Accurate segmentation of meningiomas in magnetic resonance imaging scans is essential for clinical planning, yet remains challenging due to their irregular shapes and subtle boundaries. In this study, we refine skip connections in convolutional encoder–decoder networks (widely known through the U-Net architecture) by selectively integrating shifted window transformer blocks. Unlike prior transformer-based architectures, which primarily enhance encoder or decoder stages, our approach targets shallow skip connections to improve the fusion of local detail and global context. An ablation study on the BraTS Meningioma 2023 dataset demonstrates that applying transformer blocks to the first two skip levels yields an optimal balance between accuracy and efficiency. The proposed model achieves a Dice similarity coefficient of 0.9119, outperforming conventional encoder–decoder baselines such as U-Net, Attention U-Net, and a widened U-Net variant, while delivering more precise boundary delineation with competitive recall.

• Trust in local government reduces AI risk perceptions in Australia, Spain, and the US. • Persuasion increases trust in local government AI adoption, especially in the US. • Responsible AI expectations differ significantly across the three countries. • Risk perception drives demand for responsible AI in local governance. • Responsible AI is a public demand shaped by local trust dynamics.

The integration of IoT devices with smart home energy management systems (SHEMS) presents a significant advancement in energy demand response (DR) optimization. However, due to the rapid proliferation of home appliances with varying operating characteristics as well as the variable comfort level demands of users, making effective DR decisions becomes more challenging. In this paper, we propose a hierarchical Stackelberg game-based incentive mechanism with multi-agent deep reinforcement learning (MADRL) to optimize DR in IoT-based SHEMS. We formulate the hierarchical decision-making problem as a Markov decision process (MDP) and then adopt the multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve it by finding an equilibrium solution. Through extensive simulations, we demonstrate that our proposed DR optimization approach can effectively reduce overall energy consumption and peak load by 30.41% and 28.57% from the benchmark approaches, respectively. In addition, the proposed approach maintains user comfort and increases system utility by 13.11% and 15.74% than the benchmark schemes, respectively, resulting in improved energy efficiency.

Climate change represents a critical threat to global food security, affecting agricultural production and exacerbating the food crisis projected by the FAO for 2050. Soil recovery and the adoption of sustainable agricultural practices, such as organic farming, are essential to address this challenge. Smart organic farming improves soil quality, crop productivity, and water retention capacity. In this context, vermiculture, which utilizes Eisenia Foetida (red worms), plays a fundamental role. This article highlights how humus production through vermiculture has been significantly optimized through an Edge AIoT platform that integrates an agricultural recommendation system based on bio-inspired algorithms, an LSTM network for predicting humus and worm populations, and a control system to regulate variables such as temperature, humidity, and pH. The results show an increase in humus production from 37.58% to 87.88% and in the worm population from 35.5% to 83%. Vermicompost, obtained through the non-thermophilic biodegradation of organic waste by worms, acts as a crucial biofertilizer that sustainably increases crop yields and helps farmers adapt to environmental stresses, contributing to the Sustainable Development Goals (SDGs). Finally, seven experiments were conducted in which the Edge AIoT-based agricultural recommendation platform optimized the vermicomposting process, improving efficiency and productivity in humus production. This technological approach not only mitigates the impact of climate change but also supports the recovery of degraded soils and promotes sustainable agricultural practices essential for ensuring future food security.

The integration of multi-agent deep reinforcement learning (MADRL) in adaptive and intelligent home energy management systems (AI-HEMS) enhances real-time energy management by enabling intelligent decision-making among multiple agents to optimize various problems. This approach allows smart homes to dynamically respond to changes in energy demand, pricing, and user preferences. The integration of Internet of Things (IoT) devices with AI-HEMS has been promoted to efficiently manage energy resources and maintain occupants’ comfort, where IoT devices collect data on energy consumption, usage patterns, and environmental conditions. However, ensuring trade-offs between conflicting optimization objectives, such as reducing energy consumption and electricity prices, and maximizing users’ comfort levels is challenging. In this paper, we propose a MADRL-based multi-objective demand response (MODR) optimization framework to efficiently manage and control the energy consumption of smart homes. The proposed approach aims to simultaneously reduce energy costs and maximize users’ comfort, improving the overall reliability of energy systems. We first formulate the MODR optimization problem as MDP and then adopt the MADRL algorithm to solve it. The simulation results demonstrate that our proposed DR optimization approach can effectively balance the trade-off between energy cost and user comfort levels, resulting in improved energy efficiency compared to benchmark approaches.

Demand response (DR) plays a significant role in modern energy management systems, particularly within the context of adaptive and intelligent building energy management systems (AI-BEMS). In the AI-BEMS context, DR focuses on dynamically adjusting energy usage in response to external factors, such as electricity prices, grid conditions, and environmental considerations. This survey paper explores the evolving landscape of DR within the framework of AI-BEMS, focusing on the integration of advanced technologies and adaptive strategies to optimize energy consumption and enhance grid reliability. This article reviews state-of-the-art research addressing the key concepts associated with integrating DR and AI-BEMS, including an overview of DR techniques in AI-BEMS, and an artificial intelligence and machine learning applications for the development of adaptive control strategies and DR optimization. Then, insights are provided on the future directions and the challenges in this field regarding the implementation of DR within AI-BEMS.

Abstract Human interaction has changed considerably with the emergence of the Internet. Today, a large percentage of daily communication takes place on instant messaging applications and social networks. In fact, there has been a considerable increase in the use of social networks because new social networks are being created for specific purposes, such as the search for employment or tourism. When the amount of content on a social network is large, it is necessary to help users find content of their interest. In this regard, artificial intelligence techniques can greatly facilitate the task of searching for relevant content. This paper presents a recommender system for a business and employment oriented social network, on which users are recommended job offers and other user profiles to follow. The presented system is based on virtual organizations of agents, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network; its recommendations regarding job offers and user profiles have had a high acceptance rate.

The recent viral outbreaks have had a significant impact on interpersonal relationships, particularly in enclosed spaces. Detecting and preventing the transmission of diseases such as COVID-19 has become a top priority. These diseases are typically identifiable through the symptoms they cause in humans. However, the collection of personal and health data for use in Artificial Intelligence models can give rise to ethical, security, and privacy issues. Therefore, it is necessary to have architectures that maintain the principles of Trustworthy Artificial Intelligence by design. This work proposes a decentralized architecture based on Federated Learning for symptomatic disease detection using the edge computing paradigm, storing the information in the device that collected it, and the foundations of Trustworthy Artificial Intelligence. The architecture is designed to be robust, secure, transparent, and responsible while maintaining data privacy. The proposed approach can be used with medical information capture systems with different user profiles.

The widespread use of IoT devices and advances in communication technology have led to rapid development in building management systems. Considered one of the heaviest loads in commercial buildings, heating, ventilation, and air conditioning (HVAC) has been the focus of numerous studies. This paper proposes a novel approach to provide the optimal thermal sensor location for a large commercial building. The approach combines Computational Fluid Dynamics (CFD), network coverage, and clustering to establish a multi-step flow leading to the discovery of the optimal placements within the area of interest. The simulation results show that the combination of CFD and clustering can be very effective to identify potential candidates, which is then tuned using the coverage area of the network. Multiple scenarios were considered to simulate several environmental conditions, each of which leads to a different set of locations.

The number of applications of internet of things (IoT) devices in smart buildings keeps growing continuously, and with it, the computational tasks rendered by those devices. In smart buildings, IoT devices generate massive data traffic, and the number of devices and traffic volume increases exponentially. This issue is more sensitive in smart buildings as the management of their data is critical. Therefore, matching the task’s differential needs (e.g., energy, delay) with the network resources is paramount. In a device-to-device (D2D) aided edge computing (EC) architecture, tasks can be offloaded to the resource-rich IoT device or edge node to improve offloading efficiency and minimize energy consumption and delay. Exploiting these benefits, in this paper, we propose enhanced resource allocation and energy management in smart buildings enabled by software-defined networking and EC, as well as D2D aided end-to-end communications (REED). REED aims to minimize energy consumption and delay in a smart building by jointly optimizing resource allocation and offloading decisions. To find the near-optimal solution, we use the model-free deep reinforcement learning, i.e., deep deterministic policy gradient algorithm, because the formulated problem is a mixed-integer nonlinear optimization problem with a large dimensional continuous state and action spaces in a dynamic environment. Simulation results show that the intended REED model can perform better in terms of energy consumption and delay than the other benchmark approaches.

Modern software development paradigms, including Service-Oriented Architecture (SOA), tend to make use of available services e.g., web service Application Programming Interfaces (APIs) to generate new software. Thus, for the further advancement of SOA, the development of accurate automatic tasks, such as service discovery and composition, is necessary. Most of these automated tasks rely heavily on web service metadata annotation. The lack of machine-readable documentation and structured metadata reduces the accuracy and volume of automatic data annotation, negatively affecting the performance of automated SOA tasks. This study aims to propose automatic code completion for improving web service-based systems by identifying and capturing service usage collected from public repositories that share Open Source Software (OSS). To this end, a Data-Centric Service Code Generation (DCServCG) model is proposed to improve old-fashioned, general-purpose code generators that neglect essential service-based code characteristics e.g., sequence overlap and bias issues. DCServCG takes advantage of the data-centric concept, i.e., conditional text generation, to overcome the mentioned issues. We have evaluated the approach from the point of view of language modeling metrics. The obtained results indicate that the usage of the data-centric approach reduces perplexity by 1.125. Moreover, the DCServCG model uses de-noising and conditional text generation, which is trained on the transformer by distilling the knowledge, DistilGPT2 (82M parameters) trained faster and its perplexity is 0.363 lower than ServCG (124M parameters) without de-noising and conditional text generation, which lower perplexity value indicates better model generalization performance.

This article proposes an adaptable hybrid model for recommending effective investments in different scenarios. Currently, a wide variety of methodologies are used for company valuation, especially those that take into account financial statements. However, for private held companies, there is no method that would be capable of predicting, with full certainty, the future success of an investment. The Optimal Capital Investment Case-Base Reasoning (OCI-CBR) consists of a case-based reasoning system that uses a classification algorithm to prune the case base according to a projected increase in certain company attributes. Once the cases have been pruned and the case is fed with the most profitable investment opportunities, the case-based reasoning system recommends optimal investments to potential investors. The complete model is conceived as an intelligent hybrid model that optimizes the case base by employing different algorithms for data retrieval and reuse. The system makes recommendations based on the investor’s preferences and the investment decisions of other investors with similar profiles or interests.

A large percentage of the worldwide population is affected by chronic diseases, leading to a burden of the patient and the national healthcare systems. Recommendation systems are used for the personalization of healthcare due to their capacity of performing predictive analyses based on the patient’s clinical data. This systematic literature review presents four research questions to provide an overall state of the art of the use of recommendation systems applied to the healthcare of patients with chronic diseases. Disease management was identified as the main purpose of the systems proposed in the literature. However, few solutions provide support to physicians in the clinical decision-making. Ontologies and rule-based systems were the artificial intelligence techniques most used in the systems since they can easily implement clinical guidelines. Current challenges of these systems include the low adherence, data sparsity, heterogeneous data, and explainability, that affect the success of the recommendation system. The results also show that there are few systems that provide support to patients with multiple chronic conditions. The findings of this literature review should be considered in the development of future recommendation systems that aim to support the management of chronic diseases.

Drillships and semi-submersible platforms (SSPs) are used for oil and gas offshore exploration and production activities in seas deeper than 300 meters. These SSPs must be maintained in a predefined position at a predetermined location to complete their offshore tasks. They must have a way to generate forces and angular momentum to balance out external factors (wind, currents, and waves). Actually, the offshore support vessel (OSV) is assisted by dynamic positioning systems (DPSs) in all of its operations, including transit, survival, and station maintenance, and there are significant differences in propulsion systems. In this paper, we suggested a path planning robust adaptive fuzzy-free fault-tolerant control (RAFFFTC) for SSPs with actuator constraints to improve the robust performance and quality of the control system. Accordingly, the adaptive fuzzy controller with adaptive law has been created to change the membership function of the fuzzy controller, and the Lyapunov theory for system stability analysis has been used to illustrate the$H_{\infty} $performance of robust tracking, reduced errors in the path planning control of the SSP in the cases of thrusters and disturbance-free fault-tolerant. Finally, a simulation experiment with two scenarios compared its performance to the other controller, with results verifying the proposed controller’s effectiveness and demonstrating that it achieved the control quality required in the SSP control process.

Developing software based on services is one of the most emerging programming paradigms in software development. Service-based software development relies on the composition of services (i.e., pieces of code already built and deployed in the cloud) through orchestrated API calls. Black-box reuse can play a prominent role when using this programming paradigm, in the sense that identifying and reusing already existing/deployed services can save substantial development effort. According to the literature, identifying reusable assets (i.e., components, classes, or services) is more successful and efficient when the discovery process is domain-specific. To facilitate domain-specific service discovery, we propose a service classification approach that can categorize services to an application domain, given only the service description. To validate the accuracy of our classification approach, we have trained a machine-learning model on thousands of open-source services and tested it on 67 services developed within two companies employing service-based software development. The study results suggest that the classification algorithm can perform adequately in a test set that does not overlap with the training set; thus, being (with some confidence) transferable to other industrial cases. Additionally, we expand the body of knowledge on software categorization by highlighting sets of domains that consist ‘grey-zones’ in service classification.

Industry 4.0. has empowered the integration of real-time sensor readings and process knowledge for early identification of faults. In this paper, simulation was used to evaluate the process of classifying data streams as drifting towards defective behavior. Focusing on the challenges imposed by real-word, unlabeled and non-stationary data, the proposed method was assessed by conducting a case-study in the plastic extrusion domain. The knowledge gained from initial tests on generated synthetic data sets was used to isolate symptoms of a clogged screen pack from the concept drifts detected by the Adaptive Windowing algorithm. Experimental results for 40 days of running time of an extruder show that the proposed method helped in identifying a defective screen pack state upon the appearance of defects in the extrudate.

Abstract Random Projection is one of the most popular and successful dimensionality reduction algorithms for large volumes of data. However, given its stochastic nature, different initializations of the projection matrix can lead to very different levels of performance. This paper presents a guided random search algorithm to mitigate this problem. The proposed method uses a small number of training data samples to iteratively adjust a projection matrix, improving its performance on similarly distributed data. Experimental results show that projection matrices generated with the proposed method result in a better preservation of distances between data samples. Conveniently, this is achieved while preserving the database-friendliness of the projection matrix, as it remains sparse and comprised exclusively of integers after being tuned with our algorithm. Moreover, running the proposed algorithm on a consumer-grade CPU requires only a few seconds.

The aim of this Special Issue is to share the state-of-the-art research and developments on the emerging Industry 5.0 concepts, technologies, use cases and future applications. The outstanding benefits of Industry 5.0 in terms of cost and efficiency facilitate a reality sooner than expected. However, the benefits of Industry 5.0 must not come at a price-any negative social or economic impact must be prevented. To this end, it is beneficial and important that businesses can identify the ethical issues associated with these technologies and find solutions ahead of implementation. Ethically aligned designs and standards must be the backbone of the next Industrial Revolution. Hence, there is a desperate need for the further exploration of the role of Industry 5.0 in all verticals and for the exploitation of computational intelligence. Following a series of rigorous reviews, twelve papers, presenting original research, have been selected for publication in this Section.

[EN]With its strong coverage, low energy consumption, low cost and great connectivity, the Internet of Things technology has become the key technology in smart cities. However, faced with a large number of terminals, the rational allocation of limited resources, the topology and non-uniformity of smart buildings, the fusion of heterogeneous data become important trends in Internet of Things research. As a result, this paper proposes a novel technique for processing heterogeneous temperature data collected by an IoT network in a smart building and transforms them into homogeneous data that can be used as an input for monitoring and control algorithms in smart buildings, optimizing their performance. The proposed technique, called IoT slicing, combines complex networks and clusters in order to reduce algorithm input errors and improve the monitoring and control of a smart building. For validating the efficiency of the algorithm, it is proposed as a case study using the IoT slicing technique to improve the operation of an algorithm to self-correct outliers in data collected by IoT networks. The results of the case study confirm, irrefutably, the effectiveness of the proposed method.

In smart buildings there are many different types of IoT devices that collect measurements of the environment. These sensors can vary in their characteristics and can also influence the topology of the smart building. For this reason, IoT devices collect heterogeneous measurements. Using complex network and clustering techniques we have designed a new technique that allows to transform heterogeneous data into homogeneous data, this technique is called IoT slicing. This technique consists of creating a graph with the measurements of the IoT network, and virtualizing layers based on the clustering of the graph. To validate the efficiency of this new technique we present the results of a case study using a smart building temperature control algorithm.

Abstract Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.

Entropy models the added information associated to data uncertainty, proving that stochasticity is not purely random. This paper explores the potential improvement of machine learning methodologies through the incorporation of entropy analysis in the learning process. A multi-layer perceptron is applied to identify patterns in previous forecasting errors achieved by a machine learning methodology. The proposed learning approach is adaptive to the training data through a re-training process that includes only the most recent and relevant data, thus excluding misleading information from the training process. The learnt error patterns are then combined with the original forecasting results in order to improve forecasting accuracy, using the Rényi entropy to determine the amount in which the original forecasted value should be adapted considering the learnt error patterns. The proposed approach is combined with eleven different machine learning methodologies, and applied to the forecasting of electricity market prices using real data from the Iberian electricity market operator – OMIE. Results show that through the identification of patterns in the forecasting error, the proposed methodology is able to improve the learning algorithms’ forecasting accuracy and reduce the variability of their forecasting errors.

Educational innovation is a field that has been greatly enriched by using technology in its processes, resulting in a learning model where information comes from numerous sources and collaboration takes place among multiple students. One attractive challenge within educational innovation is the design of collaborative learning activities from the social computing point of view, where collaboration is not limited to student-to-student relationships, but includes student-to-machine interactions. At the same time, there is a great lack of tools that give support to the whole learning process and are not restricted to specific aspects of the educational task. In this paper, we present and evaluate context-aware framework for collaborative learning applications (CAFCLA) as a solution to these problems. CAFCLA is a flexible framework that covers the entire process of developing collaborative learning activities, taking advantage of contextual information and social interactions. Its application in the experimental case study of a collaborative WebQuest within a museum has shown that, among other benefits, the use of social computing improves the learning process, fosters collaboration, enhances relationships, and increases engagement.

This paper presents a joint trajectory smoothing and tracking framework for a specific class of targets with smooth motion. We model the target trajectory by a continuous function of time (FoT), which leads to a curve fitting approach that finds a trajectory FoT fitting the sensor data in a sliding time-window. A simulation study is conducted to demonstrate the effectiveness of our approach in tracking a maneuvering target, in comparison with the conventional filters and smoothers. Note to Practitioners-Estimation, such as automatically tracking and predicting the movement of an aircraft, a train, or a bus, plays a key role in our daily life. In this paper, we provide a new approach for the online estimation of the target trajectory function by means of fitting the time-series observation, which accommodates the lack of quantifiable knowledge about the target motion and of the statistical property of the sensor observation noise. The resulting trajectory function can be used to infer either the past or the present state of the target. Engineering-friendly strategies are provided for computationally efficient implementation. The proposed approach is particularly appealing to a broad range of real-world targets that move in smooth courses, such as passenger aircraft and ships.

We propose a straightforward but efficient networking approach to distributed multi-target tracking, which is free of ingenious target model design. We confront two challenges: One is from the lack of statistical knowledge about the target appearance/disappearance and movement, and about the sensors, e.g., the rates of clutter and misdetection; The other is from the severely limited computing and communication capability of the low-powered sensors, which may prevent them from running a full-fledged tracker/filter. To overcome these challenges, a flooding-then-clustering (FTC) approach is proposed which comprises two components: a distributed flooding scheme for iteratively sharing the measurements between sensors and a clustering-for-filtering approach for target detection and position estimation from the local aggregated measurements. We compare the FTC approach with cutting edge distributed probability hypothesis density (PHD) filters that are modeled with appropriate statistical knowledge about the target motion and the sensors. A series of simulation studies using either linear or nonlinear sensors, have been presented to verify the effectiveness of the FTC approach.

This work focuses on the design and validation of a CBR system for efficient face recognition under partial occlusion conditions. The proposed CBR system is based on a classical distance-based classification method, modified to increase its robustness to partial occlusion. This is achieved by using a novel dissimilarity function which discards features coming from occluded facial regions. In addition, we explore the integration of an efficient dimensionality reduction method into the proposed framework to reduce computational cost. We present experimental results showing that the proposed CBR system outperforms classical methods of similar computational requirements in the task of face recognition under partial occlusion.

Smart Grid technologies enable the intelligent integration and management of distributed energy resources. Also, the advanced communication and control capabilities in smart grids facilitate the active participation of aggregators at different levels in the available electricity markets. The portfolio optimization problem consists in finding the optimal bid allocation in the different available markets. In this scenario, the aggregator should be able to provide a solution within a timeframe. Therefore, the application of metaheuristic approaches is justified, since they have proven to be an effective tool to provide near-optimal solutions in acceptable execution times. Among the vast variety of metaheuristics available in the literature, Differential Evolution (DE) is arguably one of the most popular and successful evolutionary algorithms due to its simplicity and effectiveness. In this paper, the use of DE is analyzed for solving the portfolio optimization problem in electricity markets. Moreover, the performance of DE is compared with another powerful metaheuristic, the Particle Swarm optimization (PSO), showing that despite both algorithms provide good results for the problem, DE overcomes PSO in terms of quality of the solutions.

The high amount of information that modern multi-criteria group decision making environments must handle requires the development of novel methods. These methods should be able to work with high amounts of alternatives while providing the experts a comfortable framework that they can use to carry out this complex decisions. In this paper, a novel method that tries to solve this issue is presented. Our method makes use of fuzzy ontologies in order to allow the experts to focus on what is more important: the weight that should be given to each criteria. This way, they do deal directly with the high amount of alternatives. Experts decide the importance of each criteria and the alternatives ranking is calculated automatically using the fuzzy ontology.

Technology is starting to play a key role in cities’ urban sustainability plans. This is because new technologies can provide them with robust solutions that are of benefit to citizens. Cities aim to incorporate smart systems in their industrial, infrastructural, educational, and social activities. A Smart City is managed with intelligent technologies which allow improving the quality of the services offered to citizens and make all processes more efficient. However, the Smart City concept is fairly recent. The ideas that it encompasses have not yet been consolidated due to the large number of fields and technologies that fit under this concept. All of this led to confusion about the definition of a Smart City and this is evident in the literature. This article explores the literature that addresses the topic of Smart Cities; a comprehensive analysis of the concept and existing platforms is performed. We gain a clear understanding of the services that a Smart City must provide, the technology it should employ for the development of these services, and the scope that this concept covers. Moreover, the shortcomings and needs of Smart Cities are identified and a model for designing a Smart City architecture is proposed. In addition, three case studies have been proposed: the first is a simulator to study the implementation of various services and technologies, the second case study to manage incidents that occur in a Smart City, and the third case study to monitor the deployment of large‐scale sensors in a Smart City.

This paper presents the design and development of an innovative multiagent system based on virtual organizations. The multiagent system manages information from wireless sensor networks for knowledge discovery and decision making in rural environments. The multiagent system has been built over the cloud computing paradigm to provide better flexibility and higher scalability for handling both small‐ and large‐scale projects. The development of wireless sensor network technology has allowed for its extension and application to the rural environment, where the lives of the people interacting with the environment can be improved. The use of “smart” technologies can also improve the efficiency and effectiveness of rural systems. The proposed multiagent system allows us to analyse data collected by sensors for decision making in activities carried out in a rural setting, thus, guaranteeing the best performance in the ecosystem. Since water is a scarce natural resource that should not be wasted, a case study was conducted in an agricultural environment to test the proposed system’s performance in optimizing the irrigation system in corn crops. The architecture collects information about the terrain and the climatic conditions through a wireless sensor network deployed in the crops. This way, the architecture can learn about the needs of the crop and make efficient irrigation decisions. The obtained results are very promising when compared to a traditional automatic irrigation system.

Today, most energy‐intensive processes have a high degree of optimization. However, in some of these processes large amounts of energy are inevitably released due to the way in which this energy is used. One of the most obvious examples is produced in the power plants during the process of obtaining electrical energy through the transformation of some kind of energy (chemical, kinetic, thermal, lighting, and nuclear or solar energy, among others). This released energy can be used in other processes that may need it so that no additional energy is needed. One of the possible uses of this energy is its use in greenhouses. Greenhouses need large amounts of energy to recreate the climatic conditions that crops need, which are not those of the weather station. To take advantage of the energy released from the power stations in greenhouses, a system based on agents has been developed that manages energy and allows it to be reused. This paper explains how the system allows us to reuse energy by a power plant and how the agents that integrate the system by means of communication with sensors and actuators and the use of data analysis algorithms allow us to use this energy in greenhouses, providing a reduction of the energy they need without the system. The system has been tested in several greenhouses with a pepper crop.

This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.

In this paper, a new interval optimization method is proposed to manage the uncertainty of stochastic variables to the problem of Residential Energy Management (REM). This new method is called Stochastic Predicted Bands (SPB) and it considers the uncertainty of decision making variables without knowledge of the Probability Density Function (PDF). The modeling of uncertainty is done by bands based on the prediction of stochastic variables. Besides, an auxiliary parameter is defined to provide flexibility to the decision-maker to be optimistic or conservative. Hence, applying the optimistic coefficient to the SPB method results in the enhancement of its performance. This new method is called Modified Stochastic Predicted Bands (MSPB). The simulation results of the test system show the performance of the proposed model in solving energy management problems via SPB method.

Under the common state space model for tracking a maneuvering target, the tracker needs to adapt its state transition model timely to match the target maneuver, which is usually carried out by finding the best one from a bank of candidate Markov models or employing all of them simultaneously but assigning different probabilities. Both methods suffer from time delay for confirming the target maneuver. To avoid these problems, we model the target motion by a continuous time trajectory function and the tracking problem is formulated as an optimization problem with the goal of finding the trajectory function that best fits the observation over a sliding time window. The trajectory function can be used for smoothing, filtering and even prediction. The approach is particularly applicable to a class of target motion patterns such as passenger aircraft, where little prior statistical information is available on the target dynamics or even the sensor observation except the linguistic information that “the target moves in a smooth trajectory” (as being called smoothly maneuvering target). Simulation is provided to demonstrate the supremacy of our approach with comparison to a number of classical Markov-Bayes approaches, based on Hartikainen et al.'s example.

Some concerns are raised on the prevailing generalized covariance intersection (GCI) based Gaussian mixture probability hypothesis density (GM-PHD) fusion for distributed multiple target tracking under cluttered environments, which is both communicative and computation expensive, and generates a large amount of Gaussian components (GCs) of little physical significance. The problems become more serious when targets are closely distributed and/or when clutter is heavy. To avoid these problems and to save communication and computation, we advocate to only share the sufficiently strong-weighted GCs between neighboring sensors. The shared significant GCs are simply merged based on their spatial proximity, which resembles a type of multisensor signal superposition and will enhance the signal-noise-ratio (SNR) since strong GCs are more likely to be a “target signal” than a weak one, thereby facilitating less likely false alarms and a more accurate estimation. In parallel to the conservative GC sharing and merging, a standard averaging consensus is also sought on the cardinality distribution (a.k.a. the probability distribution of the target number) among sensors. Simulations have been provided to demonstrate the superiority and reliability of our approach with comparison to the benchmark GCI approach.

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.

A preliminary framework for inferring continuous-time target trajectory (namely “track”) is given for a class of target tracking problems in which the target is subject to a rather smooth evolving process in time series, such as tracking passenger aircrafts or ships that have scheduled routes. As the core idea, the distant estimates given over time by a recursive estimator are `fitted' by using a function of continuous-time, which can be then used to infer the state for any time instants in the effective fitting period, either the past (like conventional smoothing, but curried out online) or the future (including long-term prediction). This regression analysis methodology, referred to as fitting for smoothing (F4S), also facilitates combating misdetection and outliers from which most existing tracking systems suffer. Simulations are provided to illustrate how it works and benefits in either cluttered or non-cluttered environments, with either a single target or multiple targets.

4:1! Google’s artificial intelligence (AI) program, AlphaGo, has won Go Master Lee Sedol in a best-of-five competition held in Korean March 9−15, 2016. Seen by many as a landmark moment for AI, the outcome did not come as a surprise, considering the excellent combination of 1920 CPUs with sophisticated AI algorithms, including neural networks and Monte Carlo tree search (Gibney, 2016; Silver et al ., 2016). Indeed, research on distributed computing and artificial intelligence (DCAI) has matured during the last decade and many effective applications are now deployed, performing an increasingly important role in modern computer science, including the two most hyped technologies: Internet of Things and Big Data. Indeed, it is fair to say that the application of artificial intelligence in distributed environments is becoming an essential element of high added value and economic potential.

Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial (Li et al., 2015). First, identical distribution (ID) is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent metrics including the (symmetrical) Kullback-Leibler divergence, Kolmogorov-Smirnov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive understanding of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations.

Organizations of agents based on Virtual Organizations needs to be supported by a coordinated effort that explicitly determines how the agents should be organized and carry out the actions and tasks assigned to them. The interactions of a multi-agent system cannot be only related to the agent and the communication skills, but also to the concepts of organizational engineering. Moreover, nowadays there is a clear trend towards using methods and tools that can help to develop and simulate virtual organizations by means of multiagent systems (MAS). Simulation is used for several purposes ranging from work flow to system's procedures representation. The contribution from agent based computing to the field of computer simulation mediated by ABS (Agent Based Simulation) is a new paradigm for the simulation of complex systems that require a high level of interaction between the entities of the system. The main goal of this work is a new global coordination model for an agent organization in a simulation context. The innovation of this work consists of the dynamic and adaptive planning capability to distribute tasks among the agent of the organization. The middleware used for simulation makes it possible to visualize the emergent agent behaviour and the entity agent.

During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. Nonetheless, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. In this regard, the use of context-aware technologies is an essential aspect in these developments to perceive stimuli from the context and react upon it autonomously. This work presents a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSNs). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. Furthermore, the information obtained must be managed by intelligent and self-adaptable technologies to provide an adequate interaction between the users and their environment. Agents and Multi-Agent Systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI. This way, the integration of the platform with a Service-Oriented Multi-Agent architecture is proposed. Finally, conclusions and future work are presented.

Oil spills represent one of the most destructive environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be critical in reducing environmental risks. The system presented here uses the Case-Based Reasoning (CBR) methodology to forecast the presence or absence of oil slicks in certain open sea areas after an oil spill. CBR is a computational methodology designed to generate solutions to certain problems by analysing previous solutions given to previously solved problems. The proposed CBR system includes a novel network for data classification and retrieval. This type of network, which is constructed by using an algorithm to summarize the results of an ensemble of Self-Organizing Maps, is explained and analysed in the present study. The Weighted Voting Superposition (WeVoS) algorithm mainly aims to achieve the best topographically ordered representation of a dataset in the map. This study shows how the proposed system, called WeVoS-CBR, uses information such as salinity, temperature, pressure, number and area of the slicks, obtained from various satellites to accurately predict the presence of oil slicks in the north-west of the Galician coast, using historical data.

Ambient intelligence has acquired great importance in recent years and requires the development of new innovative solutions. This paper presents a distributed telemonitoring system, aimed at improving healthcare and assistance to dependent people at their homes. The system implements a service-oriented architecture based platform, which allows heterogeneous wireless sensor networks to communicate in a distributed way independent of time and location restrictions. This approach provides the system with a higher ability to recover from errors and a better flexibility to change their behavior at execution time. Preliminary results are presented in this paper.

This paper proposes a multiagent system (MAS) that uses smart wearable devices and mobile technology for the care of patients in a geriatric home care facility. The system is based on an advanced ZigBee wireless sensor network (WSN) and includes location and identification microchips installed in patient clothing and caregiver uniforms. The use of radio-frequency identification and near-field communication technologies allows remote monitoring of patients, and makes it possible for them to receive treatment according to preventive medical protocol. The proposed MAS manage the infrastructure of services within the environment both efficiently and securely by reasoning, task-planning, and synchronizing the data obtained from the sensors. Additionally, this paper presents the design and implementation of the reasoning agent in the MAS. A system prototype was installed in a real environment and the results obtained are presented in this paper.

BACKGROUND: Bioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine. RESULTS: In addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can diagnose cancer subtypes based on the analysis of microarray data using a CBR architecture. For programmers, geneCBR programming mode includes an advanced edition module for run-time modification of previous coded techniques. CONCLUSION: geneCBR is a new translational tool that can effectively support the integrative work of programmers, biomedical researches and clinicians working together in a common framework. The code is freely available under the GPL license and can be obtained at http://www.genecbr.org.

This paper presents a multiagent architecture (GR-MAS) developed for facilitating health care in geriatric residences. GR-MAS (Geriatric Residence Multi-Agent System) contains different agent types and takes into account the integration within RFID, Wi-Fi technologies and handheld devices. The core of GR-MAS is an autonomous deliberative case-based planner agent called GerAg (Geriatric Agent for monitoring alzheimer patients). This agent, which allows adaptation and learning capabilities, has been designed to plan the nurses' working time dynamically, to maintain the standard working reports about the nurses' activities, and to guarantee that the patients assigned to the nurses are given the right care. A description of GerAg, its relationship with the complementary agents, and preliminary results of the multi-agent system prototype in a real environment are presented.

Oil spills represent one of the most destructing environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be crucial in order to reduce the environmental risks. The system presented here forecasts the presence or not of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. CBR is a computational methodology designed to generate solutions to a certain problem by analysing previous solutions given to previous solved problems. The proposed system wraps other artificial intelligence techniques such as a Radial Basis Function Networks, Growing Cell Structures and Principal Components Analysis in order to develop the different phases of the CBR cycle. CBR systems have never been used before to solve oil slicks problems. The proposed system uses information obtained from various satellites such as salinity, temperature, pressure, number and area of the slicks... OSCBR system has been able to accurately predict the presence of oil slicks in the north west of the Galician coast, using historical data.

Microarray technology allows to measure the expression levels of thousands of genes in an experiment. The use of computational methods is fundamental in cancer research. One of the possibilities is the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays. This paper presents a Case-based reasoning (CBR) system for automatic classification of leukemia patients from microarray Data. The system incorporates novel algorithms for data mining that allow to filter and classify as well as extraction of knowledge. The system has been tested and the results obtained are presented in this paper.

This paper proposes a replanning mechanism for deliberative agents as a new approach to tackling the frame problem. We propose a beliefs desires and intentions (BDI) agent architecture using a case‐based planning (CBP) mechanism for reasoning. We discuss the characteristics of the problems faced with planning where constraint satisfaction problems (CSP) resources are limited and formulate, through variation techniques, a reasoning model agent to resolve them. The design of the agent proposed, named MRP‐Ag (most‐replanable agent), will be evaluated in different environments using a series of simulation experiments, comparing it with others such as E‐Ag (Efficient Agent) and O‐Ag (Optimum Agent). Last, the most important results will be summarized, and the notion of an adaptable agent will be introduced.

Early work on case-based reasoning (CBR) reported in the literature shows the importance of soft computing techniques applied to different stages of the classical four-step CBR life cycle. This correspondence proposes a reduction technique based on rough sets theory capable of minimizing the case memory by analyzing the contribution of each case feature. Inspired by the application of the minimum description length principle, the method uses the granularity of the original data to compute the relevance of each attribute. The rough feature weighting and selection method is applied as a preprocessing step prior to the generation of a fuzzy rule system, which is employed in the revision phase of the proposed CBR system. Experiments using real oceanographic data show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules

Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well‐known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state‐of‐the‐art algorithms is crucial to the development of successful applications. This paper presents gene‐CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case‐based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty‐three adult patients with cancer plus a group of six cases corresponding to healthy persons.

Case-based reasoning systems, in general, and instance-based reasoning systems, in particular, are used more and more in industrial applications nowadays. During the last few years, researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a method for automating the retrieval stage and indexation of instance-based reasoning systems. This method is based on a modification of a new type of topology preserving map that can be used for scale invariant classification. The scale invariant map is an implementation of the negative feedback network to form a topology-preserving mapping. Maximum/minimum likelihood learning is applied in this paper to the scale invariant map and its possibilities are explored. This method automates the organization of cases and the retrieval stage of case-based reasoning systems. The proposed methodology groups instances with similar structure, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.

This paper shows how deliberative agents can be built by means of a case-based reasoning system. The concept of deliberative agent is introduced and the case-based reasoning model is presented. Once the advantages and disadvantages of such agents have been discussed, it will be shown how to solve some of their inconveniences, especially those related to their implementation and adaptation. The World Wide Web has emerged as one of the most popular vehicle for disseminating and sharing information through computer networks; a distributed agent-based solution for e-business, in which such agents have been used, is also presented and evaluated in this paper.

This article shows how autonomous agents may be constructed with the help of case-based reasoning (CBR) systems. The advantages and disadvantages of deliberative agents are discussed, and it is shown how to solve some of their inconveniences, especially those related to their implementation and adaptation. The Internet is one of the most popular vehicles for disseminating and sharing information through computer networks and it is influencing the business world. An agent-based solution is presented to show how the proposed technology may facilitate and improve an e-business strategy. © 2003 Wiley Periodicals, Inc.

An approach to hybrid artificial intelligence problem solving is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. In situations in which the rules that determine a system are unknown or fuzzy, the prediction of the parameter values that determine the characteristic behavior of the system can be a problematic task. In such a situation, it has been found that a hybrid artificial intelligence model can provide a more effective means of performing such predictions than either connectionist or symbolic techniques used separately. The hybrid forecasting system that has been developed consists of a case-based reasoning system integrated with a radial basis function artificial neural network. The results obtained from experiments in which the system operated in real time in the oceanographic environment, are presented.

An instance-based problem solving model is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. The situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that an instance-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The instance-based reasoning system incorporates a radial basis function artificial neural network for the instance adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented.

Instance-based reasoning systems and, in general, case-based reasoning systems are normally used in problems for which it is difficult to define rules. Although case-based reasoning methods have proved their ability to solve different types of problems, there is still a demand for methods that facilitate their automation during their creation and the retrieval and reuse stages of their reasoning circle. This paper presents one method based on kernels, which can be used to automate some of the reasoning steps of instance-based reasoning systems. Kernels were originally derived in the context of support vector machines, which identify the smallest number of data points necessary to solve a particular problem (e.g., regression or classification). Unsupervised kernel methods have been used successfully to identify the optimal instances to instantiate an instance base. The efficiency of the kernel model is shown on an oceanographic problem. © 2001 John Wiley & Sons, Inc.

An unsupervised neural based approach to financial forecasting is presented; its performance is compared with that from a statistical technique and two other standard neural network techniques. The authors show that the unsupervised network outperforms multilayer perceptrons, radial basis function network and a standard ARIMA model.