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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Narayanan, K. Lakshmi Naresh, R. SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering Chennai Tamilnadu Chengalpattu603 203 India SRM Institute of Science and Technology Kattankulathur Department of Networking and Communications Chennai Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9781665475174

Cloud computing is a newly developing concept in the area of computer technology. In one data centre, the data has traditionally been kept and managed. However, cloud computing nowadays offers computation as a service. It has the benefits of optimization, dependability, superior efficiency, and being a comparatively low-cost feasible solution compared to dedicated infrastructures. It expected to grow in the future. In this case, the system administrator will first encrypt the usable index using the ElGamal cryptosystem, which will then use to protect the data. Therefore, depending on the service, extensive data transferred over the network and this enormous amount of data retained in cloud service centers. The result introduces a new method of encrypting data known as batch key generation, which is a unique idea since it enables certain operations to done on the encrypted files, it is used to analysis of security and in which two parties exchange a large number of keys to enhance security. ElGamal variants divided into two kinds based on the method of creating signatures used in producing them. Afterwards, we build a general process that converts a collection of ElGamal variations into their threshold versions, and we demonstrate that the schemes we construct are unforgeable. In this article, we present a key encryption method for enhancing the privacy and security of information kept in the cloud while also reducing storage costs. © 2023 IEEE.

关键词： Cloud storage

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Asymptotic Estimation Performance of Linear Regression Model with Sparse Bayesian Learning as Both Samples and Signals Approach Infinity

Asymptotic Estimation Performance of Linear Regression Model...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Kazuaki Murayama Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

This paper considers sparse Bayesian learning (SBL) in the linear regression model used in signal processing. An estimation performance of the method is analyzed in the asymptotic case where the sample size and the number of regression coefficients both increase. This subject has two cases: one is the dependence of performance on the hyperparameters in prior distribution and the other is how the performance depends on the sample size and the number of nonzero components in regression coefficients. To address this subject, we employ methods of statistical mechanics in physics. An equivalent of thermodynamic potential is calculated, following some similarities between Bayes inference and statistical mechanics. It is known in physics that the potential provides physical quantities. In a similar way, the equivalent of potential provides us with the asymptotic evaluation of estimation performance instead of physical quantities. The estimation performance in the two cases appears to have sharply divided regions in which the estimation succeeds and fails. These results are compared with related studies.

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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computer Systems Science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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Deadlock-Free Method for Multi-Agent Pickup and Delivery Problem Using Priority Inheritance with Temporary Priority

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Procedia computer Science 2022年 207卷 1552-1561页

作者： Yukita Fujitani Tomoki Yamauchi Yuki Miyashita Toshiharu Sugawara Department of Computer Science and Communications Engineering Waseda University Tokyo 1698555 Japan

This paper proposes a control method for the multi-agent pickup and delivery problem (MAPD problem) by extending the priority inheritance with backtracking (PIBT) method to make it applicable to more general environments. PIBT is an effective algorithm that introduces a priority to each agent, and at each timestep, the agents, in descending order of priority, decide their next neighboring locations in the next timestep through communications only with the local agents. Unfortunately, PIBT is only applicable to environments that are modeled as a bi-connected area, and if it contains dead-ends, such as tree-shaped paths, PIBT may cause deadlocks. However, in the real-world environment, there are many dead-end paths to locations such as the shelves where materials are stored as well as loading/unloading locations to transportation trucks. Our proposed method enables MAPD tasks to be performed in environments with some tree-shaped paths without deadlock while preserving the PIBT feature; it does this by allowing the agents to have temporary priorities and restricting agents’ movements in the trees. First, we demonstrate that agents can always reach their delivery location without deadlock. Our experiments indicate that the proposed method is very efficient, even in environments where PIBT is not applicable, by comparing them with those obtained using the well-known token passing method as a baseline.

关键词： Multi-agent systems Pickup delivery problem Priority inheritance

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Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud 2

Watermarking and Encryption for Electronic Medical Records (...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

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Reducing Efficiency Degradation Due to Scheduled Agent Suspensions by Task Handover in Multi-Agent Cooperative Patrol Problems 34th

Reducing Efficiency Degradation Due to Scheduled Agent Suspe...

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34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021

作者： Tsuiki, Sota Yoneda, Keisuke Sugawara, Toshiharu Department of Computer Science and Communications Engineering Waseda University Tokyo1698555 Japan

This paper proposes a method to mitigate the significant performance degradation due to planned suspensions in the multi-agent cooperative patrol problem. In recent years, there has been an increased demand to utilize multiple intelligent agents that control robots. Furthermore, cooperation between multiple agents is required for performing tasks that are complex and/or cover large spaces. However, since robots are machines, they must be periodically inspected or replaced with new ones to prevent unintended breakdowns for continuous operation and to prolong the lifetime of agents as much as possible. However, such suspension of agents for inspection can cause a sudden deterioration in performance, which is not ignorable in some applications. Meanwhile, such suspensions are usually planned;thus, we can know in advance which agents will stop, and when, to anticipate a preparation period before the actual suspension time. Thus, we introduce a negotiation method in which the agents that are scheduled to be suspended hand over some responsible and important tasks to other agents to reduce the impact of a sudden performance degradation. The experimental results show that the proposed method considerably reduces the performance degradation, especially for security patrol applications. © 2021by the authors. All rights reserved.

关键词： Multi agent systems

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Anomaly Transmitter Recognition and Tracking

Anomaly Transmitter Recognition and Tracking

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IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： Tianyi Zhao Shamik Sarkar Yuan Tian Danijela Cabric Electrical and Computer Engineering Department University of California Los Angeles USA Department of Electronics and Communications Engineering Indraprastha Institute of Information Technology Delhi India

ISBN: (数字)9798350317640

ISBN: (纸本)9798350317657

Device authentication and identification are important to ensure security in spectrum access and management. While prior works have studied radio frequency fingerprinting for such purpose and demonstrated its effectiveness, most works have been focusing on closed-set classification, where only a known set of authorized transmitters appears to be identified. However, unauthorized transmitters can also exist in a designated band. Therefore, recognizing and tracking those unauthorized transmitter behaviors is necessary to protect spectrum security. This work investigates this problem and proposes an Anomaly Recognition and Tracking (ART) framework. The ART framework first learns a closed-set classifier between the authorized transmitters and modifies the classifier to detect anomaly signals. Then, the framework collects the detected anomaly signals and performs unsupervised clustering to assign labels to the predicted anomaly transmitters. Finally, the framework incrementally learns the anomaly transmitter features with limited signals and updates the classifier accordingly. We evaluate our proposed framework with a WiFi dataset and show that with 10 % false alarm rate, it can detect more than 99 % anomaly signals. Moreover, our proposed framework can distinguish between and learn the features of different anomaly transmitters with as few as 10 signals received from each anomaly transmitter. Finally, the framework can update the classifier, track the anomaly transmitters, and classify among all authorized and anomaly transmitters with more than 99 % accuracy.

关键词： Accuracy Heuristic algorithms Radio transmitters Subspace constraints Clustering algorithms Fingerprint recognition Robustness

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An Intelligent Approach for Intrusion Detection in Industrial Control System

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computers, Materials & Continua 2023年第11期77卷 2049-2078页

作者： Adel Alkhalil Abdulaziz Aljaloud Diaa Uliyan Mohammed Altameemi Magdy Abdelrhman Yaser Altameemi Aakash Ahmad Romany Fouad Mansour Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia Applied College University of Ha’ilHa’il81481Saudi Arabia College of Education New Valley UniversityEl-Kharga72511Egypt College of Art University of Ha’ilHa’il81481Saudi Arabia School of Computing and Communications Lancaster UniversityLeipzig04109Germany College of Science New Valley UniversityEl-Kharga72511Egypt

Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based *** recent years,there has been a lot of focus on the security of industrial control *** to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically *** they are so inextricably tied to human life,any damage to them might have devastating *** provide an efficient solution to such problems,this paper proposes a new approach to intrusion ***,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning ***,a prior estimation of the class is proposed based on a support vector *** results show that the proposed approach has better anomaly detection performance than existing algorithms.

关键词： Industrial control system anomaly detection intrusion detection system protection

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Real Time Conversion of American Sign Language to text with Emotion using Machine Learning 6

Real Time Conversion of American Sign Language to text with ...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Jamwal, Aryan Vasukidevi, G. Malleswari, Tyj Naga Vijayakumar, T. Chandra Sekhar Reddy, L. Gupta, Amara S A L G Gopala Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Science and Humanities Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore India Cmr College of Engineering & Technology Department of Computer Science and Engineering Telangana Kandlakoya India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Auditory impairment or hearing loss is a major problem in today's human population. Sign language helps hearing-impaired people to live their social life without much difficulty. The latest technology used in detecting sign language connects them to the rest of the world. Sign language recognition and conversion to the text based on the movement of hands and the shape formed by the fingers is a complex system. The solutions using machine learning give significant success for this complex system. This paper mainly focuses on developing a system for recognizing the different hand signs in American Sign Language and their emotions simultaneously in real-time and converting them into text. The system resolves the need for a translator to bridge the gap between a sign language based user and a non-sign language-based user. Most state-of-the-art technology involves CNN models with image pixels by identifying specific key point coordinates on the face/hand obtained. Using the latest technologies like MediaPipe, an improved CNN model is developed based on the distances between each unique identified vital point. The customized model able to achieve 80 percentage of accuracy for the live image. © 2022 IEEE.

关键词： computer vision

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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