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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Callea, F. Martinez-Perurena, A. Zarketa-Astigarraga, A. Penalba, M. Cervelli, G. Giorgi, G. Robertson, B. Iglesias, G. Fluid Mechanics Department Mondragon University Gipuzkoa Arrasate Spain Ikerbasque - Basque Foundation for Science Bizkaia Bilbao Spain Politecnico di Torino Torino Italy Civil and Construction Engineering Oregon State University CorvallisOR United States UCC MaREI Environmental Research Institute and School of Engineering Cork Ireland School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9781032905570

The paper presents a preliminary study limited to the use of wave height for the data corresponding to the Gulf of Biscay, a location with a very dominant North-West wave rose. It benchmarks different bias correction (BC) techniques and evaluates their performance, as well as the sensitivity of the BC to the reference dataset employed for the identification of the BC parameters. More precisely, the amount of data (number of years) and the selected period (exact years) are analysed. Overall, the results demonstrate that the Gumbel-based BC techniques overperform the linearly-spaced BC techniques, the directional-adjusted Gumbel Quantile Mapping technique showing the lowest bias. With respect to the sensitivity of the reference data, BC seems to provide satisfactory results even when only 1 year of data are used. However, the dispersion among the different selected periods is large, resulting in large uncertainties. This dispersion reduces significantly once 3 or more years of data are used, independently of the selected period. © 2025 The Author(s).

关键词： Dispersion (waves)

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Real-Time Human Activity Recognition Using Non-intrusive Sensing and Continual Learning 37th

Real-Time Human Activity Recognition Using Non-intrusive Sen...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Rahman, Md Geaur ur Rehman, Sabih Fealy, Shanna Vallejo, Johan Sebastian Ramirez Fuskelay, Aayush Moni, Mohammad Ali School of Computing Mathematics and Engineering Charles Sturt University Port MacquarieNSW2444 Australia School of Nursing Paramedicine and Healthcare Sciences Charles Sturt University Port MacquarieNSW2444 Australia Artificial Intelligence and Cyber Futures Institute Charles Sturt University OrangeNSW2800 Australia

ISBN: (纸本)9789819603503

With a rapid increase in the ageing population across the globe, there is an urgent need for the development of affordable and sustainable solutions to provide aged care support services. Recent advancements in sensor technologies coupled with the use of artificial intelligence (AI) make it possible to monitor and classify the activities of daily living (ADL) of residents in aged care settings, making it easier to detect and predict any potential health problems. The development of such an architecture, however, presents two key challenges: (i) the determination of appropriate sensors and (ii) the selection of suitable AI approaches to recognise individual activities. While existing studies often only focus on addressing one challenge at a time, in this paper, we present the design and implementation of a real-time human activity recognition system called HARNIC, which uses not only non-intrusive sensors but also utilises continual learning to classify individual activities in a simulated environment. We conducted a thorough analysis of current non-intrusive sensors and subsequently selected appropriate sensors for real-time activity monitoring by considering several features such as adjustable sensitivity, detection range, trigger modes, processing power and accuracy. Using the sensors, we designed and simulated a smart aged care environment in a laboratory setting and collected ADL data. This data is categorised into three levels i.e., low, medium, and high, based on the type of activity. We then worked on generating a benchmark data set used to build machine learning models and performed testing of our models. To address the second challenge, we considered incremental and non-incremental methods and evaluated their effectiveness in recognising individual activities in real-time. Our initial experiment results indicate a clear superiority of our HARNIC over the existing state-of-the-art methods used in this study. © The Author(s), under exclusive license to Spring

关键词： Adversarial machine learning

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A Survey on Ai Enabled Secure Social Industrial Internet of Things in Agri-Food Supply Chain

SSRN

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SSRN 2025年

作者： Halder, Sajal Mamun, Quazi Mahboubi, Arash Walsh, Patrick Islam, M.D. Zahidul Islam, M.D. Rafiqul School of Computing Mathematics and Engineering Charles Sturt University NSW Australia Data61 CSIRO Melbourne Australia

The integration of Artificial Intelligence (AI) with secure Social Industrial Internet of Things (SIIoT) in the agri-food supply chain represents a significant step forward in tackling the challenges of efficiency, security, and sustainability. AI technologies such as machine learning, predictive analytics, and natural language processing are harnessed to analyze vast datasets generated by interconnected IIoT devices, providing real-time insights and facilitating well-informed decision-making. These insights help in forecasting crop yields, monitoring environmental conditions, optimizing resource usage, and preventing supply chain disruptions. Secure communication protocols and blockchain technologies are implemented to safeguard data privacy and integrity, ensuring that sensitive information is protected from cyber threats. However, there are limited research works that focus the AI, IoT and security issues in each step of smart farming. Most of the existing works focused on related technologies AI, IoT and security and focused on their applications but were not classified step-wise technologies use. In this paper, we demonstrate how these various technologies play crucial roles in enhancing efficiency, transparency, and security in each step of the agri-food industry. We present two real case studies and show how these technologies are involved in each step of the agri-food industry (e.g., smart farming and cold storage supply chain). Furthermore, we present a structured taxonomy that clarifies the interconnectedness of these technologies within the agri-food industry based on recent research works. Finally, we have discussed some challenges and potential future research directions with SIIoT in the agri-food industry. © 2025, The Authors. All rights reserved.

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Self-Guided Virtual Reality Therapy for Anxiety: A Systematic Review

arXiv

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arXiv 2025年

作者： Graham, Winona Drinkwater, Russell Kelson, Joshua Kabir, Muhammad Ashad School of Psychology Charles Sturt University BathurstNSW Australia School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW Australia

Background. Virtual reality (VR) technology can be used to treat anxiety symptoms and disorders. However, most VR interventions for anxiety have been therapist guided rather than self-guided. Objective. This systematic review aimed to examine the effectiveness and user experience (i.e., usability, acceptability, safety, and attrition rates) of self-guided VR therapy interventions in people with any anxiety condition as well as provide future research directions. Method. Peer-reviewed journal articles reporting on self-guided VR interventions for anxiety were sought from the Cochrane Library, IEEE Explore Digital Library, PsycINFO, PubMED, Scopus, and Web of Science databases. Study data from the eligible articles were extracted, tabulated, and addressed with a narrative synthesis. Results. A total of 21 articles met the inclusion criteria. The findings revealed that self-guided VR interventions for anxiety can provide an effective treatment of social anxiety disorder, public speaking anxiety, and specific phobias. User experiences outcomes of safety, usability, and acceptability were generally positive and the average attrition rate was low. However, there was a lack of standardised assessments to measure user experiences. Conclusion. Self-guided VR for anxiety can provide an engaging approach for effectively and safely treating common anxiety conditions. Nevertheless, more experimental studies are required to examine their use in underrepresented anxiety populations, their long-term treatment effects beyond 12 months, and compare their effectiveness against other self-help interventions for anxiety (e.g., internet interventions and bibliotherapy). © 2025, CC BY.

关键词： Virtual environments

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Deep Learning Based Driver Drowsiness Detection in Real Time Environment - An Enhanced Approach 4

Deep Learning Based Driver Drowsiness Detection in Real Time...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ajay, Ashin Ashok, Naveen Kumar Balajiram, Gokul Tamizhselvan, Abinesh Thangavel, Senthil Kumar Somasundaram, K. Amrita Vishwa Vidyapeetham M. Tech in Artificial Intelligence Coimbatore India Amrita Vishwa Vidyapeetham B. Tech in Computer Science and Engineering Coimbatore India Amrita School of Computing Coimbatore Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Effective driver behavior monitoring is necessary due to the increase in traffic accidents caused by distracted driving. In order to identify distractions such as talking, drinking, napping, and using a phone, this research suggests a CNN-based methodology. The model uses facial characteristics such as eyes to detect and analyze these activities. Thousands of photos depicting various distracted driving postures are used to train it on a publicly accessible dataset. The strategy seeks to improve road safety by offering a reliable way to identify distractions. © 2025 IEEE.

关键词： Convolutional neural networks

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SMAR + NIE IdeaGen: A knowledge graph based node importance estimation with analogical reasoning on large language model for idea generation

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Expert Systems with Applications 2025年 279卷

作者： Oyelade, Olaide N. Wang, Hui Rafferty, Karen School of Electronics Electrical Engineering and Computer Science Queen's University Belfast BT7 1NN United Kingdom School of Engineering Computing and Mathematics University of Chichester United Kingdom

Idea generation describes a creative process involving reasoning over some knowledge to derive new information. Traditional approaches such as mind-map and brainstorming are limited and often fail due to lack of quality ideas and ineffective methods. The reasoning capability of large language models (LLMs) have been investigated for ideation tasks and have reported interesting performance. However, these models suffer from limited logical reasoning capability which hinders the use of structural and factual real-world knowledge in discovery of latent insight and predict possible outcome when applied to ideation. In addition, the possibility of LLMs regurgitating knowledge learnt from datasets might adversely impact the degree of novel ideas the models can generate. In this paper, a two-stage logical reasoning approach is applied to initiate the search for candidate idea pathways based on the knowledge graphs (KGs) to address the problem of reasoning, domain-specificity and novelty. The divergence stage this reasoning explores utilizes a new node importance estimation (NIE) technique over KGs to discover latent connections supporting idea generation. In the convergence stage of this reasoning, subgraph matching using analogical reasoning (SMAR) is applied to find matching patterns to describe a new idea. The use of SMAR + NIE and KGs helps to achieve an improvement in reasoning over KGs before transferring such reasoning to LLMs for translation of idea into natural language. To evaluate the degree of novelty of ideas generated, a relevance-to-novelty scoring metrics is proposed based on multiple premise entailment (MPE). We combined this metric with other popular metrics to evaluate the performance of SMAR + NIE on benchmark datasets, and as well on the quality of ideas generated. Findings from the study showed that this approach demonstrates competitive performance with mainstream LLMs in idea generation tasks. © 2025

关键词： Knowledge graph

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An Overview of Security Threats, Attack Detection and Defense for Large-Scale Multi-Agent Systems (LSMAS) in Internet of Things (IoT)

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 70-81页

作者： Cai, Jijing Wen, Long Feng, Hailin Fang, Kai Chen, Junxin Wei, Wei Wang, Wei Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Macau University of Science and Technology School of Innovation Engineering Macau999078 China Dalian University of Technology School of Software Dalian116621 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Shenzhen518172 China

With the global advancement of the Internet of Things (IoT), large-scale multi-agent systems (LSMAS) technology has been increasingly adopted across various industries. Despite the widespread use of IoT, vulnerabilities in its software and hardware components pose significant challenges to ensuring security. To address the security concerns faced by LSMAS, this article explores its application in IoT domains such as smart grids, smart manufacturing, and smart healthcare. By examining the structural layers of IoT-the perception layer, network layer, and application layer-this paper analyzes the specific security threats encountered by each layer, along with the defense strategies designed to counter these attacks. Furthermore, we categorize and compare both traditional and AI-based attack detection methods, dividing the latter into machine learning-based, deep learning-based, and transfer learning-based approaches. This analysis highlights the strengths and weaknesses of the various attack detection methods currently deployed in LSMAS, identifying the existing challenges they face. Finally, we outline potential trends for the future development of LSMAS in IoT, offering research directions for experts in related fields. © 2023 IEEE.

关键词： Multi agent systems

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Solving Stochastic Differential and Integral Equations Using Neural Network Method

Solving Stochastic Differential and Integral Equations Using...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： G. Anitha Priyanka Saraf Soumyendra. Singh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Artificial intelligence and nearly all its subfields include machine learning and deep learning in operations with the closings being a vital aspect across disciplines including solving mathematical problems. The presentation of this work proposes a novel approach to the computation of numerical solutions to ODEs with certain architectures of deep learning models namely single-layer CNN with logistic sigmoid activation. It uses a control variable approach which facilitates manipulation of hyperparameters and the optimizer's choice. The first goal is to develop CNN to solve ODEs with high precision powered by the Adam optimizer and backpropagation method. The extensive experiments include bespoke loss functions that capture ODE features, and analysis of the effects of different structures of neural networks on solutions. Various outcomes, such as the first application of the CNN-based approach, the assessment of adaptations through control variables, and the exploration of the effects exerted by the network structure on ODE solutions. This research find its application in the use of neural networks in solving mathematical problems particularly ODEs; finding a new way of solving them without consuming a lot of time. This paper has offered grounds for improved mathematical problem-solving and established the efficacy of CNNs and counteractive loss functions. The consequences have been carried out to numerous scientific and engineering fields and are the possibility of enhanced calculation accuracy and efficacy of ODEs in numerous practice areas. This paper presents the advantages of using convolutional neural networks (CNNs) to solve differential equations (ODEs), particularly with reference to the use of loss functions to capture important ODE features. This method, which incorporates traditional mathematical methods, reduces computational time by an average of 30% and improves accuracy by an average of 25%. Applications range from dynamic systems modeling in engineer

关键词： Accuracy Partial differential equations Neural networks Differential equations Predictive models Multilayer perceptrons Mathematical models Convolutional neural networks Predictive analytics Programming profession

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Auralize: Text to Tune

Auralize: Text to Tune

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Emerging Smart computing and Informatics (ESCI), Conference on

作者： Swati V Penumarthi Hima Varshini D. Navya Neetu Srivastava Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Mathematics Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The fusion of music and technology has expanded rapidly. AI, machine learning, and signal processing are playing increasingly vital roles in music production, analysis, and generation. This framework introduces a comprehensive framework for automated music processing, including text-to-music generation, genre classification, and noise filtering. This model excels in complex classification tasks, efficiently identifies genre specific features, and achieves a high success rate in genre differentiation. Spectral Subtraction Noise Reduction algorithm refines the audio quality. It ensures that noise is reduced based on its unique characteristics, preserving clarity and maintaining the distinct elements that define various music styles. In conclusion, such an all-integrated system has potential for practical applications in the tasks like music indexing and retrieval or adaptive audio enhancement. Furthermore, this framework provides a basis for further improvements in intelligent music generation and classification.

关键词： Support vector machines Filtering Mood Noise Noise reduction Signal processing algorithms Music Production Multiple signal classification Informatics

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