been established that the aims of intelligent and knowledge-based system research should focus on: reducing the gap between theory and practice, and thus providing well-established knowledge...

In this article we introduce the necessary elements needed to integrate a decisional technology that offers a level of trust that enables them to be used for the implementation of decisional trust systems. Thus, we refer to this approach as decisional trust, which can be achieved through the use of elements such as decisional DNA, reflexive ontologies, and security technologies. Decisional trust operates on three fronts: (1) the construction of decisional DNA as a knowledge structure capable of collecting an organization's decisional fingerprints; (2) the construction of Reflexive Ontologies as descriptions of concepts and relations with a set of selfcontained queries in a domain of study; and (3) the addition of security technologies. Our approach extends the use of Decisional DNA and Reflexive Ontologies with the aim of offering trustable decisions, and introduces elements for the exploitation of embedded, trustable, decisional knowledge which, added to security elements, can lead to trustable technologies. Fully developed, it would advance the notion of administering trustable knowledge in the current decision making environment.

Enterprises have to perform today in an environment characterized mainly by uncertainties, rapid change, and imprecision. Computer science, operations research, engineering, and management science ...

Information is seen as one of the main resources that systems analysts try to use in an optimal way. In this short article, we show how this resource can be used in integration issues. We introduce the problem of information-based integration, propose a solution, and discuss briefly future trends in this area.

The advance and application of smart techniques and technologies for information and knowledge engineering adds significantly to the emerging area of intelligent systems development. Such systems s...

This special edition follows the previous issue (Information and Knowledge Engineering for Intelligent Systems—Part I) and contains carefully selected and reviewed papers that expand significantly ...

In this article, we introduce the concept of reflexive ontologies. A reflexive ontology is a description of the concepts and relations in a domain with self-contained queries. This approach presents several advantages; (1) the speeding of the query process; (2) the addition of extra knowledge about the domain extending it with queries and answers; and (3), the self-containment of the knowledge structure. We present a framework that can be used to extend any existing ontology with the reflexivity approach. Additionally, as case study, we test the architecture with a previously presented knowledge structure called Set of Experience Knowledge Structure (SOEKS).

Set of Experience Knowledge Structure is a tool that can be a source and target of multiple technologies. It comprises variables, functions, constraints, and rules associated in a DNA shape allowing the construction of Decisional DNA. However, when having various dissimilar Sets of Experience as output of the same formal decision event, a negotiation and unification process has to be performed in order to generate a holistic Set of Experience. The purpose of this article is to show such processes and produce one holistic Set of Experience, making it an even more useful technology within many different intelligent systems and platforms.

Set of Experience Knowledge Structure (SOE) has been shown as a tool able to collect and manage explicit knowledge of formal decision events. This structure, after being homogenized and mixed, offers a set of possible solutions that, probably, could be improved. The purpose of this article is to show a search process for improved optimal solutions by implementing Evolutionary Algorithms—EA (Genetic Algorithms—GA). Afterward, according to the user's priorities, a unique optimal solution is chosen. Subsequently, such holistic improved SOE is stored as an experienced decision, feeding a knowledge repository of Decisional DNA that would be a useful technology within many different intelligent systems and platforms, including the Knowledge Supply Chain System (KSCS).

In this article, we present a Java class and an ontology system implementation for the exploitation of embedded experiential knowledge that can be used in several domains. We support this approach on three concepts: Set of Experience Knowledge Structure (SOEKS), a tool able to collect and manage explicit decisional knowledge; Decisional DNA, a structure for decisional knowledge akin to human DNA; and a group of ontologies for ubiquitous applications called SOUPA (Standard Ontology for Ubiquitous and Pervasive Applications). The SOUPA is extended with the Set of Experience Knowledge Structure (SOEKS), enhancing the decisional experience used to assemble Decisional DNA with ontology characteristics for ubiquitous and pervasive applications. Additionally, we propose a SOEKS Java class created for the support and easy implementation of applications using the extended SOUPA which will allows the construction of a Decisional DNA repository useful within many different intelligent systems and platforms.

In the ever-evolving environment of intelligent systems and knowledge management, researchers and professionals need access to the most current information about the concepts, issues, trends, and t...

This special edition follows Volume I (Knowledge Management and Ontologies-PART I) and contains carefully selected and reviewed papers that expand significantly on those topics of KES'2006 conferen...

Some of the most complicated issues about knowledge are its acquisition and its conversion into explicit knowledge. Therefore, among all knowledge forms, storing formal decision events in a knowledge-explicit way is considered an important development. Set of an experience knowledge structure is a vehicle able to acquire explicit knowledge of formal decision events. The purpose of this article is to show an effective form of transformation of a set of experience into a shareable and understandable shape able to travel among different systems. A transportable set of experience could be applied in many technologies, and in consequence, it can advance the notion of administering knowledge in the current decision-making environment.

Collecting formal decision events in a knowledge-explicit way becomes an important development in terms of knowledge administration. A Set of Experience Knowledge Structure can assist in accomplishing this purpose. However, collecting knowledge comes together with mechanisms of classifying, comparing, and selecting elements among the collected universe, i.e., the universe of formal decision events. Thus, similarity metrics play an important role in knowledge administration. The purpose of this article is to develop heterogeneous similarity metrics for set of experience knowledge structure, and within it, similarity metrics for its components: variables, functions, constraints, and rules. A comparable and classifiable set of experience would make explicit knowledge of formal decision events useful elements in knowledge administration, as well as in multiple technologies.

Design, implementation, and management of real-life systems functioning in information reach environments of our “information society” and require greater understanding about the role of data, info...

Guest Editor & CBS Advisory BoardKnowledge Management (KM) today has to deal with enormous challenges related to the broad spectrum of issues covering information science, artificial intelligence, ...

Systems become increasingly complex. Their decomposition into smaller units is the usual way to overcome the problem of complexity. This has historically led to the development of atomized structures consisting of a limited number of autonomous subsystems that decide about their own information input and output requirements, i.e., they can be characterized by what is called an information closure. Autonomous subsystems still can be interrelated and embedded in larger systems, as autonomy and independence are not equivalent concepts. These ideas are gaining a very strong interest in both academia and industry, and the atomized approach to information flow modeling and evaluation is an idea whose time has certainly come. This presentation discusses some modeling and evaluation issues, and challenges existing in the exciting area of knowledge capture for information flow-management support for autonomous subsystems.

Engineering, operations research, and management science use scientific and engineering processes to design, plan, and schedule increasingly more complex systems functioning in increasingly more complex environments to enhance their performance. One can argue that the systems have grown in complexity over the years mainly due to increased striving for resource optimization combined with a greater degree of uncertainty in the system's environment. Information is seen as one of the main resources that systems analysts try to use in an optimal way. We show how this resource can be used in integration issues. We introduce the problem of information-based integration, propose a solution, and illustrate the proposed solution with an example.

This Special Edition follows Volume 35 Nos. 5–6, (Real and Virtual Environments in Environmental Engineering: Approaches, Models, Technologies and Critical Issues) and contains carefully selected a...

This paper attempts to give a brief overview of the concept of design for environment (DfE) as part of the concurrent engineering philosophy. DfE includes designing for recyclability, reuseability, durability, and maintainability. DfE also promotes the reduction of energy consumption and product emissions as a means of environmental consciousness. As part of this research into DfE, five Hunter Valley–based Australian businesses were used as case studies in an attempt to discover what role DfE plays in local Australian industry.

Descriptive, or soft, modeling is always present at the initial stage of any modeling approaches. An introduction to a model development of Internet retailing is presented. It assumes that its significance expressed by the structure of retail and by dynamics of participation of Internet transactions in business-to-customer and customer-to-customer electronic transactions is conditioned by functionality influenced by two groups of factors: a group of technological factors and a group of human factors. These two groups are opposite in their character and rival each other in the presence of the third group: the group of market factors. The factors contained in technological and human groups are influenced by the feedback from the functionality of real-life Internet transaction services. Market factors influence functionality, yet the feedback relation takes place indirectly through the “significance of Internet retail trade” expressed by market share and sale structure.

The article signals an important problem of Information Technology (IT) projects evaluation. The authors suggest an approach that combines qualitative and quantitative techniques, focusing on representing a number of elements of project description using fundamentals of fuzzy logic. The applied techniques include fuzzification of project conditions using the membership function, inference using the rule description, and defuzzification of output data.

Concurrent engineering (CE) is a strategy that attempts to process as many product development tasks in parallel and incorporate relevant life-cycle attributes as early as possible in the design phase in an effort to reduce the duration of design projects, save development costs, and provide better quality products. The CE environment is characterized by a high degree of distributed cognitive processing in the form of product development team structures. The distribution of appropriate knowledge to members of these teams and other participants in the design process for the purpose of supporting management and planning decisions is a considerably complex problem. New approaches and tools based on artificial intelligence methodologies are needed to deal with this level of complexity in coordinating knowledge resources. This paper reviews a number of potential candidates for an intelligent software architecture that can represent this type of problem as well as support the knowledge handling necessary to solve such problems. The selection of an appropriate architecture will support the development of an intelligent information system that is able to mimic human cognitive processes as the basic tool for providing decision-making support for planning and controlling a CE design process.

Engineering, operations research, and management science use scientific and engineering processes to design, plan, and schedule increasingly more complex industrial systems in order to enhance performance. One can argue that the systems have grown in complexity over the years mainly due to increased striving for resource optimization combined with a greater degree of uncertainty in the system's environment. Information is seen as one of the main resources that managers try to use in an optimal way. Managing complex systems requires a greater understanding and knowledge about the role of information in systems operation. Today, a growing complexity of information flow is a characteristic of enterprises which concern products to be manufactured, services to be offered, processes, and company structures. Complex systems also operate in changing environments surrounded by numerous uncertainties and disturbances. Difficulties arise from unexpected tasks and events and from a multitude of possible failures and other interactions during the attempt to control various activities in dynamic environments. Therefore, management of information is one of the most important aspects to be considered in intelligent management systems, which are expected to solve unforeseen problems, even on the basis of incomplete and imprecise information. The paper discusses the importance of information in operation management as well as new challenges in information modeling, visualization, and communication in an information society.

The basis for an intelligent decision support system for design process planning within a concurrent engineering (CE) environment is the efficient utilization and coordination of planning knowledge that resides within computerized workgroups of multidisciplinary experts. A systems approach may be taken to derive, represent, and utilize the many models of reasoning that might support a human-centric view of planning in a distributed environment. The blackboard database (BB) provides a suitable framework for utilizing these models in a structured manner by representing the planning problem as a loosely coupled hierarchy of partial problems along with the knowledge needed to progressively solve different parts of this problem. This article discusses the development of such a BB system, which is intended to provide the ability to experiment with various control and domain strategies in order to yield insight into more developed and intelligent methods to assist humans in planning the CE design process.

Agile manufacturing can be defined as the capability of surviving in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. In such an environment, most, if not all, decisions have to be made precisely and in an intelligent manner. Intelligent decisions can only be made if we are aware of all aspects of our business and operation, which is not easily achieved. Simulation modelling can help in developing a proper level of this awareness needed in agile manufacturing environment.

Resource allocation and scheduling are the key areas in the operation of complex industrial systems. One can argue that the systems have grown in complexity over the years mainly due to the increased striving for performance enhancing combined with a greater degree of uncertainty and imprecision in system's external and internal environments. This complexity, always present in real life systems, makes the application of quantitative scheduling and allocation tools as problem solvers questionable in many instances. In this paper simulation technique is proposed as an analysis tool that proved to be an adequate, effective and economically efficient problem solver in the case of scheduling and resource allocation and utilization for a complex manufacturing system. The implementation described in the paper was developed using SLAMSYSTEM modelling environment.

Concurrent Engineering CE is a strategy that attempts to process as many product development tasks in parallel, and incorporate relevant life-cycle attributes as early as possible in the design phase in an effort to reduce the duration of design projects, save development costs, and provide better quality products. The implementation of such a strategy considerably increases the complexity of the design process and makes it more difficult to plan and manage. New approaches and tools based on artificial intelligence methodologies are needed to deal with the above complexity. This paper discusses an architecture that uses a particular type of database that is able to mimic human cognitive processes called a blackboard database , as the basic tool for providing decision-making support for planning and controlling a CE design process. It is expected that a blackboard-based design planning model developed in the study will be able to support companies in a major technological change from sequential to concurrent engineering.

Condition monitoring, the process of data collection for the evaluation of machinery performance and reliability, is an essential part of today's on-line predictive maintenance for complex manufacturing systems. Together with the increasing complexity of machinery systems, the number of assets that need to be diagnosed is also rapidly increasing. This is why the management of condition monitoring became a complex cooperation and resource allocation problem. This paper describes an implementation of computer simulation as a modeling and decision support tool for the management of a condition-monitoring service group. The implementation was developed using the SLAMSYSTEM modeling environment. It provides an easily adaptable management-style program that may be applied to a company with a monitoring system that varies in versatility, expertise, and availability.

The problem of formal modeling of an information flow in manufacturing agents subsystems consisting of people, machines, robots, and so on, matches very frequent real-life situations in which the following should be answered: How to structure an exchange of information between the elements of an agent? What is better, complete information but heavily delayed, or incomplete information less delayed? The formal quantitative model can be used to generate some examples of knowledge useful in answering these types of questions. Quantitative models of an information flow evaluation, however, are often too complex to serve as useful tools in the knowledge retrieval process. In this paper, the application of qualitative simulation in the process of knowledge acquisition for autonomous agents is investigated and illustrated with examples. The captured knowledge is codified as IF...AND...THEN production rules and used in dependency networks supporting decisions concerning the structuring of information flow.

In an organizational context autonomous agents consist of groups of people, machines, robots, and/or guided vehicles tied by the flow of information between an agent and its external environment as well as within an agent. Mathematical modeling is used to evaluate such an information flow. The evaluation of an information flow is performed for different types of external and internal environments. Two major cases are taken into account, i.e., static and dynamic processes describing the external environment. Only actions that are described by real numbers and utility functions that are twice differentiable are considered. The results of the model-based evaluation of an information flow in different decision situations are formulated as IF...AND...THEN rules that provide some useful knowledge about autonomous agents functioning. To support the development of a bridge between the distributed systems and artificial intelligence, an approach is suggested that combines knowledge expressed by traditional IF...THEN rules with machine learning technique based on the training of a neural network. A three-layer neural configuration is used. The concepts included are illustrated with examples providing interpretation and relation to real situations.>

An attempt is made to show how mathematical tools can be used in the analysis of an information flow in autonomous group functioning. The analysis is descriptive in nature and provides useful IF-THEN rules that can be used to support the process of structuring an information flow between a group and its external environment as well as information exchange within a group.>