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

作者： V Indumathi C. AshokKumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency.

关键词： Accuracy Machine learning algorithms Fault detection Predictive models Nearest neighbor methods Prediction algorithms Boosting Real-time systems Automotive engineering Predictive maintenance

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Secure Federated Learning with Blockchain and Homomorphic Encryption for Healthcare Data Sharing 23

Secure Federated Learning with Blockchain and Homomorphic En...

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23rd International Conference on Cyberworlds, CW 2024

作者： Firdaus, Muhammad Rhee, Kyung-Hyune Pukyong National University Industrial Science Technology Research Center Korea Republic of Telkom University School of Computing Indonesia Pukyong National University Division of Computer Engineering and AI Korea Republic of

ISBN: (纸本)9798331527174

Healthcare data is often fragmented across various institutions due to its highly sensitive and private nature. In this sense, hospitals and clinics maintain electronic health records (EHRs) independently;hence, valuable data is siloed within individual organizations, preventing comprehensive analysis that could benefit from diverse data sources. Federated learning (FL) addresses these challenges by enabling the training of a shared global model using data distributed across multiple institutions without moving the data from its source. By leveraging FL, healthcare institutions can combine their data assets to improve predictive analytics, personalized medicine, and overall healthcare outcomes, ultimately benefiting patients and the healthcare system. However, the current FL model with a central server presents several challenges within healthcare, including the risk of malicious attacks, regulatory compliance, and privacy vulnerabilities. To overcome these issues, this paper introduces the FL framework with blockchain and homomorphic encryption (HE). Our framework aims to minimize the role of the central server, enable collaborative model training across healthcare organizations, and enhance data security and privacy. In this sense, blockchain ensures the integrity and transparency of the process, while homomorphic encryption ensures that the data remains private. This framework can potentially enable institutions to enrich medical knowledge while securely keeping patient data collaboratively and facilitating healthcare analytics in practical settings. © 2024 IEEE.

关键词： Federated learning

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SQL Injection Attacks and Prevention 6

SQL Injection Attacks and Prevention

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6th International Conference on Information Systems and Computer Networks, ISCON 2023

作者： Gaur, Kashish Diwakar, Manoj Gaur, Kaamya Singh, Prabhishek Sachdeva, Tanya Pandey, Neeraj Kumar Graphic Era Deemed to Be University Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India Dit University School of Computing Dehradun248009 India

ISBN: (纸本)9798350346961

SQL Injection Attacks generally target the information stored in the database. Therefore, it becomes necessary for administrators to have mechanisms installed to detect and prevent them. Several mechanisms have been proposed for the same. In this paper, we have chosen some models proposing several SQLI detection and prevention systems, analyzed them and formed an overall comparative analysis of all of them, choosing one that we ascertain comparatively more optimal.. © 2023 IEEE.

关键词： Computer crime

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A Hybrid Unsupervised Clustering-Based Anomaly Detection Method

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Tsinghua science and Technology 2021年第2期26卷 146-153页

作者： Guo Pu Lijuan Wang Jun Shen Fang Dong School of Cyber Engineering Xidian UniversityXi'an 710126China School of Computing and Information Technology University of WollongongNSW 2522Australia School of Computer Science and Engineering Southeast UniversityNanjing 211189China

In recent years,machine learning-based cyber intrusion detection methods have gained increasing *** number and complexity of new attacks continue to rise;therefore,effective and intelligent solutions are *** machine learning techniques are particularly appealing to intrusion detection systems since they can detect known and unknown types of attacks as well as zero-day *** the current paper,we present an unsupervised anomaly detection method,which combines Sub-Space Clustering(SSC)and One Class Support Vector Machine(OCSVM)to detect attacks without any prior *** proposed approach is evaluated using the well-known NSL-KDD *** experimental results demonstrate that our method performs better than some of the existing techniques.

关键词： unsupervised learning clustering intrusion detection feature selection

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HHO-ACO hybridized load balancing technique in cloud computing

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International Journal of Information Technology (Singapore) 2023年第3期15卷 1357-1365页

作者： Sumathi, Muruganandam Vijayaraj, Natarajan Raja, Soosaimarian Peter Rajkamal, Murugesan School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India IBM Karnataka Bangalore India

Due to on-demand requirement of computing resources, cloud computing (CC) is the widely used technology in data storage, software and platform. CC provides everything as a service to requester. In an Infrastructure as... 详细信息

Due to on-demand requirement of computing resources, cloud computing (C_C) is the widely used technology in data storage, software and platform. C_C provides everything as a service to requester. In an Infrastructure as a Service (IaaS), virtual machines (VMs) play a vital role to provide infrastructure to the requester. The performance VMs depends on the distribution of work (load balancing) between VMs. The process of distributing a set of tasks or workloads over a set of VM resources is called as Load Balancing (LB). Unbalanced task distribution leads to overloaded or under-loaded issues and performance degradation. Hence, LB is a desirable and essential task in C_C. To improve the LB performance the two different meta-heuristic optimization algorithms, Harries Hawks Optimization (HHO) and Ant Colony Optimization (ACO) are hybridized in the proposed technique. The hybridized load balancing (HLB) algorithm performance is compared to HHO and ACO. The factors that are analyzed in the proposed system are the average waiting time (A_WT), average execution time (A_ET), average response time (A_RT), make-span, throughput analysis, turnaround time and LB time. The HLB mechanism is implemented and simulated in java and Cloudsim respectively. By the allocation of workloads to VMs will shows which one gives the best efficiency in LB among the VM’s. The proposed HLB technique takes 9.29% A_ET, 2.65% of A_RT, 0% of A_WT, less turnaround time and LB time than the basic HHO and ACO algorithms. Thus, the proposed HLB provided better performance than HHO and ACO is proven with different performance metrics. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Ant colony optimization Cloud computing Harries haws optimization Hybridized load balancing Virtual machine

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Infrastructure Development of Non DHT-Based Pyramid Tree Network Architecture 38th

Infrastructure Development of Non DHT-Based Pyramid Tree Net...

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38th International Conference on Computers and Their Applications, CATA 2023

作者： Roy, Indranil Rahimi, Nick Gupta, Bidyut Debnath, Narayan Department of Computer Science Southeast Missouri State University Cape GirardeauMO United States School of Computing Sciences & Computer Engineering University of Southern Mississippi HattiesburgMS United States School of Computing Southern Illinois University CarbondaleIL United States School of Computing and Information Technology Eastern International University Viet Nam

In this paper, we have considered a recently reported 2-layer non-DHT-based structured P2P network. It is an interest-based system and consists of different clusters such that peers in a given cluster possess instances of a particular resource type. It offers efficient data look-up protocols with low latency. However, the architecture lacks in one very important aspect: it is assumed that no peer in any cluster can have more than one resource type, and this could be a very hard restriction practically. This is true for all interest-based works existing in the literature. Therefore, in the present work, we have addressed this issue of generalizing the architecture to overcome this restriction and so far, have come up with some significant initial results. Work is being on to complete the generalization process. We have identified some of our previously reported data look-up protocols that will need to be modified in order to accommodate the new findings toward the generalization and while doing so, we aim at keeping the data look-up latencies of these probable modified protocols unchanged. In addition, our objective is to consider security in communication in the generalized architecture as well. To achieve it, we aim at using mainly public key-based approach for the different look-up protocols reported earlier, because results obtained so far in this direction indicate that the required number of public-private key pairs will be much smaller than the number of symmetric keys if symmetric key-based approach is used. © 2023, EasyChair. All rights reserved.

关键词： Network architecture

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Towards Improving the Anti-attack Capability of the RangeNet++ 16th

Towards Improving the Anti-attack Capability of the RangeNet...

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16th Asian Conference on Computer Vision , ACCV 2022

作者： Zhou, Qingguo Lei, Ming Zhi, Peng Zhao, Rui Shen, Jun Yong, Binbin School of Information Science and Engineering Lanzhou University Tianshui South Road 222 Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW Australia

ISBN: (纸本)9783031270659

With the possibility of deceiving deep learning models by appropriately modifying images verified, lots of researches on adversarial attacks and adversarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are our primary concerns in this paper. Aiming at the point cloud segmentation model RangeNet++ in the field of autonomous driving, we propose novel approaches to improve the security and anti-attack capability of the RangeNet++ model. One is to calculate the local geometry that can reflect the surface shape of the point cloud based on the range image. The other is to obtain a general adversarial sample related only to the image itself and closer to the real world based on the range image, then add it into the training set for training. The experimental results show that the proposed approaches can effectively improve the RangeNet + +’s defense ability against adversarial attacks, and meanwhile enhance the RangeNet++ model’s robustness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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An Effective Method for Distributing Workloads in Smart City using Sensor Networks Enabled by Fog

An Effective Method for Distributing Workloads in Smart City...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Nithiyanandam Velvizhi, R. Priyadharshini, S.P. Poongavanam, N. SRM Institute of Science and Technology Kattankulathur Campus Department of Computing Technologies Chengalpattu Kattankulathur India Sathyabama Institute of Science and Technology OMR Department of Computer Science and Engineering Chennai India St' Joseph College of Engineering OMR Department of Artificial Intelligence and Machine Learning Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

By linking sensor nodes to fog nodes in critical locations, the Distributing Workloads in Fog Enabled Smart City (DW-FESC) technique efficiently handles the problem of distribution of workloads in smart cities. To get over the processing limits of IoT devices, this setup makes sure that complicated tasks are sent off to neighbouring fog nodes. The plan uses a load balancer and an updated greedy algorithm to evenly distribute work, which speeds up execution and keeps loads from becoming too heavy. Additionally, it has a deadline-based task execution mechanism that makes sure the most important jobs are done on time. The simulation results show that DW-FESC keeps the variance from the mean execution times to a minimum while considerably improving task execution times in comparison to other methods. With the use of fog computing, DW-FESC improves the dependability, efficiency, and responsiveness of smart city applications, leading to greener city life. © 2024 IEEE.

关键词： Fog computing

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Recognition of De Novo Drug Design for Targeted Chemical Libraries through Optimization Techniques 5

Recognition of De Novo Drug Design for Targeted Chemical Lib...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Balamurugan, K.S. Navaneethakrishnan, Sundara Rajulu Srinivasulu, Senduru Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli Trichy India Sathyabama Institute of science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Jeppiaar Institute of Technology Department of Electronics and Communication Engg. Tamilnadu Sriperumbudur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331504403

The process of De Novo drug design involves the integration of artificial intelligence with computational intelligence techniques. The proposed system aims to provide precision therapeutics through the development of targeted chemical libraries designed for individual patient's needs. They also highlight the potential impact of precision medicine. The traditional approach involves trial and error methods in the combination of various chemicals for desired drug discovery that leads to the waste of time and energy. These challenges are overcome through the aid of artificial intelligence techniques. The interaction of molecules, physiochemical properties, and biological interaction of potential drugs are obtained through the aid of optimization algorithms. With the integration of computational intelligence, the design structure becomes more adaptable. This helps in the formation of chemically diverse compound libraries. The important aspect of the proposed system involves the ability of advanced precision therapeutics. The clinical information is necessary for the identification of molecular targets achieved through the aid of optimization algorithms. These obtained targets are necessary for the development of new drugs with high affinity and specificity. This helps in minimizing the off-target effects which improves the therapeutic efficacy. The novel molecular structure is generated using the Generative Adversarial Networks (GAN). The compound properties are optimized using reinforcement and an iterative learning process. The simpler structure of drug-like molecules is obtained through an evolutionary algorithm. Thus the proposed system provides rapid exploration of chemical space that leads to the formation of chemical compounds with personalized pharmacological profiles. © 2024 IEEE.

关键词： Drug discovery

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An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

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Computer Systems science & engineering 2023年第8期46卷 1563-1576页

作者： R.Bala Krishnan D.Yuvaraj P.Suthanthira Devi Varghese S.Chooralil N.Rajesh Kumar B.Karthikeyan G.Manikandan srinivasa ramanujan centre sastra deemed universitykumbakonamtamil naduindia department of computer science cihan university–duhokkurdistan regioniraq department of information technology st.joseph’s college of engineeringchennaitamil naduindia department of computer science&engineering rajagiri school of engineering&technologykakkanadkeralaindia school of computing sastra deemed universitythanjavurtamil naduindia

With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission *** steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before *** of the techniques mentioned above aid in the confidentiality of feature *** proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red,Green,and *** private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the *** K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image *** content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit(LSB)substitution technique to build the secret Content embedded image known as the stego image,which is subsequently transmitted across the internet medium to the server *** experimental results are computed using the inputs from“Open-Access Medical Image Repositories(***)”and demonstrate the scheme’s impudence as the Content concealing procedure progresses.

关键词： Contouring secret content embodiment least significant bit embedding medical data preservation secret content congregation pixel clustering

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