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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Hongzong Li Jun Wang Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Computer Science & Department of Data Science City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Bi-clustering, also known as co-clustering, is a powerful data analysis technique that simultaneously clusters rows and columns of a data matrix, revealing hidden patterns. In this paper, we propose a neurodynamics-driven binary matrix factorization approach for bi-clustering binary data. The proposed method utilizes multiple discrete Hopfield networks operating concurrently to explore local optimal solutions. Furthermore, a particle swarm optimization rule is iteratively applied to reinitialize the neuronal states for directing the search toward better solutions. Comparative evaluations across six benchmark datasets reveal that the proposed method outperforms five existing methods in terms of five internal and external indices.

关键词： Neurodynamics data analysis Hopfield neural networks Information processing Benchmark testing Particle swarm optimization Optimization Intelligent control

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Q-TEC: A Highly Secured Trapdoor Encryption Scheme for User Authentication in Content-Based Image Retrieval Systems in Cloud

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Traitement du Signal 2025年第2期42卷 1123-1130页

作者： Vijay, K. Jayashree, K. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai602105 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600123 India

Content-Based Image Retrieval systems play a critical role in the management of vast image databases, which necessitates secure user authentication and data protection when stored in cloud. Considering the shift in security issues, advanced encryption techniques are generally required. Therefore, an innovative Quantum-Resistant Trapdoor Encryption for Secure Session-Based CBIR (Q-TEC) system is proposed here. The user interface incorporates robust input validation and salting to prevent unauthorized code execution and hash collisions, respectively. Communication channel security is enhanced with TLS 1.3, PFS, and ECDH key exchange. The encryption and key management system is double-staged with Argon2 and RC4 passwords. Crystals-Kyber is used to manage the keys. Encoding indexing and access control are achieved with DBF and zk-SNARK. Secure query execution is accomplished with SMPC and homomorphic encryption with CKKS. Overall, compared to conventional methods, the proposed solution supports a high degree of security and performance, offering reasonable encryption and decryption times and reduced storage requirements. ©2025 The authors.

关键词： Authentication

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Energy Consumption Optimization using Big data Analytics for Smart Grid Management 5

Energy Consumption Optimization using Big Data Analytics for...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Devi, K. Sumithra Contractor, Dipenkumar Deepa, V. Lakshmi, T.K. Santhikala, M. Mounika, Kamati Kcg College of Technology Department of Computer Science and Engineering Chennai India Government Engineering College Department of Computer Engineering Dahod India Sri Ramakrishna College of Arts and Science Department of Information Technology Tamil Nadu Coimbatore India Malla Reddy University Department of Computer Science and Engineering Telangana Hyderabad India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Mb School of Pharmaceutical Sciences Mohan Babu University Department of Pharmacy Practice Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331522100

The application of big data analytics to improve energy usage efficiency in smart grids has been prompted by the rising need for power management. Adding to the above, it poses a big data solution for demand prediction, load consolidation, and fault diagnosis. Establishing the new and state-of-the-art innovative LSTM network that was employed in the forecast yielded a higher demand forecast accuracy of 94.2% compared to the previous systems. The load balancing index achieved 92% with an accuracy of 93.7% in fault detection, thus minimizing maintenance cost by 14.7% while improving grid reliability. The proposed model was found to give better results than previous conventional machine learning techniques and even modern ones which are more complex, especially for an ever-changing grid environment. Overall, these findings show the promise of big data solutions for handling the challenges of the contemporary smart grid while improving its efficiency through energy optimization. Consequently, this work lays a framework for subsequent studies on the remaining applications of advanced analytics in energy management systems. © 2025 IEEE.

关键词： Smart power grids

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A Resource-Efficient Federated Learning Framework for Intrusion Detection in IoMT Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Fahim-Ul-Islam, Md. Chakrabarty, Amitabha Alam, Md. Golam Rabiul Maidin, Siti Sarah Brac University Department of Computer Science and Engineering Bangladesh INTI International University Faculty of Data Science and Information Technology Nilai Malaysia

The emergence of medical sensors in Smart Healthcare Systems (SHS) has enabled the development of sophisticated Internet of Medical Things (IoMT) networks which is crucial for tracking vital physiological parameters in consumer electronics. These networks are progressively incorporated into consumer devices allowing users to monitor their health parameters effortlessly. Nonetheless, they encounter considerable security and privacy issues stemming from weaknesses in data transfer. Despite the potential of Intrusion Detection Systems (IDS), there is a critical need for a real-time, highly precise attack detection system optimized for the edge-centric Internet of Medical Things (IoMT) environment in consumer sensor devices. Therefore, we introduce the federated kolmogorov-arnold network (FedIoMT), an advanced federated learning framework that utilizes meta-learning along with an advanced clustering method to achieve robust model aggregation. Our FedIoMT acts as both an adaptive personalized updating mechanism and a shared global classifier. In addition, we propose a novel intrusion detection model that uses the kolmogorov-arnold convolutional network (KANConvNet) as its local classifier, which improves scalability and interpretability in the FL framework updates. Our FedIoMT model demonstrates superior performance compared to other existing FL-based methods, achieving validation accuracies of 99.38%, 99.23%, 99.26%, and 99.2% across four benchmark IoMT datasets under the IID setting. FedIoMT utilizes significantly more floating point operations per second (FLOP) counts up to 2.8 times more than other models and also excels in memory efficiency, consuming up to 93% less peak memory across devices compared to the alternatives. We thoroughly evaluated FedIoMT on embedded consumer devices, demonstrating its resource-efficient characteristics. © 2025 IEEE.

关键词： Network intrusion

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AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tidke, Bharat Pragadeeswaran, S. Lakshmi, R. Poornima Bhuvaneswari, G. Manikandan, G. Murugan, S. University Nagpur Campus Pune India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering college Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Kavaraipettai Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities. © 2025 IEEE.

关键词： Sustainable city

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Hybrid Deep Transfer Learning for Enhanced Brain Tumor Detection through the Integration of MobileNetV2 and InceptionV3

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Procedia computer science 2025年 258卷 2968-2977页

作者： Roseline Oluwaseun Ogundokun Charles Awoniyi Nabeela T. Adebola Akinrotimi Akinyemi Samson Adebisi Akinpelu Matthew O. Adigun Department of Centre of Real Time Computer Sciences Kaunas University of Technology Kaunas 44249 Lithuania Department of Computer Science Landmark University Omu-Aran Nigeria Bupa Insurance United Kingdom Data Science Department University of Salford United Kingdom Department Of Computer Science Caleb University Imota Lagos Nigeria School of Mathematics Statistics and Computer Science University of Kwazulu-Natal Durban South Africa Department of Computer Science University of Zululand Kwadlangezwa South Africa

Brain Tumors (BT) are one of the most hazardous diseases in the world, primarily because of the speed at which they spread and high mortality rates. Hence, diagnosing BT at an early stage with higher accuracy is necessary. Recent breakthroughs in deep transfer learning have achieved great performances in identifying common types of BT, such as glioma, meningioma, and pituitary tumors. In this paper, we will propose a novel hybrid deep transfer learning architecture by combining the strengths of two state-of-the-art pre-trained deep convolutional neural networks: MobileNetV2 and InceptionV3. We implement a parallel feature extraction process, reduce dimensionality in the extracted features using dropout techniques, and pass the refined data through fully connected layers for accurate classification. In this context, our two-step methodology of sub-image classification and final image categorisation with the help of majority voting and maximum probability principles shows much-improved results. Validated on a comprehensive BT dataset, our approach achieves 98.68% accuracy, 97% precision, 97.25% recall, and 97% F-measure with 0.9975 AUC and average precision of 99.50%. The outcomes surpass the performance of existing models on precision, sensitivity, and generalization, thus revealing the value that this technique may hold in whole-slide MRI image classification and clinical practice.

关键词： Brain tumor Transfer learning Fine-tuning Deep learning Pre-trained model Deep neural network

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Anomaly Detection in Social Networks using Improved Local Outlier Factor 1

Anomaly Detection in Social Networks using Improved Local Ou...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Mhsnhasan, Muntader Sowmya, M. Prabhakaran, M.V. Ghamya, K. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively. © 2025 IEEE.

关键词： Principal component analysis

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Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases

Implementing Machine Learning for Early Detection and Progno...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Iqbal, J. L. Mazher Gurrapu, Omprakash Saritha, P.S. Rajesh Kanna, R. Jayanthi, R. Tejaskumar, M.B. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India Volvo Trucks NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Christ University Department of Computer Science Bangalore India Dayananda Sagar College of Engineering Department of Master of Computer Applications Bengaluru India Don Bosco Institute of Technology Bangalore India

ISBN: (纸本)9798331538538

The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, Random Forest, XGBoost, and CNN to forecast the occurrence of the. Due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care. © 2025 IEEE.

关键词： Diseases

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Modelling a Multi-Perceptron Q Network Layer for Analyzing Patient Condition in Healthcare Sector

Modelling a Multi-Perceptron Q Network Layer for Analyzing P...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Karthi M Manirajan S P Prince A R. Santhosh Department of Artificial Intelligence and Data Science Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Chronic diseases like chronic respiratory diseases, diabetes, heart disease (HD) and cancer are the important causes of mortality globally. The diagnosis of Heart-related diseases with a variety of symptoms or characteristics is incredibly complex. Internet of Things (loT) embedded solution has improved with the increase in smart wearable devices. However, people who undergo heart attacks unexpectedly have poor survival rates. As a result, an Internet of Things based Multi-Perceptron Q-Network (MQN)-based monitoring program is suggested for cardiac patients to assist in identifying heart disease and administering medication appropriately. The technology based on MQN is used to securely encrypt this sensor data before sending it to the cloud. The encrypted and decrypted data is eventually done and MQN classifier is used to complete the classification process. The two categories of data that make up the classified outcomes are i) regular and ii) irregular. It indicates the individual's cardiac status, and if the results are peculiar, the doctor is notified via text so that the patient can receive treatment. When compared to current algorithms, the MQN for HD diagnosis exhibits better enhancement in the estimated investigational results. Furthermore, when contrasted against the current MQN, the introduced Multi-layered Perceptron (MLP) system facilitates safe data transmission yields the maximum security level of 96%, and is accomplished in the shortest amount of time for both decryption and encryption.

关键词： Heart Accuracy Sensitivity Pulmonary diseases Mortality Medical services Encryption Internet of Things Wearable devices Diseases

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A multi-scale CNN with atrous spatial pyramid pooling for enhanced chest-based disease detection

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PeerJ computer science 2025年 11卷 e2686-e2686页

作者： Bukhari, Muhammad Abdullah Shah Bukhari, Faisal Asif, Muhammad Aljuaid, Hanan Iqbal, Waheed Department of Computer Science University of the Punjab Lahore Pakistan Department of Data Science University of the Punjab Lahore Pakistan Department of Computer Science National Textile University Faisalabad Pakistan Computer Sciences Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

We introduce a sophisticated deep-learning model designed for the early detection of COVID-19 and pneumonia. The model employs a convolutional neural network-integrated with atrous spatial pyramid pooling. The atrous spatial pyramid pooling mechanism enhances the convolutional neural network model’s ability to capture fine and large-scale features, optimizing detection accuracy in chest X-ray images. This improvement, along with transfer learning, significantly enhances the overall performance. By utilizing data augmentation to address the scarcity of available X-ray images, our atrous spatial pyramid pooling-enhanced convolutional neural network achieved a validation accuracy of 98.66% for COVID-19 and 83.75% for pneumonia, which beats the validation results of the other state of the art approaches (the metrics used for evaluation were accuracy, precision, F1-score, recall, specificity, and area under the curve). The model’s multi-branch architecture facilitates more accurate and adaptable disease prediction, thereby increasing diagnostic precision and robustness. This approach offers the potential for faster and more reliable diagnoses of chest-related conditions. © (2025), (PeerJ Inc.). All rights reserved.

关键词： Convolutional neural networks

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