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2024 IEEE International Conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Shali, Hong Rana, Muhammad Ehsan Abdul Hameed, Vazeerudeen Tripathy, Hrudaya Kumar Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331508432

The music industry has undergone a profound transformation in recent decades, driven by technological advancements. The advent of digital music, streaming services, and social media has significantly altered the way music is created, distributed, and consumed. In this context, cloud computing has emerged as a powerful force, reshaping the landscape of the industry. This paper presents a comprehensive analysis of the integration of cloud computing into the music industry and its subsequent impact. The authors examined how advancements in cloud technology are revolutionizing music production, consumption, and distribution, enabling the emergence of innovative business models. The scalability and flexibility offered by cloud computing have empowered small IT product manufacturers to rapidly launch their services and bring their business ideas to fruition. Consequently, the music industry has witnessed a substantial shift, recognizing cloud computing as a key driver of business model innovation. Furthermore, the convergence of cloud computing with mobility, social media, and big data has not only transformed daily life but has also revolutionized business practices within the music industry. © 2024 IEEE.

关键词： Cloud platforms

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Multi Head Graph Attention for Drug Response Predicton 3

Multi Head Graph Attention for Drug Response Predicton

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Selvi Rajendran, P. Sivannarayna, M. Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9781665464871

Precision medicine is based on curing diseases based on a patient's genetic profile, lifestyle, and environmental factors. This method improves clinical trial success rates and speed up drug regulatory approval. Predicting tumour vulnerability to specific anti-cancer therapy is critical for the successful implementation of precision medicine. Drug combinations have been shown to be very effective in cancer treatment to lower the drug resistance and improve the therapeutic effectiveness. The experiments carried out in all these therapeutic combinations have become expensive and time-consuming as a result of the increasing number of anti-cancer drugs. Large-scale drug response testing on cancer cell lines might help to understand the way drugs react with cancer cells. This study proposes a multi head graph attention network to perform drug response prediction. © 2023 IEEE.

关键词： Diseases

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A New Method to Detect the Dissimilarity in the Blood Flow of Both Carotid Arteries Using Photoplethysmography 3rd

A New Method to Detect the Dissimilarity in the Blood Flow o...

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The 3rd International Conference on Frontiers in computing and Systems (COMSYS 2022)

作者： Shakya, Kshitij Chowdhury, Shubhajit Roy School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi175005 India

ISBN: (纸本)9789819926794

This paper describes a method for detecting the change in blood flow profile in the left and right common carotid artery to detect any hindrance, possibly a plaque in the blood flow. A device was made which extracts the PPG signals from the carotid arteries over the neck skin and compares the results between the two. For the case where the growth of plaque is assumed to be different for both carotid arteries, which is generally the case, this method suits best. The least complex and portability of the device made allows users to perform this method easily and inexperienced persons can easily be trained to do so. An all-purpose PPG device was designed to extract PPG signals from various sites on the body with this one device. Two such identical devices were made to be used to get reference and target signals from their respective sites to draw some promising outcomes from it. The modes of application of device have been described in the later part of the paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Photoplethysmography

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Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature 39

Breaking Barriers in Physical-World Adversarial Examples: Im...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yichen Chou, Yuxuan Zhou, Ziqi Zhang, Hangtao Wan, Wei Hu, Shengshan Li, Minghui National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEs. Based on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method's superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method's effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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A Reliable Framework for Detection of Smart Contract Vulnerabilities for Enhancing Operability in Inter-Organizational Systems

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Journal of Mobile Multimedia 2024年第2期20卷 411-433页

作者： Arunprasath, S. Suresh, A. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chengalpattu Tamil Nadu Chennai 603203 India

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement's terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain. © 2024 River Publishers.

关键词： blockchain technology Ethereum smart contracts genetic algorithms Inter-organizational systems isolation forest smart contract vulnerability detection vulnerability detection

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Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

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Computers, Materials & Continua 2024年第8期80卷 2579-2609页

作者： Yangrong Chen June Li Yu Xia Ruiwen Zhang Lingling Li Xiaoyu Li Lin Ge School of Computer Science Zhengzhou University of AeronauticsZhengzhou450046China Henan Xin’an Century Technology Co. Ltd.Henan Data Security Engineering Technology Research CenterZhengzhou450046China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control *** the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication *** grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for ***,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for ***,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security ***,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal *** index system contains 18 indicators in 3 ***,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and *** higher the level,the greater the security ***,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by *** results show that calculated security risk level based on the proposed assessment method are consistent with actual ***,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

关键词： Smart grid intelligent electronic device security assessment abnormal behaviors network traffic running states

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Generative Adversarial Networks for Self-Supervised Transfer Learning in Medical Image Classification 5th

Generative Adversarial Networks for Self-Supervised Transfer...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kumar, Rohit Yogeetha, B.R. Savita Mehta, Deepak School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Self-supervised transfer mastering for clinical picture analysis is a method that uses deep getting-to-know procedures to research large units of medical imaging facts without using guide labels. By way of using switch getting to know, the version can come across diffused patterns from the facts that are not easily located with the aid of the medical doctors or researchers. This method can diffuse clinical imaging packages consisting of type, segmentation, and item detection. The self-supervised transfer studying technique involves educating an artificial intelligence (AI) model on a set of scientific picas with available labels. Further, the model can detect small capabilities and patterns that may be ignored through manual labeling, leading to more excellent correct outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Integrating AI into Software engineering: A Critical Review and Future Directions

Integrating AI into Software Engineering: A Critical Review ...

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

作者： Aditya Pai, H. Sharath, K.R. Babalad, Kavita Guru Prasad, M.S. Chandrappa, S. Kumar, Pratik Mit School of Computing Mit Art Design and Technology University Pune India Department of Computer Science and Engineering Dehradun India School of Computing and Information Technology Reva University Bangalore India Bengaluru India Department of Computer Applications Dehradun India

ISBN: (纸本)9798331527495

The field of Artificial Intelligence (AI) has been witnessing a huge demand in the field of research, tools development, and applications of deployment. There are multiple software companies which are shifting their focus to developing AI systems, many are also deploying the AI paradigms to their current system. Correspondingly, the academic research community has also used AI paradigms to solve the traditional software engineering problems. AI has become very useful to the software engineering community. The paper also uses a survey of the most commonplace methods of using AI into the Software engineering, that covers the methods for SE phases such as Requirements, Design, Development, Testing, Release, and Maintenance. The paper also aims in answering to the sufficient intelligence in the SE lifecycle, this evaluates the overlapping of the SE and AI domains, and challenge the current conventional wisdom of the state-of-the-art. © 2025 IEEE.

关键词： Software testing

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