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18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024

作者： Adamides, Giorgos Panayides, Andreas Loizou, Christos P. Nicolaou, Andria Pantzaris, Marios Pattichis, Constantinos Department of Computer Science University of Cyprus Nicosia Cyprus 3AE HEALTH LTD Nicosia Cyprus CYENS CoE Nicosia Cyprus Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus School of Molecular Medicine The Cyprus Institute of Neurology and Genetics University of Cyprus Nicosia Cyprus

ISBN: (纸本)9783031855719

Multiple Sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. Nerves are covered by a layer called myelin, which is responsible for protecting and maintaining their functionality. In the case of MS, the myelin becomes damaged, resulting in the nerves functioning unpredictably. MS is a disease characterized by relapses, which can cause permanent and irreversible damage to the patient's mobility, vision, and sensations, unless appropriate medical treatment is administered in time. This highlights the importance of the early detection of the presence or differentiation of MS using computer assisted diagnosis (CAD) systems. This study proposes an automatic segmentation approach of MS damaged regions using deep neural networks that can be integrated with any 3rd party CAD system. The data utilized comprised of a total of 1838 T2-type Magnetic Resonance Imaging (MRI) images collected from a cohort of 38 patients who underwent imaging at two distinct time points. Each MRI image was accompanied by meta-data, detailing the specific locations of the observed damage attributed to MS. The experimental setup investigated and optimized a comprehensive combination of different data preprocessing and augmentation steps, using several variations of the U-Net convolutional neural network (CNN), like U-Net++, Attention U-Net, ResUNet-a and TransUNet. The proposed methods achieved a segmentation accuracy of 0.70 based on the Dice Similarity Coefficient (DSC), a performance comparable to 2D and 3D state-of-the-art approaches in literature. A software framework encapsulating the automated model was also developed to facilitate the clinical practice workflow and underpin adoption by medical practitioners. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Image segmentation

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Ecological Development of a Novel Machine Learning Technique for Electricity Consumption and Cost Prediction in Cloud Computing 3rd

Ecological Development of a Novel Machine Learning Technique...

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3rd International Conference on Electronic Governance with Emerging Technologies, EGETC 2024

作者： Gogineni, Neelima Saravanan, M.S. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha Institute of Medical and Technical Sciences Chennai602105 India Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

ISBN: (纸本)9783031770289

In the realm of cloud computing, optimizing electricity consumption and accurately predicting costs are pivotal for Ecological and cost-effective operations. This paper introduces a pioneering machine learning methodology designed to predict electricity usage and estimate associated expenses in cloud computing environments. Leveraging historical consumption data and diverse parameters, the proposed technique employs a sophisticated algorithmic model, such as ensemble learning or deep neural networks, to forecast electricity consumption patterns. Additionally, it incorporates factors like workload fluctuations, resource allocation strategies, and pricing models to enhance the precision of cost predictions. In this research, we suggest an Extreme Gradient Boosting (XGBoost) model to predict electricity consumption, offload or transfer storage, rates, reducing the costs associated with energy use in data hubs. Based on an actual world dataset provided by the Independent Energy Systems Provider (IESO) inside the efficacy of this in the Canadian province of Ontario approach is evaluated to alleviate data storage in data centers and successfully cut down on energy use. Seventy percent of data goes toward testing, while the remaining thirty percent data is used for training. The results emphasize the potential of this technique to change the management of electricity consumption and cost projections in cloud computing, offering a significant step towards a more efficient and Ecological development of cloud ecosystem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud Data Storage Cost Prediction Electricity Consumption Machine Learning XGBoost

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Initial Observations from Field Testing of a Digital Participatory Tool to Improve Water Security in Rural India 24

Initial Observations from Field Testing of a Digital Partici...

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2024 International Conference on Information and Communication Technologies and Development, ICTD 2024

作者： Mehta, Shivani A Dutt, Aila Tannirkulam, Ajay Singh, Akshay Pratap Verma, Aman Singha, Anamitra Sreenidhi, Ananda Nandi, Ananjan Ankit Kumar, Ankit Dabli, Atharv Athira, P. Ramachandran, Balakumaran Bansal, Chahat Sheth, Chintan Sanjaybhai Dsouza, Craig Kumar, Deepak Sharma, Dharmisha Harshita Singh, Jaskaran Dadheech, Kapil Agrahari, Ksheetiz Krishna, Om Prasad, Pooja Radhakrishnan, Priyadarshini Sardana, Ramita Bhabak, Rittwick Bodavula, Ruptirumal Sai Vishnubhatla, Saketh Haldar, Samitha Agrawal, Sanjali Maurya, Shiv Prakash Kumari, Shruti Siddharth, S. Kumari, Sukriti Vishnu, S. Seth, Aaditeshwar School of Information Technology Indian Institute of Technology Delhi Delhi New Delhi India Gram Vaani Delhi New Delhi India Magasool Tamil Nadu Chennai India Department of Computer Science and Engineering Indian Institute of Technology Delhi Delhi New Delhi India Civil Engineering Indian Institute of Technology Palakkad Kerala Palakkad India Well Labs Karnataka Bengaluru India School of Public Policy Indian Institute of Technology Delhi Delhi New Delhi India Indian Institute of Technology Delhi Delhi New Delhi India

ISBN: (纸本)9798400710414

With almost half the Indian population facing water stress [42], likely to be aggravated with climate change [76, 122], an intersecting crises of environment, livelihood, and social justice can worsen the vulnerability of rural communities that are directly dependent upon water resources for their livelihood and sustenance. To address this challenge of rural water security especially for marginalized communities, MGNREGA is a public works and employment generation scheme in India that contributes towards the construction of water related assets such as farm ponds, checkdams, and trenches and bunds, to improve irrigation and climate resilience of rural communities. MGNREGA is a demand-driven scheme that solicits demands for water related assets through the local governance structure in India of village-level community meetings called Gram Sabhas. In many areas, Civil Society Organizations (CSOs) and community based institutions also play a significant role by coordinating participatory processes to build the capacity of rural community members to participate in local governance and demand such assets under MGNREGA. However, through multiple field visits, we observed a need to strengthen local processes with appropriate data and tools to support context-sensitive and evidence-based bottom-up planning. In this study, we adopt an approach of building a digital participatory tool, built on a technology stack that encapsulates different geospatial datasets, to answer key research questions of whether such technology artefacts can improve the participation of rural communities in water security planning and management, through, (1) community empowerment by building a data-driven understanding of water security, (2) ecological sustainability of water resources through scientific planning of assets, and (3) equity led participatory processes to improve fairness in the distribution of assets. We discuss here the design of the tool, Commons Connect, underlying datasets on whic

关键词： Data encapsulation

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Exergy-based economic, environmental, and sustainability analysis of two different biogas-fueled trigeneration proposals for a wastewater treatment plant (WWTP)

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Energy 2025年 326卷

作者： Mirmasoumi, Siamak Khoshbakhti Saray, Rahim Asgari, Nima Mechanical Engineering Department Chabahar Maritime University Chabahar Iran Faculty of Mechanical Engineering Sahand University of Technology Sahand New Town Tabriz Iran Department of Electrical & Computer Engineering Western University LondonON Canada

The objective of this study is to assess the performance of two CCHP systems in wastewater treatment plants (WWTPs): one powered by a micro gas turbine (AD-CCHP-MGT) and the other by an internal combustion engine (AD-CCHP-ICE). The anaerobic digestion (AD) unit of the WWTP provides biogas, which is analyzed through experimental digestion tests. Both the biogas fueled systems use the waste heat for various applications including feedstock pretreatment. The AD condition is thermophilic process (55 °C) with thermal pretreatment at 90 °C for 0.5 h. The results indicate that the self-sufficiency of power for AD-CCHP-ICE system is 43.41 %, whereas it is 30.90 % for AD-CCHP-MGT system. Furthermore, AD-CCHP-ICE system provides low-cost energy products and more electric power output, resulting in a shorter payback period. Additionally, excluding the AD investment cost, the payback period of systems are almost halved. The exergy-based environmental and sustainability analyses indicate that the specific CO2 emission rate of AD-CCHP-ICE is lower than that of AD-CCHP-MGT. With an environmental improvement index 1.5 times higher, the AD-CCHP-ICE is more environmentally friendly. Moreover, AD-CCHP-ICE demonstrates a larger sustainability index than AD-CCHP-MGT, as the exergy destruction and loss rates differ between the systems, following the same trend. © 2025 Elsevier Ltd

关键词： Waste heat

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Enhancing Industrial Automation Through IIoT-Enabled Cloud Computing and LSTM for Scalable and Real-Time Data Analytics 3

Enhancing Industrial Automation Through IIoT-Enabled Cloud C...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Shanmugapriya, S. Prasad, Rajendra Al-Farouni, Mohhammed H. Velmurugan, R. Singh, K. Ranjith Selvin Pradeep Kumar, S. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Chengalpattu India School of Agriculture Uttaranchal University Dehradun India College of technical engineering The Islamic University Department of Computer Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (纸本)9798331542375

In this thesis, we investigate the combined application of the Industrial Internet of Things (IIoT), cloud computing and Long Short-Term Memory (LSTM) networks to facilitate advanced industrial automation through the industrial analytics enabled by scalable and real time data processing. IIoT is an industrial approach to generating very large amounts of data from connected industrial devices that can be parsed through cloud computing to yield immediate insights into production processes, equipment health, and operational efficiency. Since this is a time series data, we employ LSTM (A type of recurrent neural network) in this architecture which is able to handle time series data well and is a good fit for predictive maintenance and anomaly detection problems in industrial setting. The proposed system involves three main components: Second, the IoT collection of data from IIoT devices, storage of data in the cloud, and processing of data are stored in the cloud;and finally, real time analysis of data using LSTM models. The LSTM models train on historical datasets containing sensor readings and machine logs, to make accurate equipment failure and process optimization predictions. For large scale data processing we use cloud computing platforms like AWS or Azure to manage where these big databases are located. Evaluation of the system performance is presented in the study with regard to prediction accuracy, latency and a comparison of LSTM with other ML techniques. We can see that that there's strong potential for these technologies to have a powerful impact on industrial automation, downtime, and operational efficiency. Yet, there are still data security and system scalability issues to explore. This research shows that IIoT implants and cloud computing, in conjunction with LSTM, can be designed to transform existing production processes. © 2025 IEEE.

关键词： Long short-term memory

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Semantic Attribute Mapping for Zero-Shot Object Classification in Deep Learning Networks

Semantic Attribute Mapping for Zero-Shot Object Classificati...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Agarwal, Pragya Reddy, B. Ravali Shakir, Sarah Abbas Raju, Hemanth Kumari, B. Geetha Kansal, Lavish Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq New Horizon College of Engineering Bangalore India Griet Department of Cse Bachupally Telangana Hyderabad India Lovely Professional University Phagwara India

ISBN: (纸本)9798331527495

One of the successful approaches to deal with the issue in object classification is the ability of the model to recognize categories that it has not seen before, referred to as the zero-shot learning or ZSL. This work aims at training and testing ZSL methods on a standard benchmark known as Animals with Attributes 2 (AwA2). In this work, CNN, to be precise ResNet is used for feature extraction after which a semantic attribute of each class is used for classification. These then are the semantic attributes of objects The ZSL model aligns the visual features from unseen objects to these attributes for classification. Evaluations based on top 1 and the top-5 accuracy to know if the model is likely to generalize to other classes. To ensure the system is computationally efficient and scalable, TensorFlow/Keras is used for the CNN features, and Hugging Face transformers for attribute features. © 2025 IEEE.

关键词： Zero-shot learning

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Strategic Placement of FACTS Devices for Optimizing Power Transmission Efficiency and Stability

Strategic Placement of FACTS Devices for Optimizing Power Tr...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Blake Pickett Asaf Varol Dept. of Engineering Management & Technology College of Engineering and Computer Science University of Tennessee at Chattanooga Chattanooga TN US Department of Computer Engineering College of Engineering & Natural Sciences Maltepe University Istanbul Türkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Flexible AC Transmission Systems (FACTS) devices play a critical role in enhancing power transmission efficiency and stability within modern electrical grids. This study investigates the strategic placement of two key FACTS technologies—Thyristor-Controlled Series Capacitors (TCSC) and Static Synchronous Compensators (STATCOM)—to optimize power flow, voltage regulation, and reactive power compensation. Using MATLAB Simulink modeling, a 200kilometer transmission line is simulated with different configurations to assess performance impacts based on device type and location. The analysis reveals that TCSC devices significantly improve power transfer by reducing line reactance and enhancing voltage stability, while STATCOM devices provide superior reactive power support and contribute to overall system stability. Comparative evaluations demonstrate that placing both devices at the sending end of the transmission line yields optimal results in terms of efficiency, power loss reduction, and operational resilience. Additionally, the study addresses the potential environmental implications of deploying FACTS devices near high-emission generation sources, highlighting the need for sustainable integration strategies. These insights offer practical guidance for utilities and system planners aiming to modernize transmission infrastructure through targeted technological interventions.

关键词： Performance evaluation Thyristors Reactive power Power transmission lines Software packages Power system stability Propagation losses Stability analysis Automatic voltage control Power capacitors

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TCP SYN Flood Attack Detection Using Logistic Regression and Multi-Agent Reinforcement Learning

TCP SYN Flood Attack Detection Using Logistic Regression and...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Mummana Sanjay Arun Raj Kumar P Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal Mangaluru Karnataka State India Department of Computer Science and Engineering National Institute of Technology Calicut Kozhikode Calicut State India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

In the realm of cybersecurity, Distributed Denial of Service (DDoS) attacks remain a continuous threat, particularly TCP SYN flood attacks due to their stealthiness and potential for disruption. In this paper, we propose a combination of Multi-Agent Reinforcement Learning (MARL) with logistic regression for enhancing TCP SYN attack detection, leveraging Actor-Critic as the reinforcement learning algorithm. A novel approach is introduced for hyperparameter optimization using MARL, offering an alternative to traditional techniques such as GridSearchCV and RandomSearchCV. We present a comparative analysis between traditional logistic regression and MARL enhanced approaches, evaluating their performance using metrics such as accuracy, false negatives, and false positives. Results demonstrate that our proposed approach significantly improves detection accuracy and reduces false positives, underscoring its potential in bolstering cybersecurity defenses against sophisticated DDoS threats.

关键词： Logistic regression Adaptation models Accuracy Reinforcement learning Telecommunication traffic Denial-of-service attack Floods Reliability computer crime Tuning

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Enhancing Alzheimer's Disease Detection Using Optimized Attribute Selection and Random Forest Classifier for Improved Accuracy

Enhancing Alzheimer's Disease Detection Using Optimized Attr...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Sathiya, A. Basha, Ch Hussaian Vasanth, S. Sharmila, Jasphin Jeni Prabakaran, S. Indhumathi, R. Department of Electronics and Communication Engineering Karur639113 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai600124 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Cms College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore641032 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331521394

This research aims at improving the identification of Alzheimer's dementia by using the optimized attribute selection method and preliminary diagnosis data-driven learning algorithm, namely Random Forest Classifier. Alzheimer's is a progressive neurological disorder exhibiting barriers in preliminary diagnosis primarily due to the high density and intricate nature of medical information. In this study, attribute selection techniques are used to minimize the data dimensionality, by selecting only key features for classification with low computational complexity and improved model accuracy. The Random Forest Classifier is used because it is accurate, can process big data and because of its diverse uses. Through selecting the most relevant features of the model, it provides a prominent level of precise and acute identification of the early stage of Alzheimer's and could significantly enhance the medical treatment effects. The outcomes from this study indicate that feature optimization can be used interchangeably with preliminary diagnosis data-driven learning algorithms for accurate disease prediction. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Jialiang Gao, Feng Gan, Yanhai Dong, Junyu Du, Qian Ocean University of China School of Computer Science and Technology Qingdao266100 China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

Accurately forecasting sea ice concentration (SIC) in the Arctic is critical to global ecosystem health and navigation safety. However, current methods still is confronted with two challenges: 1) these methods rarely explore the long-term feature dependencies in the frequency domain. 2) they can hardly preserve the high-frequency details, and the changes in the marginal area of the sea ice cannot be accurately captured. To this end, we present a Frequency-Compensated Network (FCNet) for Arctic SIC prediction on a daily basis. In particular, we design a dual-branch network, including branches for frequency feature extraction and convolutional feature extraction. For frequency feature extraction, we design an adaptive frequency filter block, which integrates trainable layers with Fourier-based filters. By adding frequency features, the FCNet can achieve refined prediction of edges and details. For convolutional feature extraction, we propose a high-frequency enhancement block to separate high and low-frequency information. Moreover, high-frequency features are enhanced via channel-wise attention, and temporal attention unit is employed for low-frequency feature extraction to capture long-range sea ice changes. Extensive experiments are conducted on a satellite-derived daily SIC dataset, and the results verify the effectiveness of the proposed FCNet. © 1980-2012 IEEE.

关键词： Sea ice

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