Abstract Detecting hate speech is challenging because language constantly evolves, often carrying subtle emotional undertones that can be difficult to interpret. While many existing approaches rely on semantic analysis, they frequently miss these emotional cues and struggle to adapt to different contexts. This paper introduces an enhanced Dual Contrastive Learning (DCL) framework, integrating emotion profiling with fine-tuned language models (BERTweet, RoBERTa, TimeLMs) to address these gaps. By using SHAP (SHapley Additive exPlanations) for interpretability, our approach enhances both detection accuracy and explainability. Experiments on SemEval-2019, Davidson, and a Unified Twitter corpus show that our EmotionDCL models outperform baseline methods, with TimeLMs achieving 81.20% accuracy on SemEval and 94.04% on Davidson. Ablation studies confirm the synergy between contrastive learning and emotion integration, highlighting anger, fear, and anticipation as dominant emotional drivers of hate speech. These findings show the importance of emotion-aware, context-adaptive detection systems and the need for strategies to address class imbalance and linguistic evolution.

Abstract The extensive use of Internet of Things devices leads to the generation of vast amounts of high-frequency time-series data. Analysing these data provides valuable insights for business decision-making. However, to provide timely decisions, we should be able to analyse time-series streaming data on the fly. While numerous advanced analytical techniques have been developed to rapidly identify behaviour profiles that highlight prominent patterns and enable anomaly detection in time-series data, these methods are mainly tailored to static datasets rather than dynamic data streams. In addition, existing methods for incrementally detecting and visualising patterns and anomalies in multi-variate time-series streaming data still have limitations. This paper proposes a swarm intelligence-based visual analytics approach and algorithm to learn behaviour profiles incrementally, automatically determining the appropriate number of profiles from the data stream. Each profile is a network of data prototypes representing the data distributions. A new visualisation method is developed to visualise behaviour profiles that can reveal cyclic patterns and anomalies without requiring a dimensionality reduction step. Experiments with real-world time-series datasets show that the proposed approach can capture, cluster and visualise key patterns and anomalies, providing actionable insights from the data.

This paper introduces a novel approach, evolutionary multi-objective optimisation for fairness-aware self-adjusting memory classifiers, designed to enhance fairness in machine learning algorithms applied to data stream classification. With the growing concern over discrimination in algorithmic decision-making, particularly in dynamic data stream environments, there is a need for methods that ensure fair treatment of individuals across sensitive attributes like race or gender. The proposed approach addresses this challenge by integrating the strengths of the self-adjusting memory K-Nearest-Neighbour algorithm with evolutionary multi-objective optimisation. This combination allows the new approach to efficiently manage concept drift in streaming data and leverage the flexibility of evolutionary multi-objective optimisation to maximise accuracy and minimise discrimination simultaneously. We demonstrate the effectiveness of the proposed approach through extensive experiments on various datasets, comparing its performance against several baseline methods in terms of accuracy and fairness metrics. Our results show that the proposed approach maintains competitive accuracy and significantly reduces discrimination, highlighting its potential as a robust solution for fairness-aware data stream classification. Further analyses also confirm the effectiveness of the strategies to trigger evolutionary multi-objective optimisation and adapt classifiers in the proposed approach.

This paper presents the design and implementation of a technology platform to capture and analyze the real-time behavior of stroke survivors in their everyday living environments. We collected data on participants ‘self-perceived activity challenges and skills, moods, and environmental context using the Experience Sampling Method (ESM). Through involving clinicians, therapists, and researchers in a co-design methodology, we built the Staying Connected mobile app using the latest mobile technologies and an interactive dashboard for data analysis and visualization. A pilot study involving stroke survivors and healthy individuals was conducted to refine the user experience and validate the system. The data analysis was based on Flow theory, which emphasizes a state of deep involvement and satisfaction in activities. The platform adeptly integrates real-time experience data collection via random sampling and goal directed activity-based feedback. Out of 327 experience samples collected from 11 healthy participants, 43 (13%) were found in flow. Similarly, from 112 experience samples collected from 8 stroke survivors, 15 (13%) were found in flow. An interactive analytic dashboard provided therapists with more tailored insights into the participants' real-time behaviors and experiences. The development of this platform marks a significant step forward in capturing and comprehending the real-time behavior of stroke survivors. It supports the effectiveness of the co-design process in developing real-time clinical applications and highlights the platform's capacity to customize therapies and enhance patient outcomes.

Large Language Models (LLMs) are leading the Generative Artificial Intelligence transformation in natural language understanding. Beyond language understanding, LLMs have demonstrated capabilities in reasoning tasks, including commonsense, logical, and mathematical reasoning. However, their proficiency in causal understanding has been limited due to the complex nature of causal reasoning. Several recent studies have discussed the role of external causal models for improved causal understanding. Building on the success of Retrieval-Augmented Generation (RAG) for factual reasoning in LLMs, this paper introduces a novel approach that utilizes Causal Graphs as external sources for establishing causal relationships between complex vectors. This method is empirically evaluated using two benchmark datasets across the metrics of Context Relevance, Answer Relevance, and Grounding, in its ability to retrieve relevant context with causal alignment. The retrieval effectiveness is further compared with traditional RAG methods that are based on semantic proximity.

Motivated by recent innovations in biologically inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on the principles of vector symbolic architectures (VSAs) for fast learning of a topology preserving feature map of unlabeled data. It relies on two major operations of VSA, binding and bundling. The algorithmic part of Hyperseed is expressed within the Fourier holographic reduced representations (FHRR) model, which is specifically suited for implementation on spiking neuromorphic hardware. The two primary contributions of the Hyperseed algorithm are few-shot learning and a learning rule based on single vector operation. These properties are empirically evaluated on synthetic datasets and on illustrative benchmark use cases, IRIS classification, and a language identification task using the n -gram statistics. The results of these experiments confirm the capabilities of Hyperseed and its applications in neuromorphic hardware.

Machine perception of emotions is integral to the development of human-centric Artificial Intelligence (AI) in sustainable industrial applications. Human expressions of emotions are not always direct. Word embeddings are mature techniques that can extract the semantics of such indirect expressions from text data. However, they are not primed to extract emotions. In this paper, we propose a novel approach that generates robust word embeddings for implicit and explicit expressions of emotion. This approach consists of two techniques, mask and rogue, we evaluate both techniques on two benchmark datasets for emotion classification. Our results confirm the effectiveness of the proposed approach in extracting emotions from diverse contexts. We have shared the emotion word embedding for public use.

Contemporary energy platforms are leveraging advanced data management and Artificial Intelligence (AI) capabilities in response to the increasing complexity of energy systems and grids. Despite these advances, it is a non-trivial and challenging task to support the decision-making needs of the human operators of such complex energy-related implementations. Conversational agents or chatbots are a potential emerging technology that can be utilized to address this challenge. Although there is a large body of literature on chatbots in general, they are not robust as they rely on predefined conversational pathways that are inadequate to efficiently address the complexities of dynamic data spaces in energy platforms. The capability of generating answers in real-time by communicating with the dynamic dataspace is crucial as energy management decisions are real-time and time sensitive. In this paper, we present the design and development of Cooee, a chatbot for conversational engagement with the dynamic data spaces of complex energy environments. Cooee leverages state-of-art language models along with rule-based language processing methods for a conversational interaction with dynamic data spaces, which consequently supports and enables decision-making by human experts. We have developed Cooee as a standalone application and then integrated into a real-world energy AI platform deployed within a multi-campus tertiary education institution setting. Cooee was empirically evaluated in this setting and compared with several state-of-the-art Q&A approaches.

Idioms are figures of speech that contradict the principle of compositionality. This disposition of idioms can misdirect Natural Language Processing (NLP) techniques, which mostly focus on the literal meaning of terms. In this paper, we propose a novel idiom detection model that distinguishes between literal and idiomatic expressions. It utilizes a token classification approach to fine-tune BERT(Bidirectional Encoder Representations from Transformers). It is empirically evaluated on four idiom datasets, yielding an accuracy of more than 0.94. This model adds to the robustness and diversity of NLP techniques available to process and understand increasing magnitudes of free-form text and speech. Furthermore, the social value of this model is in enabling non-native speakers to comprehend the nuances of a foreign language.

The rapid advancement of the energy sector in terms of diverse energy generation options and increasing energy consumption loads has eventuated the need for highly accurate demand forecasting methods. The prevalence of large volumes of energy data streams and sophisticated Artificial Intelligence (AI) algorithms has enabled a rapid transition to AI-based forecasting methods that are more accurate and computationally efficient. Despite this transition, demand forecasting during peak events and peak temporal periods continues to be a challenge due to the irregularity and transience of such events. Besides the challenge of managing supply and demand, the financial viability of forecasting is also questioned when the forecast decreases in accuracy during peak periods when the energy price is an increasing function. In this paper, we have set out to address the challenge of peak demand forecasting by specifically transforming both input vectors and input attributes of the smart meter data streams. Input vectors are transformed using active thresholding while input attributes are transformed into a feature subset using model explainability. We have evaluated the effectiveness of this data transformation on the current state-of-the-art AI for energy demand forecasting, gradient boosting. We conduct a comparative evaluation using two real-world energy consumption datasets drawn from the La Trobe Energy AI/Analytics Platform (LEAP), of La Trobe University’s Net Zero Carbon Emissions Program. The proposed approach surpasses the baseline approach in both datasets, with an improvement of 27% for the second dataset which is a high energy consumption setting.

Healthcare systems and services are rapidly transitioning towards a patient-centred care approach. As an allied health profession that is highly individualised, physiotherapy, requires technological innovations to advance into this space. Virtual Reality (VR) technologies have been explored and applied successfully towards achieving this transition, specifically in single-player and multi-player gaming configurations. However, most VR games are not customised to the individuals and not appropriate for the rehabilitation of patients with unique needs. Given the large volumes of data generated by the patient during an engagement with a VR game, Artificial Intelligence (AI) can be leveraged to deliver personalised physiotherapy rehabilitation. This is a work-in-progress paper that presents the design and development of a framework for personalised physiotherapy rehabilitation using AI and VR, in the setting of a single-player VR game. The completed framework will be trialled in a public hospital setting to evaluate its effectiveness in providing patient-centred physiotherapy rehabilitation for diverse patient cohorts.

Hyperdimensional (HD) Computing leverages random high dimensional vectors (>10000 dimensions) known as hypervectors for data representation. This high dimensional feature representation is inherently redundant which results in increased robustness against noise and it also enables the use of a computationally simple operations for all vector functions. These two properties of hypervectors have led to energy efficient and fast learning capabilities in numerous Artificial Intelligence (AI) applications. Despite the increasing number of such AI HD applications, their susceptibility to adversarial attacks has not been explored, specifically in the text domain. To the best of our knowledge, this is the first research endeavour to evaluate the adversarial robustness of HD text classifiers and report on their vulnerability to such attacks. In this paper, we designed and developed n-grams based HD computing text classifiers for two primary applications of HD computing; language recognition and text classification, and then performed a set of character level and word level grey-box adversarial attacks, where an attacker’s goal is to mislead the target HD computing classifier to produce false prediction labels while keeping added perturbation noise as low as possible. Our results show that adversarial examples generated by the attacks can mislead the HD computing classifiers to produce incorrect prediction labels. However, HD computing classifiers show a higher degree of adversarial robustness in language recognition compared to text classification tasks. The robustness of HD computing classifiers against character-level attacks is significantly higher compared to word-level attacks and has the highest accuracy compared to deep learning-based classifiers. Finally, we evaluate the effectiveness of adversarial training as a possible defense strategy against adversarial attacks in HD computing text classifiers.

Safe and effective vaccination is leading the recovery from the COVID-19 pandemic. Despite the urgency of full vaccination that prevents serious illness, a state of vaccine messaging augmented by disinformation campaigns in online social networks has emerged. Several studies have established a link between social media activity and vaccine messaging, and most platforms are actively removing vaccine disinformation. The objective of this study is to apply a validated Artificial Intelligence (AI) framework to extract, analyze and synthesize themes, emotions and emotion transitions associated with COVID-19 vaccine messaging in online social networks. We applied the framework on approximately 400,000 COVID-19 vaccine-related posts and conversations on two social media platforms, Twitter and Reddit, from March 2020 to September 2021. The results of this study are threefold, firstly, the discovery of a minority of implied anti-vaccine themes on infertility, microchips, gene editing and fetal cells that have remained undetected. Secondly, the discovery of six themes that capture a majority of the vaccine messaging namely, social lockdown measures, frontline healthcare providers, side effects, vaccine distribution, breakthrough infections and vaccine efficacy on variants. Thirdly, the variety and intensity of emotions expressed since the start of the pandemic, and comparatively negative emotions being expressed in recent months. We anticipate the findings of our study will contribute towards improved vaccine messaging as the world returns to a new normal from the COVID-19 pandemic.

We introduce an open dataset of high-granularity Photovoltaic (PV) solar energy generation, solar irradiance, and weather data from 42 PV sites deployed across five campuses at La Trobe University, Victoria, Australia. The dataset includes approximately two years of PV solar energy generation data collected at 15-minute intervals. Geographical placement and engineering specifications for each of the sites are also provided to aid researchers in modelling solar energy generation. Weather data is available at 1-minute intervals and is provided by the Australian Bureau of Meteorology (BOM). Apparent temperature, air temperature, dew point temperature, relative humidity, wind speed, and wind direction were provided under the weather data. The paper describes the data collection methods, cleaning, and merging with weather data. This dataset can be used to forecast, benchmark, and enhance operational outcomes in solar sites.

The increasing use of Vector Symbolic Architectures (VSA) in machine learning has contributed towards en-ergy efficient computation, short training cycles and improved performance. A further advancement of VSA is to leverage sparse representations, where the VSA-encoded hypervectors are sparsified to represent receptive field properties when encoding sensory inputs. The hyperseed algorithm is an unsupervised machine learning algorithm based on VSA for fast learning a topology preserving feature map of unlabelled data. In this paper, we implement two methods of sparse block-codes on the hyperseed algorithm, they are selecting the maximum element of each block and selecting a random element of each block as the nonzero element. Finally, the sparsified hyperseed algorithm is empirically evaluated for performance using three distinct bench-mark datasets, Iris classification, classification and visualisation of synthetic datasets from the Fundamental Clustering Problems Suite and language classification using n-gram statistics.

Sequence-based methods for visual place recognition (VPR) have great importance due to their ability of additional information capture through the sequences compared to single image comparison. Vector symbolic architecture (VSA) started to gain attention within these methods due to the unique capabilities for representing variable-length sequences using single high-dimensional vectors. But the effect of different sequence parameters for the visual place recognition task is yet to be explored. In this work, we explore the parametrization of sequence encoding with VSA in the SeqNet variant of sequence-based visual place recognition and introduce a new hierarchical VPR method, which utilizes the proposed parametrization. We show that with our parametrization the VSA realization of sequence-based visual place recognition achieves on par results to conventional algorithms, while featuring the capability of being implemented on novel neuromorphic hardware for efficient execution.

Abstract Understanding how people engage in daily activities within a region can provide valuable information for smart city planners and strategic partners to use to assist in their decision‐making processes. Such insights may relate to economic activities, sustainable city design, environmental impacts, and responses to climate change, contributing to the improvement in the quality of human life. Considerable attention is recently directed towards smart city initiatives that benefit majority of people, rather than projects that cater to the political, architectural, or vanity needs of a minority. However, understanding citizen requirements, behaviors, and opinions is difficult, and requires the use of technology and appropriate information sources. While social‐media big data have provided opportunities to develop evidence‐based insights into human daily activities, effective analytical methods to harness these opportunities remain in development. We propose a new analytical method to provide a deeper understanding of citizen activities by constructing building blocks in their activity storylines, with analysis of these storylines providing evidence‐based insights into their activities. Results demonstrate the usefulness of our method to smart city planners and strategic partners, providing invaluable insights to assist them in making decisions regarding sustainable smart city development.

Abstract The advancements of the Internet of Things (IoT) and voice-based multimedia applications have resulted in the generation of big data consisting of patterns, trends and associations capturing and representing many features of human behaviour. The latent representations of many aspects and the basis of human behaviour is naturally embedded within the expression of emotions found in human speech. This signifies the importance of mining audio data collected from human conversations for extracting human emotion. Ability to capture and represent human emotions will be an important feature in next-generation artificial intelligence, with the expectation of closer interaction with humans. Although the textual representations of human conversations have shown promising results for the extraction of emotions, the acoustic feature-based emotion detection from audio still lags behind in terms of accuracy. This paper proposes a novel approach for feature extraction consisting of Bag-of-Audio-Words (BoAW) based feature embeddings for conversational audio data. A Recurrent Neural Network (RNN) based state-of-the-art emotion detection model is proposed that captures the conversation-context and individual party states when making real-time categorical emotion predictions. The performance of the proposed approach and the model is evaluated using two benchmark datasets along with an empirical evaluation on real-time prediction capability. The proposed approach reported 60.87% weighted accuracy and 60.97% unweighted accuracy for six basic emotions for IEMOCAP dataset, significantly outperforming current state-of-the-art models.

Resource constraint job scheduling is an important combinatorial optimization problem with many practical applications. This problem aims at determining a schedule for executing jobs on machines satisfying several constraints (e.g., precedence and resource constraints) given a shared central resource while minimizing the tardiness of the jobs. Due to the complexity of the problem, several exact, heuristic, and hybrid methods have been attempted. Despite their success, scalability is still a major issue of the existing methods. In this study, we develop a new genetic programming algorithm for resource constraint job scheduling to overcome or alleviate the scalability issue. The goal of the proposed algorithm is to evolve effective and efficient multipass heuristics by a surrogate-assisted learning mechanism and self-competitive genetic operations. The experiments show that the evolved multipass heuristics are very effective when tested with a large dataset. Moreover, the algorithm scales very well as excellent solutions are found for even the largest problem instances, outperforming existing metaheuristic and hybrid methods.

The emergence of low latency and high bandwidth 5G networks, alongside localized computation and data storage of edge computing are enabling real-time applications in industrial settings, such as smart grid, smart cities, and smart factories. The resolution, frequency and variety of data streams generated by such applications are not effectively processed and analysed by contemporary machine learning algorithms. This challenge is further complicated by the unlabelled and non-deterministic nature of the data streams. Hardware accelerated machine learning has been proposed to address some of these challenges but limited work has been published on unsupervised learning from unlabelled data. In this paper, we extend the hardware accelerated Self Organizing Map (SOM) algorithm by optimizing the learning rule for computational efficiency, followed by a comparative empirical evaluation with two other variants, tri-state SOM and integer SOM. We have used two datasets representative of real-time industrial applications in 5G networks and smart grids, for this evaluation.

Evolving production scheduling heuristics is a challenging task because of the dynamic and complex production environments and the interdependency of multiple scheduling decisions. Different genetic programming (GP) methods have been developed for this task and achieved very encouraging results. However, these methods usually have trouble in discovering powerful and compact heuristics, especially for difficult problems. Moreover, there is no systematic approach for the decision makers to intervene and embed their knowledge and preferences in the evolutionary process. This article develops a novel people-centric evolutionary system for dynamic production scheduling. The two key components of the system are a new mapping technique to incrementally monitor the evolutionary process and a new adaptive surrogate model to improve the efficiency of GP. The experimental results with dynamic flexible job shop scheduling show that the proposed system outperforms the existing algorithms for evolving scheduling heuristics in terms of scheduling performance and heuristic sizes. The new system also allows the decision makers to interact on the fly and guide the evolution toward the desired solutions.

Over the past decade, smart city applications have gained significant attention in industrial informatics. However, little attention has been given to perceiving the emotions and perceptions of citizens who have a direct impact on smart city initiatives. In this article, we propose the use of publicly available abundant social media conversations that contain contextual information encompassing citizens' emotions and perceptions, which could be considered to provide the means to feel the “emotional pulse” of a city. We propose an automated AI-based observation framework to detect the emergence of public emotions and negativity in conversations. We evaluated the applicability of the framework using 29 928 social media conversations toward the much-debated topic of self-driving vehicles which will become increasingly relevant to smart cities. The patterns and transitions of citizens' collective emotions were modeled using the Natural Language Processing and Markov models while the negativity (toxicity) in conversations was evaluated using a deep learning based classifier. The framework could be adopted by industry leaders and government officials for smart observation of citizen opinions to improve security, communication, and policymaking.

Growing big data has posed a great challenge for machine learning algorithms. To cope with big data, the algorithm has to be both efficient and accurate. Although evolutionary computation has been successfully applied to many complex machine learning tasks, its ability to handle big data is limited. In this paper, we proposed a dynamic self-organising swarm algorithm to learn an effective set of prototypes for big high-dimensional datasets in an unsupervised manner. The novelties of this new algorithm are the energy-based fitness function, the adaptive topological neighbourhood, the growing/shrinking capability, and the efficient learning scheme. Experiments with well-known datasets show that the proposed algorithm can maintain a very compact set of prototypes and achieve competitive predictive performance as compared to other algorithms in the literature. The analyses also show that prototypes generated by the proposed algorithms have a stronger separatability compared to those from other prototype generation algorithms.

Work-related stress is an uncompromising burden with compounding effects on individuals, communities, organizations, and the economy. Highly automated digital environments, such as smart factories and cyber-physical ecosystems, are significantly impacted by these negative effects due to the inherent constraints on human engagement and social interaction. Verbal communication between human operators is a critical resource in such environments, as it can be effectively utilized to address this challenge. A mix of statistical and artificial intelligence (AI) techniques have been reported in recent literature for the detection of stress-related indicators from audio recordings. However, none of these studies has focused on the challenges of detecting work-related stress in cyber-physical environments, such as smart factories, where verbal communication is not only constrained but also impaired by background noise and other disturbances common to factory settings. In this paper, we address these challenges by proposing a novel deep learning approach, based on the workings of the Convolutional Neural Network (CNN) and Growing Self-Organizing Map (GSOM) algorithms. In addition to this novel AI approach, sampling strategy and speaker diarization techniques are utilised for noise reduction. Pitch and speech augmentation techniques address the data imbalance issue common in most real-world datasets. The accuracy and effectiveness of the proposed approach is demonstrated using a benchmark dataset (DAIC-WOZ). We report F1 scores of 82% and 64% for normal and distressed classes respectively, which outperform the state-of-the-art models. We conclude with a discussion on the empirical evaluation of the proposed approach in cyber-physical environments and directions for future work.

Humans have continuously endeavored to enhance their sensory perception and awareness of the physical surroundings for the betterment of themselves as individuals as well as communities. Advancing this notion to the present day, the Internet of Things (IoT) provides a unique opportunity to attempt the same in an increasingly digital landscape. The prevalence and pervasiveness of IoT data streams gives us this ability to represent a situation with clarity and nuance, leading to a refined awareness of the environment. However, there are several challenges in managing the scale, velocity, and magnitude of IoT data streams, as well as the cohesive representation of these varied sources in a single frame of reference. In this article, we present a new algorithm, the deep growing self-organizing map (deep GSOM) algorithm that addresses these challenges. Deep GSOM incrementally generates a latent representation of situational awareness from high entropy to low entropy IoT data streams. It utilizes an implementation of the fuzzy integral to define a metric that can be moved across spatial and temporal situations to profile the density of congestion. We have also expanded deep GSOM into an IoT platform that can collate, aggregate, and process an entire network of IoT data streams. We demonstrate the workings of deep GSOM on the real-life scenario of profiling vehicular and pedestrian movement using IoT data streams of two highly urbanized cities. The results of these experiments confirm the validity and effectiveness of the proposed approach for generating situational awareness from multiple IoT data streams.

The rapid growth in autonomous industrial environments has increased the need for intelligent video surveillance. As a predominant element of video surveillance, recognition of complex human movements is important in a wide range of surveillance applications. However, the current state-of-the-art video surveillance techniques use supervised deep learning pipelines for human activity recognition (HAR). A key shortcoming of such techniques is the inability to learn from unlabeled video streams. To operate effectively in natural environments, video surveillance techniques have to be able to handle huge volumes of unlabeled video data, monitor and generate alerts and insights derived from multiple characteristics such as spatial structure, motion flow, color distribution, etc. Furthermore, most conventional learning systems lack memory persistence capability which can reduce the influence of outdated information in memory-guided decision-making resulting in limiting plasticity and overfitting based on specific past events. In this article, we propose a new adaptation of the Growing Self-Organizing Map (GSOM) to address these shortcomings by 1) adopting two proven concepts of traditional deep learning, hierarchical, and multistream learning, applied into GSOM self-structuring architecture to accommodate learning from unlabeled video data and their diverse characteristics, 2) address overfitting and the influence of outdated information on neural architecture by implementing a transience property in the algorithm. We demonstrate the proposed model using three benchmark video datasets and the results confirm its validity and usability for HAR.

Rapid developments in urbanization and autonomous industrial environments have augmented and expedited the need for intelligent real-time video surveillance. Recent developments in artificial intelligence for anomaly detection in video surveillance only address some of the challenges, largely overlooking the evolving nature of anomalous behaviors over time. Tightly coupled dependence on a known normality training dataset and sparse evaluation based on reconstruction error are further limitations. In this article, we propose the incremental spatiotemporal learner (ISTL) to address challenges and limitations of anomaly detection and localization for real-time video surveillance. ISTL is an unsupervised deep-learning approach that utilizes active learning with fuzzy aggregation, to continuously update and distinguish between new anomalies and normality that evolve over time. ISTL is demonstrated and evaluated on accuracy, robustness, computational overhead as well as contextual indicators, using three benchmark datasets. Results of these experiments validate our contribution and confirm its suitability for real-time video surveillance.

The Self-Organizing Map algorithm has been proven and demonstrated to be a useful paradigm for unsupervised machine learning of two-dimensional projections of multidimensional data. The tri-state Self-Organizing Maps have been proposed as an accelerated resource-efficient alternative to the Self-Organizing Maps for implementation on field-programmable gate array (FPGA) hardware. This paper presents a generalization of the tri-state Self-Organizing Maps. The proposed generalization, which we call integer Self-Organizing Maps, requires only integer operations for weight updates. The presented experiments demonstrated that the integer Self-Organizing Maps achieve better accuracy in a classification task when compared to the original tri-state Self-Organizing Maps.

Industrial applications are increasingly adopting conversational agents (chatbots) for tasks ranging from primitive conversation interfaces to intelligent human assistants. In this emerging field of research, there has been a strong drive towards modelling human-like characteristics and behaviors in chatbots. However, only a limited number of research endeavors have focused on a chatbot for automatic characterization of end-user emotions. In order to address this limitation, we propose an artificial intelligence based cognitive model for emotion awareness in chatbots using Markov chains, word embedding, and Natural Language Processing. The proposed model is able to extract emotions from conversations, detect emotion transitions over time, predict real-time emotions and intelligently profile human participants based on their distinct emotional characteristics. We conducted experiments using a real-world end-user dataset to demonstrate the functionality of the proposed model. Results from experiments confirm the plausibility of this model for emotion awareness in industrial conversational agents.

Recognition of complex human activities is a prominent area of research in intelligent video surveillance. The current state-of-the-art techniques are largely based on supervised deep learning algorithms. The inability to learn from unlabeled video streams is a key shortcoming in supervised techniques in most current applications where large volumes of unlabeled video data are utilized. Furthermore, the dominant focus on persistence in traditional machine learning algorithms has induced two limitations; the influence of outdated information in memory- guided decision making, and overfitting of acquired knowledge on specific past events, weakening the plasticity of the learning system. To address the above requirements, we propose a new adaptation of the Growing Self Organizing Map (GSOM), formed in a hierarchical two-stream learning pipeline to accommodate unlabeled video data for human activity recognition, which facilitates plasticity by implementing a transience property, without losing the stability of the learning system. The proposed model is evaluated using two benchmark video datasets, confirming its validity and usability for human activity recognition.

The fusion of sensor data is an essential requirement for active perception in autonomous robots in order to accurately perceive an environment, make appropriate adjustments that improve the understanding of the environment and generate actionable insights. Despite a large body of literature in this domain, only a handful of research focuses on unsupervised machine learning, which is increasingly important in unlabeled, unknown environments. This paper proposes a scalable self-organizing neural architecture for environmental perception based on multimodal fusion using unsupervised machine learning. Inspired by the biological counterpart, the neural architecture consists of topographic maps across each modality and incorporates a novel scalable self-organization process that handles high volume, high-velocity sensor data. The co-occurrence relationships across modalities are captured in cross-modal connections and the reverse Hebbian projection is used for multimodal representation. This proposed unsupervised machine learning-based scalable fusion technique was implemented on Apache Hadoop and Apache Spark. It was validated using an extensive multimodal human activity sensor dataset to demonstrate efficient representation and multimodal fusion for active perception.

The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, high-frequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.

This study considers a resource constrained job scheduling problem. Jobs need to be scheduled on different machines satisfying a due time. If delayed, the jobs incur a penalty which is measured as a weighted tardiness. Furthermore, the jobs use up some proportion of an available resource and hence there are limits on multiple jobs executing at the same time. Due to complex constraints and a large number of decision variables, the existing solution methods, based on meta-heuristics and mathematical programming, are very time-consuming and mainly suitable for small-scale problem instances. We investigate a genetic programming approach to automatically design reusable scheduling heuristics for this problem. A new representation and evaluation mechanisms are developed to provide the evolved heuristics with the ability to effectively construct and refine schedules. The experiments show that the proposed approach is more efficient than other genetic programming algorithms previously developed for evolving scheduling heuristics. In addition, we find that the obtained heuristics can be effectively reused to solve unseen and large-scale instances and often find higher quality solutions compared to algorithms already known in the literature in significantly reduced time-frames.

Multimodal sensory fusion is a fundamental requirement for autonomous robots to form an unambiguous and meaningful representation of their surroundings. In this paper, we propose a multisensory self-organizing neural architecture for multimodal fusion using unsupervised machine learning. Inspired by biological evidence from the organization of the human sensory system, the proposed architecture consists of self-organizing neural layers for learning individual modalities. We have incorporated scalable computing for self-organization, so the processing can be scaled to support large datasets and short computation times. The lateral associative connections capture the co-occurrence relationships across individual modalities for cross-modal fusion in obtaining a multimodal representation. Experiments are conducted on an audio-visual dataset consisting of utterances to evaluate the quality of multimodal fusion over individual unimodal representations. Multimodal representation achieves significant improvements over the unimodal representations. These results indicate the proposed architecture is capable of forming effective multimodal representations in short computation times from congruent multisensory stimuli.

Driver behavior recognition is an essential determinant for effective coordination and communication between autonomous and human-driven vehicles. Effective coordination will ensure traffic flow optimization, collision avoidance and hazard detection. Humans exhibit a diverse array of driving behaviors and respond differently to task demands and capabilities of each driving situation. These behaviors can be captured from driving data generated by the vehicle, such as acceleration, brake position, steering wheel position, and transformed into driver behavior characterizations using unsupervised incremental machine learning. In this paper, we extend the Driver Demands and Capabilities Model for intelligent change detection. This model is implemented as an unsupervised incremental machine learning algorithm for behavior change detection, and differentiation between abrupt behavior change and repeating behavior change. Experiments were conducted using the first openly available dataset of annotated DAVIS driving recordings accompanying driving data. Results demonstrate and confirm the capabilities of the proposed model and algorithm for detecting driver behavior change, distinguishing between abrupt and repeat behavior changes. Detected changes can be communicated to all vehicles within the immediate vicinity for effective coordination and improved situational understanding.

The traditional approach to health care is being revolutionized by the rapid adoption of patient‐centered healthcare models. The successful transformation of patients from passive recipients to active participants is largely attributed to increased access to healthcare information. Online support groups present a platform to seek and exchange information in an inclusive environment. As the volume of text on online support groups continues to grow exponentially, it is imperative to improve the quality of retrieved information in terms of relevance, reliability, and usefulness. We present a text‐mining approach that generates a knowledge extraction layer to address this void in personalized information retrieval from online support groups. The knowledge extraction layer encapsulates an ensemble of text‐mining techniques with a domain ontology to interpose an investigable and extensible structure on hitherto unstructured text. This structure is not limited to personalized information retrieval for patients, as it also imparts aggregates for crowdsourcing analytics by healthcare researchers. The proposed approach was successfully trialed on an active online support group consisting of 800,000 posts by 72,066 participants. Demonstrations for both patient and researcher use cases accentuate the value of the proposed approach to unlock a broad spectrum of personalized and aggregate knowledge concealed within crowdsourced content.

The proliferation of Internets of Things (IoT) technologies in both industrial and non-industrial settings has led to the accumulation of Big Data sets. Analysis of these high-volume, high-velocity datasets require advanced processing techniques that incorporate parallel and distributed computations. In this paper, we present a novel distributed self-adaptive neural-network algorithm, the Distributed Growing Self-Organizing Map (DGSOM) algorithm to address the growing need for unsupervised machine learning of Big Data sets on distributed computing environments. The algorithm was tested on a Big Data set of sensor recordings of human activity collected from wearable devices, 2.8 million records. Results indicate that the distributed algorithm significantly reduces execution time compared to its serial counterpart. Moreover, the self-adaptive nature and controlled growth of the algorithm demonstrates data-driven structure adaptation and multi-granular pattern analysis. Overall, the proposed algorithm addresses the need for pattern discovery and visualization from Big Data sets generated by IoT devices which are increasingly commonplace in industrial scenarios.

IoT systems deployed in industrial and smart factory settings generate large volumes of data at high velocity. Context awareness is mandatory for knowledge discovery and actionable insights from such high-velocity, high-volume IoT data streams. Changes to the context of a data stream are represented in the underlying data distribution. Research in concept drift aims to detect and adapt to such changes in a data distribution. Concept drift detection can be extended to suit ad hoc Big Data streams generated by IoT systems, by introducing the cognitive principles of learning. This paper proposes an unsupervised incremental learning algorithm for detection and adaption of concept drift based on the cognitive principles of learning. It executes in automated time windows, detects concept drift using movement in space and determines the type of concept drift using movement in time. The algorithm was applied to a Big Data set representing an IoT system for urban vehicular movement and traffic. Results confirm that the proposed algorithm generates context-awareness by detection and adaptation to concept drift in high-volume, high-velocity IoT systems.

The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action recognition and event detection. A key shortcoming of such techniques is the inability to learn from unlabeled video streams. The entire video stream is unlabeled when the requirement is to detect irregular, unforeseen and abnormal behaviors, anomalies. Recent developments in intelligent high-level video analysis have been successful in identifying individual elements in a video frame. However, the detection of anomalies in an entire video feed requires incremental and unsupervised machine learning. This paper presents a novel approach that incorporates high-level video analysis outcomes with incremental knowledge acquisition and self-learning for autonomous video surveillance. The proposed approach is capable of detecting changes that occur over time and separating irregularities from re-occurrences, without the prerequisite of a labeled dataset. We demonstrate the proposed approach using a benchmark video dataset and the results confirm its validity and usability for autonomous video surveillance.

The global popularity of microblogs has led to an increasing accumulation of large volumes of text data on microblogging platforms such as Twitter. These corpora are untapped resources to understand social expressions on diverse subjects. Microblog analysis aims to unlock the value of such expressions by discovering insights and events of significance hidden among swathes of text. Besides velocity; diversity of content, brevity, absence of structure and time‐sensitivity are key challenges in microblog analysis. In this paper, we propose an unsupervised incremental machine learning and event detection technique to address these challenges. The proposed technique separates a microblog discussion into topics to address the key problem of diversity. It maintains a record of the evolution of each topic over time. Brevity, time‐sensitivity and unstructured nature are addressed by these individual topic pathways which contribute to generate a temporal, topic‐driven structure of a microblog discussion. The proposed event detection method continuously monitors these topic pathways using multiple domain‐independent event indicators for events of significance. The autonomous nature of topic separation, topic pathway generation, new topic identification and event detection, appropriates the proposed technique for extensive applications in microblog analysis. We demonstrate these capabilities on tweets containing #microsoft and tweets containing #obama.

As the world advances into the information age, the proliferating demand for energy entails an increasing need for effective smart energy management systems. The resulting smart grids and smart energy management solutions are beginning to generate Big Data; high-variety data at cumulative volumes and velocity. Simultaneously, data analytics techniques and methodologies are being introduced to comprehend this changing nature of data. A number of conventional data mining techniques have successfully transitioned into the Big Data landscape. However, integration of multi-source information in this landscape remains hitherto unaddressed. In this paper, we present a novel data fusion technique that incrementally integrates information from multiple sources. Based on an incremental, unsupervised learning algorithm and possibilistic fusion, the technique overcomes limitations to continuous learning and integrating information of mixed granularity. The technique is also extensible into the Big Data landscape. The collective effect of these features postulate smart energy management as a fitting application domain for the proposed technique. We demonstrate practical applicability of the technique using a household energy and utility consumption dataset. Results and analytics outcomes from these experiments confirm its effectiveness as a data fusion technique and its extensibility into further high-volume applications in smart energy management.

Growing volumes of text and increasing expectations on the complexity of analysis entail advanced approaches to text mining. Unsupervised text clustering is an efficient approach to determine structural groupings in a text corpus without the impact of external bias. The information content of such structural groupings needs to be enhanced by integrating semantics into the cluster outcomes. This integration can eventuate at different stages of the clustering process where semantic relatedness measures can be used for the integration. In this paper we propose a novel method of semantics integration to cluster separation in a dynamic self-organizing map algorithm. We demonstrate the effectiveness of the proposed method and the value of semantics integration for cluster separation with empirical results from two benchmark text datasets.

Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.

Humans learn in an incremental manner. Due to this reason, humans continuously refine their knowledge of the environment with the experience gained. Many strides have been made in the machine learning area to exploit the power of incremental learning. Incremental learning, in contrast to onetime learning is far more useful and effective when data is not completely available at once. Here, we investigate an unsupervised incremental learning algorithm known as Incremental Knowledge Acquisition and Self Learning (IKASL) algorithm. IKASL algorithm is able to capture knowledge in an incremental manner, without disrupting past knowledge. Furthermore, IKASL algorithm encodes acquired knowledge in such a way that it can be used to acquire new knowledge more efficiently. This paper discusses several limitations of original IKASL algorithm and proposes several extensions to the original algorithm which enhances its performance. These modifications include influencing spread factor, implementing fuzzy integral based generalizing technique, etc. Furthermore, the paper report observations of several experiments conducted with several datasets to assess the necessity and value of incremental learning in the real world. These experiments are carefully designed to reflect interesting characteristics of the IKASL algorithm.

The Adaptive Suffix Trie algorithm was previously proposed by the authors as a sequence learning algorithm for capturing frequent sub sequences of variable length. This algorithm builds up a suffix trie data structure, capturing repetitive patterns in a given set of sequences. Its application has been demonstrated in bioinformatics and text clustering. Suffix trees are the space efficient variant of suffix tries and are thus more widely used in the current literature. In this paper we propose the Adaptive Suffix Tree algorithm, which is based on the same learning principles as the Adaptive Suffix Trie, but has the advantage that it is more space efficient. We discuss the new algorithm in detail and demonstrate that the same set of sub sequences can be learnt by the proposed algorithm while utilizing less than 50% of the space used by its predecessor.

Machine learning techniques which are involved in knowledge extraction from stationary datasets have been becoming inefficient due to the dynamic nature of contemporary data spaces. Hence, machine learning research constantly investigates incremental learning techniques to address this requirement. However, it is always a challenge to uncover useful information incrementally from a non-stationary input space because of the complexity an algorithm introduces to counter the stability-plasticity dilemma. In order to facilitate this demand a learning model is proposed using the self-organization and competitive learning strategy. Moreover, an algorithm which is implemented based on the proposed model is also presented with the experimental results to prove the validity of the proposed learning model in a non-stationary context.

Self Organizing Map (SOM) and Growing Self Organizing Map (GSOM) are widely used techniques for text mining. Mining large text data sets is significantly processor intensive [1]. Recently Fast Growing Self Organizing Map (FastGSOM) was proposed an improvement to the GSOM for clustering text data more efficiently [2]. For text corpuses with thousands of documents, the time requirement could still be a bottleneck with high turnaround times for the analysis process. We propose a new scalable parallel algorithm for text analysis using FastGSOM which can harness the power of parallel and distributed computing for efficient analysis of large scale text datasets. We demonstrate that the proposed algorithm has similar or better accuracy compared to GSOM and is several orders more efficient when operating in parallel.

Self-organising maps are widely used for exploratory data analysis. High processing power requirement for large scale data clustering is a key problem with self-organising maps. Although a number of serial approaches have been developed to reduce the time requirement, algorithms that could utilise distributed computing outperforms serial algorithms for processing large datasets. An effective distributed approach is to divide the dataset into partitions, train a self-organising map on each partition and merge the maps to form a single map representing the whole data set. The recently proposed Parallel GSOM algorithm has demonstrated that parallel computation can significantly reduce training time for self-organising maps. However, if the actual clusters in the dataset are distributed across several partitions, the individual trained maps could contain redundant neurons. Presence of redundancy increases the time requirement for the merging process. Reduction of redundant neurons would reduce the time consumption of the merging process thereby improving the efficiency of the whole data clustering process. In this paper, we propose a redundant neuron reduction algorithm for self-organising maps which improves the efficiency of the merging process. We demonstrate that the proposed algorithm has faster performance over the Parallel GSOM algorithm.

Proper maintenance of sewer pipes is vital for the healthy functioning of a city. Due to the difficulty of reach for sewage pipes, automating pipe inspection has high potential in providing an efficient and objective identification of defects which could lead to damaging the pipe system. A popular approach has been to send remote controlled robots to photograph the pipes and process the images to identify possible defects. However majority of the images contain regular pipe features such as the flow line, pipe joints and pipe connections. Regular features pose a challenge for automated defect detection algorithms which require high processing time. This paper proposes a self organising map based approach to leverage the regularity of image features to isolate regions of interest which could contain defects. As a result, the search space is narrowed down for the defect detection algorithms, decreasing the overall processing time. Novelty of the work lies in the feature extraction and the gradual isolation of the potential defective image features to a manageable size. Therefore, this technique is suitable for large scale real applications. We demonstrate the effectiveness of the proposed approach for a real pipe image data set.

Sequences occur naturally in many domains such as biology, engineering, finance and scientific research. Since humans have the inherent ability to comprehend and utilize sequences in day to day cognitive tasks such as speech, vision and motor control; biologically inspired sequence learning techniques are used for explanatory data analysis in these domains. Identifying the common substrings which exist in sequences helps in determining the underlying structure and calculating the similarity between sequences. The suffix trie, suffix tree and suffix array are data structures which are used in many solutions to sequence based problems. However, these are static data structures and not flexible tools which can be used for sequence learning. In this paper we present the Adaptive Suffix Trie algorithm, a sequence learning algorithm which can be used for identifying substrings of different lengths and frequencies from a given set of sequences. In contrast to suffix data structures which store all suffixes, the adaptive suffix trie only captures the frequent substrings that occur in the given dataset, resulting in a less complex structure with only the relevant or useful information. We show how the algorithms' learning parameters can be adapted for extracting substrings with the required characteristics and then demonstrate it's application in the classification of biological sequences.

Text clustering can be considered as a four step process consisting of feature extraction, text representation, document clustering and cluster interpretation. Most text clustering models consider text as an unordered collection of words. However the semantics of text would be better captured if word sequences are taken into account. In this paper we propose a sequence based text clustering model where four novel sequence based components are introduced in each of the four steps in the text clustering process. Experiments conducted on the Reuters dataset and Sydney Morning Herald (SMH) news archives demonstrate the advantage of the proposed sequence based model, in terms of capturing context with semantics, accuracy and speed, compared to clustering of documents based on single words and n-gram based models.

A significant problem which arises during the process of knowledge discovery is dealing with data which have temporal dependencies. The attributes associated with temporal data need to be processed differently from non temporal attributes. A typical approach to address this issue is to view temporal data as an ordered sequence of events. In this work, we propose a novel dynamic unsupervised learning approach to discover patterns in temporal data. The new technique is based on the Growing Self-Organization Map (GSOM), which is a structure adapting version of the Self-Organizing Map (SOM). The SOM is widely used in knowledge discovery applications due to its unsupervised learning nature, ease of use and visualization capabilities. The GSOM further enhances the SOM with faster processing, more representative cluster formation and the ability to control map spread. This paper describes a significant extension to the GSOM enabling it to be used to for analyzing data with temporal sequences. The similarity between two time dependent sequences with unequal length is estimated using the Dynamic Time Warping (DTW) algorithm incorporated into the GSOM. Experiments were carried out to evaluate the performance and the validity of the proposed approach using an audio-visual data set. The results demonstrate that the novel “GSOM Sequence” algorithm improves the accuracy and validity of the clusters obtained.

The human brain processes information in both unimodal and multimodal fashion where information is progressively captured, accumulated, abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has produced various sources of electronic data and continues to do so exponentially. Finding patterns from such multi-source and multimodal data could be compared to the multimodal and multidimensional information processing in the human brain. Therefore, such brain functionality could be taken as an inspiration to develop a methodology for exploring multimodal and multi-source electronic data and further identifying multi-view patterns. In this paper, we first propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. Secondly, we present a cluster driven approach for the implementation of the proposed brain inspired model. Particularly, the Growing Self Organising Maps (GSOM) based cross-clustering approach is discussed. Furthermore, the acquisition of multi-view patterns with clusters driven implementation is demonstrated with experimental results.

This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

Incremental learning is a core necessity in developments towards intelligent machines. Artificial learning as implemented in contemporary neural network algorithms does not fully encompass an incremental, autonomous learning capacity. In this paper we present a self learning algorithm capable of incrementally acquiring knowledge across learning periods. A dynamic unsupervised learning algorithm, the GSOM algorithm, forms the basis of the presented incrementally knowledge acquiring self learning (IKASL) algorithm, to which we have introduced a layer of aggregation for continuous learning, knowledge acquisition and retention. We also present a novel application of the IKASL algorithm for continuous learning of hidden patterns from semantics of text.

Complex business application systems that involve non trivial decision making can have highly unpredictable situations. In such situation adaptive and intelligent behaviors would able to mitigate the risk in business. Vessel berthing application in container terminals is regarded as a very complex dynamic application, which requires autonomous decision making capabilities to improve the productivity of the berths. On the other hand, BDI agent systems have been implemented in many applications and found some limitations in learning. We propose a new enhanced hybrid BDI model with ANFIS and reinforcement learning methods to over come the above limitation. Our paper discusses how the commitment strategy of agent's desire, intentions and plans could be enhanced with intelligent learning capabilities. A new motivation based distance calculation method supported with ANFIS and reinforcement learning is proposed in the paper, which improve the reactive, proactive and intelligent behaviors of generic BDI agents in complex applications.

Text mining has emerged as a different stream in data mining because of the unstructured nature associated with free text. Many algorithms have been developed to assist in text mining. This paper presents the use of text mining based on a novel high dimensional clustering algorithm that leads to the exploratory data mining on data associated with the text. Experimental results of analyzing a real-world text data set and associated data are also presented.

Mining of text data from the Web has become a necessity in modern days due to the volumes of data available on the Web. While searching for information on the Web using search engines is popular, to analyze the content on large collections of Web pages, feature map techniques are still popular. One of the problems associated with processing large collections of text data from the Web using feature map techniques is the time taken to cluster them. This paper presents an algorithm based on a growing variant of the self organizing map called the HDGSOMr. This novel algorithm incorporates randomness into the self-organizing process to produce higher quality clusters within few epochs and utilizing smaller neighborhood sizes resulting in a significant reduction in overall processing time. Details of the HDGSOMr algorithm and results of processing large collections of text data proving the efficiency of the algorithm are also presented.

Berth scheduling and monitoring of the vessel operations are of paramount importance in order to assure faster turnaround time and high productivity of any container terminal. The need for an intelligent system that dynamically adapts to the changing environment is apparent, as there are a limited number of berths and resources available in container terminals for delivering services to vessels. We discuss how BDI (Beliefs, Desires and Intentions) agents can be supported with Neural Network and fuzzy logic in a collaborative environment of a multi agents system for the scheduling and monitoring of vessel berths in container ports. Straightforward plans are handled by the generic BDI architecture. Complex planning which requires the learning and adaptability behavior is modeled with neural networks. Beliefs with fuzzy scenarios are modeled with fuzzy logic enabling agents to make rational decisions in the environment of uncertainty. Agents can autonomously adapt to the changing environment in assigning berths for vessels.

The growing self organizing map (GSOM) algorithm is a variant of the self organizing map (SOM). It has a dynamically growing structure that adapts to the natural structure of the data. It has been identified that the growing of the GSOM can get negatively affected when used with very large dimensional data such as those in text and DNA data sets. This paper addresses these issues and presents a modified version of the GSOM called the high dimensional GSOM (HDGSOM). The algorithm and experimental results showing the improved performance of the HDGSOM are also presented.

Model integration is one of the most important and widely researched areas in model management of decision support systems (DSS). In disaster management area, independent DSS models handle specific decision-making needs, but it is possible that the need for combination of these models will be required. Therefore, the need for model integration and selection of such models arises. This paper presents the idea of decision support model integration based on software agents in an interactive disaster management domain. In this environment an automated model agent communicates with the other decision support system models and presents the hybrid decision support system model as a solution. This system starts with minimal information about the user's preferences, and preferences are elicited and inferred incrementally by analyzing the needs and requirements of the user.

Strategic planning and dynamism in decision making are essential factors in a vessel berthing application in any container port to assure faster turnaround time and high productivity. BDI agents have been used in many applications with limited capabilities. We propose a new hybrid BDI architecture with learning capacities overcoming some limitations exists in the generic BDI agent model. A new "knowledge acquisition model" (KAM) module is proposed with a supervised neural network and adaptive neuro fuzzy inference system (ANFIS) in the intention reconsideration process of the agent model. Commitment strategy of the new intention reconsideration process is based on the motivation of the state transitions and the effect of belief changes in the environment.

Intelligent agent technology, which expects to combine the marked trends in history of computing such as ubiquity, interconnection, intelligence, delegation and human orientation can be considered as a step towards the next stage of artificial intelligence. This new technology attempts to reduce the gap between man and machine. The remarkable ability of a human being to make decisions is art ongoing learning and evolutionary process. Therefore, when reducing the man-machine gap, one of the main issues to address is how to make agents decision makers in a human oriented way. The paper presents novel conceptual agent framework to provide human like decisions inspired by human behavior and brain study models. The proposed learning and evolutionary agent architecture make the agent capable of handling the dynamism in the environment too. The experiments illustrated with the banking application demonstrate how the proposed framework enables a software agent to make decisions in a human oriented manner.

The paper describes a new multiagent system with enhanced capabilities obtained through a hybrid of intelligent techniques. The processing in the model is handled by two types of agents: distributed agents and a central administrator agent. Localized processing at the individual agents is carried out using mathematical techniques and genetic algorithms. The central administrator agent dynamically obtains information about the problem domain from the Internet and maintains a knowledge pool using a clustering technique called the growing self‐organizing map (GSOM). Distributed agents communicate with the central administrator agent if they need further knowledge about the problem domain to provide solutions to user‐defined tasks. The approach integrates traditional mathematical, data mining, and evolutionary techniques with a multiagent system. The proposed system is implemented as a travel optimizer application for the e‐tourism domain. Finally, the possibilities of integrating the proposed technique with currently available e‐tourism applications to provide the customer with enhanced solutions are identified.

The growing self-organizing map (GSOM) has been presented as an extended version of the self-organizing map (SOM), which has significant advantages for knowledge discovery applications. In this paper, the GSOM algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and as such continue with finer clustering of only the interesting clusters. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set. Further analysis is conducted on selected sections of the data and as such of smaller volume. Therefore, this method facilitates the analysis of even very large data sets.

Supervised and unsupervised self generating neural network architectures have been used in the recent past. Our previous work (1998) has described an unsupervised self generating feature map, called the growing self organising map (GSOM). In this paper we describe some extensions to the GSOM such that it could be used to map and analyse more realistic data sets.

We describe a data analysis method using a structure adapting neural network with two additional layers. The neural network used is an extended version of a self-organising feature map which can adapt its structure to better represent the clusters in data. Once the clusters are identified, we use two additional layers on the feature map to analyse the clusters and the representation of attributes in the clusters. Simulations and initial results with two simple benchmark data sets are also described.