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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： H. Azath Chitra Sabapathy Ranganathan G. Arunsankar R. Reena Roy M. Rajmohan S. Murugan School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Mphasis Corporation Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Chennai Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Many sectors have formed Sustainable Industrial Symbiosis (SIS) to improve resource efficiency and environmental sustainability. This study explores how SIS frameworks may optimize waste management by combining Internet of Things (IoT) technologies with gradient-boosting algorithms. Using sensors and IoT devices allows for real-time data gathering on waste production, composition, and processing steps. Gradient Boosting models are used to optimize recycling operations, reduce waste, and forecast the best use of resources. The objective is to improve SIS with integration of IoT and Gradient Boosting, enabling effective waste management, improving resource reuse, and reducing environmental impact. With this connectivity, adaptive management solutions may be used, which in turn helps to improve resource utilization and decrease environmental impact, thereby supporting a sustainable circular economy. As shown in case studies and simulations, the proposed IoT-Gradient Boosting method successfully achieves cost savings and environmental advantages in industrial environments. The results show how IoT and advanced analytics can revolutionize waste management, leading to more environmentally friendly manufacturing processes and better sustainability initiatives. By using IoT and machine learning (ML) methods to solve difficult problems in waste management and resource optimization, this study helps move the discipline of SIS forward.

关键词： Waste management Symbiosis Federated learning Scalability Boosting Real-time systems Sensors Internet of Things Sustainable development Optimization

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A Landmark-Aided Navigation Approach Using Side-Scan Sonar

arXiv

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arXiv 2025年

作者： Davenport, Ellen Nguyen, Khoa Jang, Junsu Ma, Clair Fish, Sean Lenain, Luc Meyer, Florian The Scripps Institution of Oceanography The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States The Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology AtlantaGA30332 United States

Cost-effective localization methods for Autonomous Underwater Vehicle (AUV) navigation are key for ocean monitoring and data collection at high resolution in time and space. Algorithmic solutions suitable for real-time processing that handle nonlinear measurement models and different forms of measurement uncertainty will accelerate the development of field-ready technology. This paper details a Bayesian estimation method for landmark-aided navigation using a Side-scan Sonar (SSS) sensor. The method bounds navigation filter error in the GPS-denied undersea environment and captures the highly nonlinear nature of slant range measurements while remaining computationally tractable. Combining a novel measurement model with the chosen statistical framework facilitates the efficient use of SSS data and, in the future, could be used in real time. The proposed filter has two primary steps: a prediction step using an unscented transform and an update step utilizing particles. The update step performs probabilistic association of sonar detections with known landmarks. We evaluate algorithm performance and tractability using synthetic data and real data collected field experiments. Field experiments were performed using two different marine robotic platforms with two different SSS and at two different sites. Finally, we discuss the computational requirements of the proposed method and how it extends to real-time applications. © 2025, CC BY-NC-ND.

关键词： Sonar

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PAC-MC: An Efficient Password-Based Access Control Framework for Time Sequence Aware Media Cloud

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Haiyan Liu, Penghui Zhong, Xiaoxiong Luo, Fucai Xiao, Bin Yang, Yuanyuan Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Zhejiang Gongshang University School of Computer Science and Technology Hangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs. © 2025 IEEE.

关键词： Access control content protection media cloud password searchable encryption time sequence

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Optimizing Cervical Lesion Detection Using Deep Learning with Particle Swarm Optimization

Optimizing Cervical Lesion Detection Using Deep Learning wit...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Zia Khan Saif Ur Rehman Khan Omair Bilal Asif Raza Ghazanfar Ali School of Computer Science and Engineering Central South University Lushan Road Changsha China Department of Computer Science Bahauddin Zakariya University Multan Pakistan Institute of Computing Muhammad Nawaz Sharif University Multan Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Cervical cancer ranks as the second most frequent malignancy in women worldwide. Cervigram is a type of cervical cancer screening tool that uses digital imaging technology. It captures images of the cervix to detect abnormalities or signs of cancerous growth. This method can assist in early detection and prompt treatment, potentially saving lives. Cervical is its reliance on human interpretation rather than automated analysis for detecting cervical abnormalities. In this study, we employed a metaheuristic approach PSO to enhance hierarchical feature learning in the Xception model for classifying cervigram images into cancerous and normal categories. By leveraging PSO, our approach optimizes the feature extraction process, while Xception’s deep learning capabilities facilitate intricate feature extraction, enabling precise differentiation between cancerous and normal cervigrams. We employ residual block with the Xception pre-trained model to facilitate faster convergence during training due to its ability to capture and refine complex hierarchical features. In this study, we utilized the benchmark Cervigram dataset, comprising 2779 Cancer and 1960 Normal images. The experiment demonstrates that our proposed model achieved an accuracy of 97.57%. Furthermore, our model outperforms existing studies and other pre-trained models.

关键词： Deep learning Training Analytical models Accuracy Metaheuristics Benchmark testing Feature extraction Robustness Particle swarm optimization Cervical cancer

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Framework for Drug User Analysis and Prediction

Framework for Drug User Analysis and Prediction

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Dhanush Sethuraman Ela Ananya Joseph Jeffrey J Senthil Kumar Thangavel P Rangasami Somasundaram K Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Social Work Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics Amrita School of Physical Sciences Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This paper provides a framework for knowledge and action related to and about substance abuse, that integrates both capacity development and data-driven analysis for drug use analysis and modeling. In line with the National Action Plan for Drug Demand Reduction (NAPDDR) the framework focuses on a multi-pronged strategy. It then gives examples of so-called capacity-building exercises, such as those carried out by Amrita Vishwa Vidyapeetham in Tamil Nadu in 2020. These programs targeted key stakeholders, including healthcare professionals, police personnel, teachers, and social workers, focusing on preventive education, medical management, psychosocial issues, and rehabilitation strategies related to substance abuse. Pre- and post-training evaluations showed a strong increase in participants knowledge and treatment skills for substance use disorders. Second, the paper presents a data-driven, quantitative analysis of drug use patterns and related factors. Using a Support Vector Machine (SVM) algorithm, the study utilizes a dataset of drug consumption patterns to determine the factors which are most important for drug consumption. Importantly, the framework also include Explainable AI (XAI) methods to increase the interpretability and tracibility of the machine learning model. XAI approaches are used to interpret the model's decision process and to identify the most important factors in predicting the use of drugs. Incorporating insights from the capacity-building program evaluation and from the XAI-aided machine learning analysis, this framework provides an integrated end-to-end framework for drug user analysis and the generation of actionable recommendations for the design of targeted interventions and prevention strategies.

关键词： Drugs Training Support vector machines Analytical models Adaptation models Explainable AI Addiction Buildings Predictive models Stakeholders

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Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction

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computers in Biology and Medicine 2025年 184卷 109321-109321页

作者： Xuan, Ping Wu, Shien Cui, Hui Li, Peiru Nakaguchi, Toshiya Zhang, Tiangang Department of Computer Science and Technology Shantou University Shantou China School of Cyberspace Security Hainan University Haikou China Department of Computer Science and Information Technology La Trobe University Australia Australian Centre for AI in Medical Innovation La Trobe University Australia School of Computer Science and Technology Heilongjiang University Harbin China Center for Frontier Medical Engineering Chiba University Japan

Identifying the potential side effects for the interested drugs can help reduce harm to patients caused by drugs in clinical use and decrease the risk of drug development failure. Multiple functionally similar drugs often have multiple similar side effects, resulting in the closed relationships among these nodes. However, most of previous methods did not completely encode the features from the biological perspective to mine the complex associations between the drugs and side effects. A prediction model based on interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning, ICAL, was proposed to fuse the global correlations reflected by multiple hypergraphs and to learn the attributes of a pair of drug and side effect nodes enhanced by the channels and attributes. First, we designed a hypergraph architecture where a hyperedge reflects the complex correlations between a single drug (side effect) and all the drugs and side effects, and the entire hypergraph composed of the hyperedges reveals the global correlations of all the drugs and side effects. Two hypergraphs were established based on two types of drug similarities, and each hypergraph implies its specific complex relationships among multiple drugs and side effects. Second, we proposed an interactive hypergraph neural network to enable the learning of global correlation features of drugs and side effects from the two hypergraphs. It propagated the node features across multiple hypergraphs and encoded the context relationships within these hypergraphs. Besides, the attentions at the channel level and at the attribute level were proposed to integrate the semantic correlations among multiple channels and to encode the long-distance dependence within the attributes of a pair of drug and side effect. The experimental results based on cross-validation showed that our new model outperformed seven advanced prediction methods in terms of AUC, AUPR, and recall rates for the top-ranked candidat

关键词： Prediction models

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Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array

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IEEE Transactions on Vehicular Technology 2025年

作者： Tang, Xiao Jiang, Yudan Liu, Jinxin Du, Qinghe Niyato, Dusit Han, Zhu Xi'An Jiaotong University School of Information and Communication Engineering Xi'an710049 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518063 China Northwestern Polytechnical University School of Electronics and Information Xi'an710072 China Xi'An Jiaotong University School of Mechanical Engineering Xi'an710049 China Nanyang Technological University College of Computing and Data Science Singapore Singapore University of Houston Department of Electrical and Computer Engineering Houston77004 United States

This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and antenna element positioning. Due to the non-convexity and multi-fold difficulties from an optimization perspective, we develop a deep learning-based framework where beamforming is tackled as a Rayleigh quotient problem, while antenna positioning is addressed through multi-layer perceptron training. The neural network parameters are optimized using stochastic gradient descent to achieve effective jamming mitigation strategy, featuring offline training with marginal complexity for online inference. Numerical results demonstrate that the proposed approach achieves near-optimal anti-jamming performance thereby significantly improving the efficiency in strategy determination. © 2025 IEEE.

关键词： Beamforming

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EXAD: Framework for Designing Explanations in Adaptive User Interfaces

EXAD: Framework for Designing Explanations in Adaptive User ...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Iqra Batool Gad Gad Mostafa M. Fouda Mohamed I. Ibrahem Zubair Md Fadlullah Department of Computer Science Western University London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Adaptive user interfaces that personalize their behavior based on user patterns are becoming increasingly common, yet they often fail to communicate their adaptations to users effectively. This paper introduces EXAD, a framework for designing explanations in adaptive interfaces that considers adaptation type, explanation timing, and user characteristics. Through a mixed-methods study with 42 participants, we investigated how explanation approaches affect trust, understanding, and efficiency across content, layout, and interaction adaptations. Our findings demonstrate that explanation effectiveness varies significantly by adaptation type: Proactive explanations build the highest trust for layout adaptations, while progressive explanations offer the best balance between transparency and efficiency. We extend the EXAD framework to B5G (Beyond Fifth Generation) network systems, enabling context-aware explanations that enhance trust and transparency. Our results show that carefully designed explanation strategies improve user understanding and operational efficiency, and can be translatable in dynamic network environments such as resource allocation based on user patterns in B5G networks. This research bridges theoretical work in explainable AI with practical implementation challenges in adaptive interfaces.

关键词： Human computer interaction Adaptive systems Explainable AI Layout User experience Timing Resource management Robots Optimization Network systems

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Early Diabetes Prediction Through Multiple Machine Learning Models: A Comparative Analysis

Early Diabetes Prediction Through Multiple Machine Learning ...

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AI-Driven Smart Healthcare for Society 5.0

作者： Anuja Sharma G S Pradeep Ghantasala Laishram Bijialuxmi Devi Pradeepta Kumar Sarangi Rajesh Sharma R Akey Sungheetha College of Nursing AIIMS Bilaspur Himachal Pradesh Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Chitkara University College of Nursing Chitkara University Himachal Pradesh India Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

People of every stage of life are affected by diabetes, which has emerged as a significant health issue in modern society. A total of 463 million individuals between the ages of 20 and 79 were estimated to have diabetes in 2019, and projections suggest that figure might rise to 700 million by 2045. Healthcare systems face serious difficulties as a result of this spike, which raises the need for medical treatment, increases disability rates, and causes more early deaths. Diabetes is linked to a number of health problems, including cardiovascular disorders, renal problems, and eyesight loss. Early identification and treatment, however, can lessen or delay these issues. Four ML models are used in this work to categorize and forecast diabetes: DT, XGBoost, LR, and RF. The results indicate that the XGBoost model outperforms the others, achieving an accuracy rate of 96.04%.

关键词： Radio frequency Training Analytical models Accuracy Scalability Medical treatment Machine learning Predictive models Diabetes Faces

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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