Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

This article describes our research in technologies for the management and control of distributed energy resources. An agent-based management and control system is being developed to enable large-scale deployment of distributed energy resources. Local intelligent agents will allow consumers who are connected at low levels in the distribution network to manage their energy requirements and participate in coordination responses to network stimuli. Such responses can be used to reduce the volatility of wholesale electricity prices and assist constrained networks during summer and winter demand peaks. In our system, the coordination of energy resources is decentralized. Energy resources coordinate each other to realize efficient autonomous matching of supply and demand in large power distribution networks. The information exchange is through indirect (or stigmergic) communications between agents. The coordination mechanism is asynchronous and adapts to change in an unsupervised manner, making it intrinsically scalable and robust.

Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

Alzheimer’s disease (AD) affects over 50 million people worldwide, making early and accurate diagnosis essential for effective treatment and care planning. Diagnosing AD through neuroimaging continues to face challenges, including reliance on subjective clinical evaluations, the need for manual feature extraction, and limited generalisability across diverse populations. Recent advances in deep learning, especially convolutional neural networks (CNNs) and vision transformers, have improved diagnostic performance, but many models still depend on large labelled datasets and high computational resources. This study introduces an attention-enhanced CNN with a multi-activation fusion (MAF) module and evaluates it using the Alzheimer’s Disease Neuroimaging Initiative dataset. The channel attention mechanism helps the model focus on the most important brain regions in 3D MRI scans, while the MAF module, inspired by multi-head attention, uses parallel fully connected layers with different activation functions to capture varied and complementary feature patterns. This design improves feature representation and increases robustness across heterogeneous patient groups. The proposed model achieved 92.1% accuracy and 0.99 AUC, with precision, recall, and F1-scores of 91.3%, 89.3%, and 92%, respectively. Ten-fold cross-validation confirmed its reliability, showing consistent performance with 91.23% accuracy, 0.93 AUC, 90.29% precision, and 88.30% recall. Comparative analysis also shows that the model outperforms several state-of-the-art deep learning approaches for AD classification. Overall, these findings highlight the potential of combining attention mechanisms with multi-activation modules to improve automated AD diagnosis and enhance diagnostic reliability.

In this paper, we present an event-on-demand (EoD)video adaptation system. The proposed system supports users in deciding their events of interest and considers network conditions to adapt video source by event selection and frame dropping.Firstly, events are detected by audio/video analysis and annotated by the description schemes (DSs)provided by MPEG-7 Multimedia Description Schemes (MDSs). And then, to achieve a generic adaptation solution, the adaptation is developed following MPEG-21 Digital Item Adaptation (DIA)framework. We look at early release of the MPEG-21 Reference Software on XML generation and develop our own system for EoD video adaptation in three steps:1) the event information is parsed from MPEG-7 annotation XML file together with bitstream to generate generic Bitstream Syntax Description (gBSD). 2) Users' preference, Network Characteristic and Adaptation QoS (AQoS) are considered for making adaptation decision. 3) adaptation engine automatically parses adaptation decisions and gBSD to achieve adaptation.Unlike most existing adaptation work, the system adapts video of events with interest according to users' preference. Implementation following MPEG-7 and MPEG-21 standards provides a generic video adaptation solution. gBSD based adaptation avoids complex video computation. 30 students from various departments were invited to test the system and their responses has been positive.

Management of a very large number of distributed energy resources, energy loads, and generators, is a hot research topic. Such energy demand management techniques enable appliances to control and defer their electricity consumption when price soars and can be used to cope with the unpredictability of the energy market or provide response when supply is strained by demand. We consider a multi-agent system comprising multiple energy loads, each with a dedicated controller. This paper introduces our latest research in self-organization of coordinated behavior of multiple agents. Energy resource agents (RAs) coordinate with each other to achieve a balance between the overall consumption by the multi-agent collective and the stress on the community. In order to reduce the overall communication load while permitting efficient coordinated responses, information exchange is through indirect communications between RAs and a broker agent (BA). This gives a decentralized coordination approach that does not rely on intensive computation by a central processor. The algorithm presented here can coordinate different types of loads by controlling their set-points. The coordination strategy is optimized by a genetic algorithm (GA) and a fast coordination convergence has been achieved.