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A Conditional GAN and Dual-Channel Hybrid Deep Feature Framework for Robust Sensor Fault Detection in WSNs

A Conditional GAN and Dual-Channel Hybrid Deep Feature Frame...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

Sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 ieee.

关键词： Gallium nitride

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Enhancing Real-Time IoT Applications in Cloud Communication Networks Through Adaptive Intelligent Routing and Secure data Transmission 2

Enhancing Real-Time IoT Applications in Cloud Communication ...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Swathi, B. Ramesh, B. Latha, Kambala Madhu Bhavsingh, M. Padma, M. Manjunatha, N. G Pulla Reddy Engineering College Department of CSE Andhra Pradesh Kurnool India CVR College of Engineering Department of ECE Hyderabad India Avanthi's Research and Technological academy Andhra Pradesh India Ashoka Women's Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Lecturer in Computer Applications Government Degree College Srisailam Project Andhra Pradesh Nandyala India Shri Jagdishprasad Jhabarmal Tibrewala University Department of CSE Rajasthan India

ISBN: (纸本)9798331505745

The growing Internet of Things network with cloud technology enables real-time solutions in healthcare, autonomous systems, and industrial automation. These systems face challenges with response time, shifting network layouts, power conservation, and data security threats. RPL and traditional routing protocols struggle to adapt to complex networks, while machine learning-based and EERP approaches solve some issues but leave others unresolved. This study presents an AI routing method that enhances live data transfer between IoT systems and cloud platforms. The algorithm tracks network shifts to make fast routing decisions, reducing time delays and conserving power. This study implement basic encryption, device confirmation, and abnormal event recognition to protect data transfers. Simulations compared the technique's performance to RPL and EERP approaches, measuring latency, data delivery rate, energy usage, network speed, and security capabilities. Testing shows a 25-50% improvement in latency and 10-20% better PDR results. Energy utilization decreases by 30-50%, while throughput remains superior. Security features demonstrate reduced encryption overhead with improved anomaly detection and authentication. This research addresses IoT-cloud communication network challenges through an adaptable system that conserves power, protects data, and enhances reliability for time-sensitive applications. © 2025 ieee.

关键词： Internet of things

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 ieee.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Applying Communication Privacy Management Theory to Youth Privacy Management in AI Contexts 4

Applying Communication Privacy Management Theory to Youth Pr...

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4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Campbell, Molly Joshi, Sandhya Barthwal, Ankur Shouli, Austin Shrestha, Ajay Kumar Vancouver Island University Computer Science Department Nanaimo Canada Vancouver Island University VIU Affiliate Nanaimo Canada

ISBN: (纸本)9798331518882

The rapid integration of Artificial Intelligence (AI) technologies into the lives of young digital citizens has escalated privacy concerns and the need for critical examination. This study uses Communication Privacy Management (CPM) Theory to understand how youth and critical stakeholders navigate these concerns. A total of 306 participants were surveyed, comprising 146 AI professionals, 127 parents and educators and 33 youths (aged 16-19). Employing a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from open-ended responses. Descriptive statistics reveal distinct perspectives among different demographics regarding data ownership, education, transparency and trust, parental role and perceived risks and benefits associated with AI systems. Structural equation modelling identified key influences on youth privacy management, highlighting the significance of transparency and trust, education and awareness, and parental data sharing among AI professionals, parents, educators, and young digital citizens. The qualitative analysis further underscored unique concerns, emphasizing a lack of understanding and data misuse contributed to the feeling of helplessness shared by all stakeholders. This study underscores the importance of integrating diverse stakeholders' perspectives in the development of AI systems to address the complex challenges faced by youth. Recommendations include collaborative policymaking, implementing user-centric design practices, and enhancing privacy education to empower young digital citizens. © 2025 ieee.

关键词： Differential privacy

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Enhancing Product Bundling with Large Language Models 13

Enhancing Product Bundling with Large Language Models

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13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Bayrak, Ahmet Tugrul Kaplan, Alper Data Science and Innovation Ata Technology Platforms Istanbul Turkey

ISBN: (纸本)9798331516147

Recommendation systems have become popular in recent years. Today, in a world where competition is increasing, recommendation systems are used in many different areas. When the number of products and customers is high, it is important to recommend the right products to the right people. For this reason, different methods are widely used in recommendation systems and various techniques have been explored and developed to improve recommendation accuracy. The study evaluates the potential use of large language models. ChatGPT is chosen to create and expand existing product bundles. Apriori and word embedding models such as Word2Vec and FastText have been prepared for recommendations based on existing and bundles. The results show that large language models can improve bundle creation processes in some scenarios and that tools such as ChatGPT have the potential to improve recommender systems. © 2025 ieee.

关键词： Embeddings

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A Novel Method for Clustering High-Dimensional data Using K-Means 1

A Novel Method for Clustering High-Dimensional Data Using K-...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kunchakuri, Naveen Kandari, Chandana Patchigolla, Veera Lakshmayya Clarksburg MD United States Germantown MD United States

ISBN: (纸本)9798331518578

The k-means approach is a fundamental tool in unsupervised machine learning (ML) for grouping high-dimension data. data mining, recognizing patterns, analyzing images, bioinformatics, and ML are just a few of the fields that use clustering, an efficient quantitative data processing method. data can be divided into categories of items with unique properties by using clustering. Numerous activities, management, and service data utilized by managers for system functional assurance are generated by cellular radio systems. Although there are strong clustering techniques like Self Organizing Mapping (SOM), there is almost no research demonstrating how well these techniques function with OAM databases that have an abundance of features (>20) gathered from real-world system adoption. Furthermore, there are still a few unresolved issues regarding the relevance of the clustering techniques. For example, when employing SOM, the beneath data fails to explicitly provide data on clusters following SOM training;therefore, the k-means method for classifying SOM components must be used afterward. In this regard, an approach for clustering radio units depending on a mix of SOM and K-means techniques is described in this study. The technique is used to identify the short-term conduct (hourly observational intervals) and long-term conduct (15 days observational duration) of wireless units by extracting cell shapes. The investigation makes use of OAM statistics gathered from a real 4G/LTE system installed in a significant European town. © 2025 ieee.

关键词： Conformal mapping

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In-Context Multitask Learning for Few-shot Fine-tuning of Large Language Models in Traditional Chinese Medicine Tongue Diagnosis

In-Context Multitask Learning for Few-shot Fine-tuning of La...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Fu, Changzeng Fu, Zelin Yan, Shaojun Lyv, Xiaoyong Zhao, Yuliang Northeastern University China Osaka University Japan Hebei Key Laboratory of Marine Perception Network and Data Processing @ NEUQ China

ISBN: (纸本)9798350368741

Tongue diagnosis is integral to Traditional Chinese Medicine (TCM) for evaluating a patient's body constitution. Yet, this field faces challenges such as indirect constitution diagnosis, a dearth of labeled datasets, and the complexities of few-shot learning. Existing studies focus mainly on analyzing tongue color and coating, rather than directly analyzing the body constitution from the patient's tongue. Moreover, the lack of publicly available datasets with constitution labels impedes model development for constitution diagnosis. The resource-intensive process of creating large-scale labeled datasets calls for efficient training methods on small datasets. Addressing these issues, this paper presents an In-Context Multitask Learning approach to improve few-shot fine-tuning accuracy for Large Language Models (LLMs) in constitution diagnosis. We created a dataset for color-coating analysis and a few-shot dataset with constitution labels and a structured prompt-label framework, allowing LLMs to learn from varied datasets. Our method shows improved performance over traditional methods in constitution diagnosis and significantly boosts LLMs' generalization and robustness in TCM's complex diagnostic tasks, providing a viable path for automating tongue diagnosis. © 2025 ieee.

关键词： Few-shot Fine-Tuning Large Language Models Multitask Learning Tongue Diagnosis

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AI-Driven Patient Monitoring in Smart Hospitals: A Survey on Edge Computing Integration and Applications 2

AI-Driven Patient Monitoring in Smart Hospitals: A Survey on...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ranjani, R. Naresh, R. SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chengalpattu Tamilnadu Chennai603 203 India

ISBN: (纸本)9798331505745

The integration of artificial intelligence (AI) and edge computing in healthcare is transforming patient monitoring systems, particularly within smart hospitals. These technologies enable real-time data processing, enhanced decision-making, and improved patient outcomes by shifting computational tasks closer to data sources, reducing latency, and alleviating the burden on centralized cloud infrastructures. This survey paper provides a comprehensive review of the current state of AI-driven patient monitoring systems in smart hospitals, focusing on the role of edge computing. It explores key applications, such as vital sign monitoring, early disease detection, and predictive analytics, while highlighting the benefits of edge computing, including real-time analytics, enhanced data privacy, and resource efficiency. Furthermore, the paper examines challenges in the deployment of AI and edge computing in clinical settings, such as data security, interoperability, scalability, and the need for robust AI algorithms tailored to healthcare environments. The survey concludes with a discussion of future research directions and potential advancements to further enhance AI-driven patient monitoring systems in smart hospitals. This review serves as a valuable resource for researchers and healthcare professionals aiming to understand the intersection of AI and edge computing in modern healthcare infrastructures. © 2025 ieee.

关键词： data Analytics

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Predictive Maintenance in Autonomous systems using Improved Support Vector Machine 1

Predictive Maintenance in Autonomous Systems using Improved ...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Mamatha Bai, B.G. Divya, N. Sree Hamedshnain, Ammar Kumar, S. Senthil Vasumathi, T. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, rapid development in the domain of Predictive Maintenance have resulted in improved equipment lifetime span, decrease maintenance costs and reduced the unplanned downtime. Predictive maintenance is an approach that addresses any equipment or component failures in the systems proactively. However, integration of this approach in automated systems has several challenges such as ensuring the accuracy of fault predictions, management of large-scale data and maintenance of system reliability under various operational conditions. To overcome these challenges Improved Support Vector Machine (ISVM) is proposed. Initially, the data is collected from the Predictive Maintenance dataset (PMD) and the preprocessing is performed through Inter Quartile Range (IQR) which enhances the quality of data and modeling efforts. Further relevant features are extracted using Recursive Feature Elimination (RFE). Finally, the prediction is performed using ISVM which achieved better outcomes compared to existing a One-Class Support Vector Machine (SVM) in terms of accuracy as well as recall by achieving 90.58%, 80.79% respectively. © 2025 ieee.

关键词： Predictive maintenance

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Computer-Aided English Translation system based on Text-to-Text Transfer Transformer model 1

Computer-Aided English Translation system based on Text-to-T...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Cheng, Yue Chengdu College University of Electronic Science and Technology of China School of Arts and Sciences Chengdu China

ISBN: (纸本)9798331529246

In recent years, design and application of a computer-aided English translation system have become essential in the context of globalization for effective crosslinguistic communication in various sectors such as academic, professional, and social interactions. Even though there are many traditional translation methods still they face issues related to failing to preserve context, and linguistic nuances. Therefore, this research aims to provide a robust solution to enhance translation quality and efficiency by incorporating Text-to-Text Transfer Transformer (T5) model for performing translations with unified framework that handles a wide variety of language-based tasks, solving text-to-text transformation. Initially, the data collected from the Workshop on Statistical Machine Translation (WMT) dataset. The collected data is preprocessed by utilizing tokenization for obtaining efficient data. The proposed T5 model generates initial word vectors, capturing contextual meaning for text generation and organized using phrase and rule encoders. Further, encoder divided the data into monolingual and bilingual encoders, that framework enhances translation accuracy while systematically processing input text while maintaining linguistic coherence and contextual integrity. The results of proposed T5 model shows that evaluation of English translation model achieved high performance than existing model with Bilingual Evaluation Understudy (BLEU) as 25.4%. © 2025 ieee.

关键词： Computer aided language translation

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