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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Parikshit N. Mahalle V.P. Arul Kumar Ansh Kataria S Srividhya Sudhanshu Dev H Malathi Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Information Technology Karpagam Institute of Technology Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Biotechnology and Genetics JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Multiple healthcare devices are integrated into an IoMT network to enhance monitoring of patients and real-time treatment. These networks make crucial health care choices using many devices. To protect these devices and networks, researchers have used encoding, hashing, confidentiality safeguarding, attribute-based admittance controller, and more. However, continuous monitoring security frameworks are complicated or unchangeable. Present methods’ security relies on their interior setup, which is seldom extendable for new assaults. This explore recommends a hybrid metaheuristic approach for health care IoT safety. Changing encryption and hashing standards may dynamically rearrange the blockchain paradigm. EHO and GWO are used to continually optimize blockchain-based IoMT implementation security and QoS performance. In the proposed paradigm, dual fitness functions increase safety and QoS for numerous attacks. Fitness functions adjust encryption and hashing settings to enhance performance against various attacks. Blockchain-based deployment may be scaled and used in real time by integrating EH and GW optimization models for dynamic attack situations. The prototypical is evaluated beneath masquerade, Sybil, MITM, and DDoS assaults and associated to leading models.

关键词： Biological system modeling Computational modeling Metaheuristics Medical services Quality of service Throughput Real-time systems Safety Security Monitoring

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Self-Replicating Imperative Node Evaluator for Improved Network Intrusion Detection

Self-Replicating Imperative Node Evaluator for Improved Netw...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： B.S. Babitha Chitrakant O. Banchhor K.S. Bhuvaneshwari Saniya Khurana K.K. Senthilkumar Shuthanshu Marwaha School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Cyber-attacks occur when someone attempts to gain unauthorized access to another person's or company's computer network. Network resources are considered compromised when several activities are made that potentially jeopardize their availability, anonymity, or reliability. Any time a system that detects intrusions notices questionable activity on an organization or system, for example, a potential breach of security policy, it notifies a management station. It is hard to efficiently identify threats in real-time due to the massive amount of data sent via cyber networks. The focus of this study is on developing an AI network for the purpose of detecting cyber threats. Checking user behavior for anomalies is an excellent way to spot fraud. When it comes to cyber defense, one of the biggest mysteries is how systems can detect previously unseen vulnerabilities and take action to mitigate or eradicate them. In order to stop cyber assaults, new cyber security measures are needed to identify malicious nodes before they interact. This study introduces an auto-triggering mode architecture and a replicating itself code for an aggressive node evaluator. As soon as it detects an intrusion, it may fix the network immediately, securing it and reducing the number of false alerts. The proposed model has a 98% success rate in both intruder identification and self-replication. Though progress has been made, there is still room for enhancement in reducing the number of false alarms.

关键词： Technological innovation Codes Accuracy Signal processing Real-time systems Security Servers artificial intelligence Time complexity Cyberattack

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network

arXiv

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arXiv 2024年

作者： Qiu, Yangyang Xu, Guoan Gao, Guangwei Guo, Zhenhua Yu, Yi Lin, Chia-Wen The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia The Tianyijiaotong Technology Ltd. Suzhou215100 China The Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8511 Japan The Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, the integration of the local modeling capabilities of Convolutional Neural Networks (CNNs) with the global dependency strengths of Transformers has created a sensation in the semantic segmentation community. However, substantial computational workloads and high hardware memory demands remain major obstacles to their further application in real-time scenarios. In this work, we propose a lightweight multiple-information interaction network for real-time semantic segmentation, called LMIINet, which effectively combines CNNs and Transformers while reducing redundant computations and memory footprint. It features Lightweight Feature Interaction Bottleneck (LFIB) modules comprising efficient convolutions that enhance context integration. Additionally, improvements are made to the Flatten Transformer by enhancing local and global feature interaction to capture detailed semantic information. The incorporation of a combination coefficient learning scheme in both LFIB and Transformer blocks facilitates improved feature interaction. Extensive experiments demonstrate that LMIINet excels in balancing accuracy and efficiency. With only 0.72M parameters and 11.74G FLOPs, LMIINet achieves 72.0% mIoU at 100 FPS on the Cityscapes test set and 69.94% mIoU at 160 FPS on the CamVid test dataset using a single RTX2080Ti GPU. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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DEPS: AN IMPROVED DEEP LEARNING MODEL FOR DE NOVO PEPTIDE SEQUENCING

arXiv

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arXiv 2022年

作者： Ge, Cheng Lu, Yi Qu, Jia Xie, Liangxu Wang, Feng Zhang, Hong Kong, Ren Chang, Shan Institute of Bioinformatics and Medical Engineering School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou University Changzhou213164 China School of Mathematics China University of Mining and Technology Xuzhou221116 China

De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetative models. In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing even with missing signal peaks or large number of noisy peaks in tandem mass spectrometry data. It is showed that, for the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino acid precision and peptide recall respectively. Furthermore, the results suggested that DePS outperforms DeepNovoV2 on the cross-species dataset. © 2022, CC BY.

关键词： Amino acids

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Advanced Battery Management for Electric Vehicles: A Deep Dive into Estimation Techniques Based on Deep Learning for the State of Health and State of Charge of Lithium-Ion Batteries

Advanced Battery Management for Electric Vehicles: A Deep Di...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Saad El Fallah Jaouad Kharbach Jonas Vanagas Živile Vilkelytė Sonata Tolvaišienė Ghita Ikmel Assia Redouani Saloua Lagnaoui Khaoula Boumais Mohammed Ouazzani Jamil Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fez Morocco Université Sidi Mohamed Ben Abdellah Faculté des Sciences Dhar El Mahraz Laboratoire de Physique de Solide Fez Morocco Dept. Electrical Engineering Vilnius TECH Vilnius Lithuania Signals Systems Components Laboratory (LSSC) Faculty of Science and Technology Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Artificial Intelligence Data Science and Emerging Systems (IASSE) ENSA Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

The precision of state of charge (SoC) prediction prediction of the SoC is necessary to avoid deep discharging and remains an important challenge in the field of electric vehicles and overcharging, which can damage batteries and shorten their life. the energy storage industry. The SoC is the percentage of energy The degradation of lithium-ion batteries is a key area of research, available in a battery relative to its total capacity. A precise as these type of batteries are extensively employed in many sectors, in particular electric vehicles, electronic devices and renewable energies. Evaluation systems based on deep learning, like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are becoming increasingly popular for their ability to process complex data and make accurate predictions. As lithium-ion batteries degrade over time, their reliability in storing and delivering energy is diminishing. To effectively monitor and manage this degradation, researchers are turning to evaluation systems based on deep learning. These approaches enable the prediction of battery state of health (SoH) and SoC, making it easier to optimize battery use and extend battery life. This article presents various techniques for predicting the SoH and SoC of batteries to evaluate the degradation of cells.

关键词： Lithium-ion batteries Degradation Deep learning Recurrent neural networks Estimation Battery management systems Electric vehicles

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Multi-Path Transmission of Real-Time Remote Sensing data via Heterogeneous LEO Inter-Satellite-Links

Multi-Path Transmission of Real-Time Remote Sensing Data via...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Binquan Guo Zehui Xiong Dusit Niyato Shiwen Mao Zhu Han Schocl of Telecommunication Engineering Xidian University Xi'an P. R. China Tianjin Artificial Intelligence Innovation Center (TAIIC) Tianjin P. R. China Pillar of ISTD Singapore University of Technology and Design Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Auburn University Auburn AL USA Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Real-time transmission of remote sensing data is critical for many applications. The rapid enhancement in sensing capabilities has posed challenges in sending large volumes of collected information to users on the ground in a timely manner. Deployed with inter-satellite links (ISLs), low-earth-orbit mega-constellations can facilitate the relay of remote sensing data to ground stations. However, due to scarce spectrum resources, increasing data throughput remains a challenge. Towards this, we explore multi-path transmission of real-time remote sensing data via heterogeneous ISLs, with the aim of maximizing data transmission throughput. We formulate the problem of multi-path transmission via heterogeneous ISLs as a linear program-ming problem, and find that its complexity is exacerbated when dealing with large-scale satellite networks. Therefore, we present an alternative approach using graph theory, transforming the problem into a classical maximum flow problem. This approach ensures optimal resolution of the original problem within polynomial time. Simulations conducted on the Gaofen and Starlink constellation demonstrate the performance benefits of our approach regarding both time complexity and throughput.

关键词： Satellites Low earth orbit satellites Throughput Routing Real-time systems Polynomials Transceivers Sensors data communication Remote sensing

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Optimization of Precoders for MIMO System for 6G Communication

Optimization of Precoders for MIMO System for 6G Communicati...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： J. Sumithra V.N.Senthil Kumaran Bhavani G G. Ramya Uma Maheswari S S. Saranya Department of Electronics and Communication Engineering RMD Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tiruchirappalli Tamil Nadu India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Biosciences Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai Tamil Nadu India Department of Artificial intelligence and data science Jeppiaar institute of technology Chennai Tamil Nadu India

The goal of this work is to improve the efficiency and effectiveness of a dual-functional communication and radar system. It uses a combination of novel precoder techniques to achieve both the data rate and target detection requirements of its users. A comprehensive evaluation of the performance indicators of the system for radar detection and communication is performed. The optimal power allocation techniques for the beams are then determined using the derived results. The results of the analysis are then validated through computer simulations. The developed precoders are then compared with the benchmark approaches and achieved a siginficant improvement in sum rate and energy efficiency.

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Fractal video compression for IOT-based smart cities applications using motion vector estimation

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Measurement: Sensors 2023年 26卷

作者： Jang Bahadur Saini, Dilip Kumar Kamble, Shailesh D. Shankar, Ravi Kumar, M. Ranjith Kapila, Dhiraj Tripathi, Durga Prasad de, Arunava Department of Computer Science and Engineering Himalayan School of Science & Technology Swami Rama Himalayan University Uttarakhand India Department of Artificial Intelligence and Data Science Indira Gandhi Delhi Technical University for Women Delhi India Department of Electronics and Communication Engineering SR University Warangal India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Computer Science & Engineering Lovely Professional University Punjab Phagwara India Department of Electronics & Communication Koneru Lakshmaiah Education Foundation Andhra pradesh Vaddesearam522302 India Department of Electronics & Communication Asansol Engineering College West bangal Kanyapur India

Recently, Internet of the things (IoTs) devices have been used highly in surveillance applications in smart environments, cities, and buildings. Most of the surveillance data recorded by the IoT devices is in the form of video and needs to be transmitted to the admin or server by video streaming technology. Fractal video compression uses a self-similarity concept for video compression, meaning the fractal image contains self-similarity of itself that is explained and denoted by change. Computational cost is more in fractal video coding;different methods have been developed to reduce this computational cost. The proposed work's main objective is to implement a video streaming approach by combining the Diamond-search-pattern- block-matching motion estimation algorithm and hash-based fractal video compression algorithm to reduce the encoding time. It improves both the delay and security of the block matching system and the developed approach measures based on evaluation parameters: Peak signal-to-noise ratio (PSNR), encoding time, decoding time, Mean squared error (MSE), and Compression ratio for Smart City Applications. Compared to other algorithms, the IoTs video data is streamed efficiently in a smart city to analyse the abnormalities. © 2023 The Authors

关键词： Internet of things

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Expression of Concern for: A Novel Dynamic Watermarking for Secure data Protection from Cyber Theft Based on artificial intelligence Supervision

Expression of Concern for: A Novel Dynamic Watermarking for ...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： Ravikiran Madala R. Monica Sulakshana Sagar Malwade Selvakumaran. S Shilpa Jivan Budhavale N. Sathyabalaji Information Technology University of Cumberlands Durham North Carolina United States Department of Electronics and Communication Engineering Nandha Engineering College Erode TamilNadu India Department of Computer Engineering Dr.Vishwanath Karad School of Polytechnic and Skill Development Pune Maharashtra India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai TamilNadu India Department of Computer Engineering Dr. Vishwanath Karad School of Polytechnic and Skill Development Pune Maharashtra India Department of Computer Science and Engineering JKK Munirajah College of Technology Erode TamilNadu India

ISBN: (数字)9798350347791

ISBN: (纸本)9798350347807

One of the centre innovations of the Fourth Modern Insurgency (otherwise called Industry 4.0) is artificial intelligence (computer based intelligence), which can be utilized to shield Web associated gadgets from dangers, assaults, harm, and undesirable access. The current range of cyber security issues can be deftly handled by utilising well-known simulated intelligence techniques such as AI and profound learning procedures, the possibility of normal language handling, information portrayal and thinking, as well as the possibility of information or rule-based master frameworks displaying. In view of these man-made intelligence strategies, we give an exhaustive examination of "Simulated intelligence driven cyber security" in this article, which may be helpful for clever cyber security government agencies and the board. The cyber security registration method can be made more computerised and intelligent than the standard security frameworks by the security intelligence exhibiting in light of such simulated intelligence ***, we present various choices for future examination that fall inside the domain of our examination. At last, this paper's primary objective is to give direction and references to industry specialists in the field of cyber security, especially from a canny registering or simulated intelligence based specialized viewpoint.

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