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Congress on Smart Computing Technologies, CSCT 2023

作者： Upadhyay, Siddharth Singh, Khushi Suganeshwari, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819780952

Ensuring data privacy and security is a critical challenge in cancer diagnosis, driving the adoption of Federated Learning as a collaborative and privacy-preserving solution for tumour classification. Tumour classification plays a significant role in enabling precise diagnoses and treatments. However, centrally aggregating medical data raises concerns regarding the privacy and security of an individual. To tackle these challenges, we use Federated Learning, which offers a privacy-preserving approach that involves collaborative model training on decentralized data sources. This paper showcases the research contributions and gaps in the field of healthcare data, focusing on the significance of ethics. This survey introduces the concepts of federated learning, emphasizing healthcare studies highlighting the importance of tumor classification in healthcare decision-making. Moreover, this paper discusses the recent advancements in Federated Learning concerning tumor classification, including a wide variety of data such as medical imaging, genetics, and clinical data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Improving Deepfake Detection Using Deep Learning Techniques 9

Improving Deepfake Detection Using Deep Learning Techniques

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Nair, Harsh Ullas Tiwari, Ananya Rahat, Noor Kaliyar, Rohit Kumar Yadav, Ankit School of Computer Science Engineering and Technology Bennett University India

ISBN: (纸本)9798350377972

The widespread use of deepfake technology has created previously unheard-of difficulties for digital media's credibility and authenticity. We describe a unique deepfake detection model that addresses this urgent problem by uti- lizing cutting-edge methods such as DenseNet architecture, Multi-Scale Information (MSI) integration, and visual attention mechanisms. Our approach looks at complex visual signals and patterns present in images to properly distinguish between real and altered information. © 2024 IEEE.

关键词： Contrastive Learning

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ArchSentry: Enhanced Android Malware Detection via Hierarchical Semantic Extraction

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2822-2837页

作者： Wang, Tianbo Liu, Mengyao Li, Huacheng Zhao, Lei Jiang, Changnan Xia, Chunhe Cui, Baojiang Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University School of Computer Science and Engineering Beijing China Wuhan University School of Cyber Science and Engineering Wuhan China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China

Android malware poses a significant challenge for mobile platforms. To evade detection, contemporary malware variants use API substitution or obfuscation techniques to hide malicious activities and mask their shallow semantic characteristics. However, existing research lacks analysis of the hierarchical semantic associated with Android apps. To address this problem, we propose ArchSentry, an enhanced Android malware detection via hierarchical semantic extraction. First, we select entities and their relationships relevant to Android software behavior through the software architecture and represent them using a heterogeneous graph. Then, we structure meta-paths to represent rich semantic information to achieve semantic enhancement and improve efficiency. Next, we design a meta-path semantic selection method based on KL Divergence to identify and eliminate redundant features. To achieve a comprehensive representation of the overall software semantics and improve performance, we construct a feature fusion approach based on Restricted Boltzmann Machines (RBM) and AutoEncoder (AE) during the pre-training phase, while preserving the probability distribution characteristics of various meta-paths. Finally, Deep Neural Networks (DNN) process fusion features for comprehensive feature sets. Experimental results on real-world application samples indicate that ArchSentry achieves a remarkable 99.2% detection rate for Android malware, with a low false positive rate below 1%. These results surpass the performance of current state-of-the-art approaches. © 2004-2012 IEEE.

关键词： Android malware

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Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud

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Digital Communications and Networks 2023年第3期9卷 688-697页

作者： Zhuo Chen Peihong Wei Yan Li College of Computer Science and Engineering Chongqing University of TechnologyChongqing200433China School of Artificial Intelligence Chongqing University of TechnologyChongqing200433China

Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of *** this paper,we study the task scheduling problem in the hierarchically deployed edge *** first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem,blue and then prove the NP-hardness of the *** from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision,we propose a newly designed scheduling policy,named Joint Neural Network and Heuristic Scheduling(JNNHSP),which combines a neural network-based method with a heuristic based *** takes the Sequence-to-Sequence(Seq2Seq)model trained by Reinforcement Learning(RL)as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution,thereby achieving a good balance between the quality and the efficiency of the scheduling ***-depth experiments show that compared with a variety of related policies and optimization solvers,JNNHSP can achieve better performance in terms of scheduling error ratio,the degree to which the policy is affected by re-sources limitations,average service latency,and execution efficiency in a typical hierarchical edge cloud.

关键词： Edge cloud Task scheduling Neural network Reinforcement learning

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Reactive Power Optimization of AC-DC Hybrid System Based on Improved Whale Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2084-2098页

作者： Huang, Xin-Yu Liang, Quan Chen, Xian-Fu Zhan, Chuang-Tao School of Electronic Electrical Engineering Physics Fujian University of Technology Fuzhou350118 China School of Computer Science Mathematics Fujian University of Technology Fuzhou350118 China

With the continuous construction of HVDC, it has gradually become dis-tances and large capacity transmission, as well as one of the main technologies of regional power grid interconnection. The scale of AC-DC hybrid grid continues to grow, bringing new challenges to reactive power balance and voltage stability of power grid. An improved whale optimization algorithm is proposed to solve the reactive power optimization problem of AC-DC hybrid systems. By introducing local neighborhood search mechanism, adaptive threshold and adaptive inertia weight, the local search accuracy, global search ability and convergence speed of the algorithm are improved. The reactive power optimization model is established with the total network loss and node voltage offset as the objective function, and the solution steps are given. An IEEE 22-node system with DC transmission lines is used as an example to simulate the proposed algorithm, and compared with the traditional whale optimization algorithm and genetic algorithm, the results show that the proposed algorithm has good convergence and stability, and can obtain better reactive power optimization effect. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Optimization algorithms

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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ANALYSIS OF TWO ANY ORDER SPECTRAL VOLUME METHODS FOR 1-D LINEAR HYPERBOLIC EQUATIONS WITH DEGENERATE VARIABLE COEFFICIENTS

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Journal of Computational Mathematics 2024年第6期42卷 1627-1655页

作者： Minqiang Xu Yanting Yuan Waixiang Cao Qingsong Zou College of Science Zhejiang University of TechnologyHangzhou 310023China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510275China School of Mathematical Sciences Beijing Normal UniversityBeijing 100875China School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational ScienceSun Yat-sen UniversityGuangzhou 510275China

In this paper,we analyze two classes of spectral volume(SV)methods for one-dimensional hyperbolic equations with degenerate variable *** classes of SV methods are constructed by letting a piecewise k-th order(k≥1 is an integer)polynomial to satisfy the conservation law in each control volume,which is obtained by refining spectral volumes(SV)of the underlying mesh with k Gauss-Legendre points(LSV)or Radaus points(RSV)in each *** L^(2)-norm stability and optimal order convergence properties for both methods are rigorously proved for general non-uniform ***,we discover some very interesting superconvergence phenomena:At some special points,the SV flux function approximates the exact flux with(k+2)-th order and the SV solution itself approximates the exact solution with(k+3/2)-th order,some superconvergence behaviors for element averages errors have been also ***,these superconvergence phenomena are rigorously proved by using the so-called correction function *** theoretical findings are verified by several numerical experiments.

关键词： Spectral Volume Methods L^(2)stability Error estimates Superconvergence

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Predicting Mental Health Issues Indicators in Textual Data Using Enhanced Bidirectional Encoder Representations from Transformers

Predicting Mental Health Issues Indicators in Textual Data U...

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International Conference on Computing and Machine Learning, CML 2024

作者： Verma, Naman Majumder, Madhulika Anbarasi, L. Jani School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819778386

Mental health is a major pillar in the overall health of a human being and allows the person to function well in society to fulfill his/her needs, responsibilities, and desires. Yet according to World Mental Health Report (WHO), more than one out of a hundred deaths is caused by suicide, making it a major cause of death among young individuals. With social media platforms serving as an easily accessible platform for a person of any demographic, age or sex, to voice their thoughts, and read unvalidated opinions about sensitive topics like mental health, boundaries between personal expression and potential threats to mental well-being are constantly becoming blurry. With a certain increase in online discussions regarding mental health among various individuals, it becomes important to explore these linguistic nuances of comments and analyze them for better understanding as well as the prediction of mental state of the individual. Using natural language processing techniques, this study aims to establish the patterns and sentiments conveyed by commentary on social media, to better identify potential indicators of mental health issues. Using a bidirectional representation natural learning model (BERT) and a simple three-layer architecture, a dataset containing over 27 thousand text entries, relating to people with anxiety, depression, and other mental health issues social media interactions, was analyzed. The model achieved an accuracy of 87.68% and 85.45% precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Double-Click: dual-click iteration and dual-branch networks for interactive segmentation 16

Double-Click: dual-click iteration and dual-branch networks ...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Jiang, Mingcheng Wang, Di Ren, Benzhe School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The main aim of Interactive image segmentation is to accurately segment regions of interest by utilizing simple user-provided interactive information. Currently, the most popular training approach involves using the previous segmentation result as supplementary information for the next segmentation process to establish a connection between the two interactions. This iterative training method transfers all the information carried by interactions to the global level, without considering its impact at the local level. This will affect the final segmentation results and the training efficiency. To address this problem, we propose an iterative training strategy called Double-Click to involve cropping the image based on the newly added clicks and then separately feeding the cropped image and the newly added clicks into a smaller branch network to obtain predictions for the local region. Subsequently, the locally segmented result is fused with the previous segmentation result to achieve the final outcome. Then, the refined segmentation results are used to guide the next segmentation, ensuring that more precise results guide subsequent segmentations while maintaining the guidance information of the clicks on the global image. Our training strategy improves the training effectiveness by handling two clicks in an iterative process. The accuracy of the segmentation is ensured through both local and global predictions. Additionally, we designed a dual-branch network to accommodate our proposed training strategy. The effectiveness of this strategy has been tested on four validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results demonstrate that the Double-Click method achieves better performance with fewer interactions compared to previous methods. © 2025 SPIE.

关键词： Semantic Segmentation

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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