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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Sharma, Manju Sharma, Monika Lavanya Sharma, Pramod Gurpur, Shashikala Taqui, Syed Noeman University Institute of Engineering and Technology Maharshi Dayanand University Department of Computer Science and Engineering Haryana Rohtak124001 India The Technological Institute of Textile and Sciences Department of Computer Engineering Haryana Bhiwani127021 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Viruthunagar626126 India University of Technology School of Engineering & Technology Rajasthan Jaipur303903 India Viman Nagar Maharashtra Pune411014 India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350389609

Recently, the most prominent study areas are Internet of Things (IoT) and its applications. The attributes of the Internet of Things (IoT) facilitate its practical uses in real life, but also leave it vulnerable to online attacks. Numerous IoT nodes gather, store, and handle personal data, making them a prime target for hackers. The main objective of a network intrusion detection system is to find network intrusions, which is essential to guaranteeing the Internet of Things' security. Numerous investigators have tackled this issue and have put out numerous intrusion detection methods. Cyberattack subcategories are hard to identify and performance improvement is a challenge for the existing system. This research project introduces a new method for security analysis in Internet of Things networks by creating the Diffusion Kernel Spike Attention Network with Atomic Optimization (DifK-SAN+AO). This research employs a Diffusion Kernel Spike Attention Network to counteract cyberthreats and provides an overall threat analysis of the Internet of Things. The classification of threat and normal patterns on an IoT network is the main topic of this research study. For every form of attack, the proposed model shows promising findings. We use empirically based, simulation-generated datasets (ToN-IoT, BoT-IoT, and CIC-IoT-23 datasets) to detect attacks directed on the IoT ecosystem. In order to perform a security analysis, the results can be compared using the security metrics that are calculated, including the accuracy, precision, recall, and F1-score factors. The experimental results are more reliable than current algorithms and show 99.2% accuracy in detecting a variety of threats. As it would be observed from the results of the simulations, this demonstrates how efficient our method is in assessing the impacts of the attacks in IoT networks and maintaining good performance values. © 2024 IEEE.

关键词： Network intrusion

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tran, Tan Chang, Lin-Ching Lum, Peter The Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States The Catholic University of America Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350362480

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors - challenges in capturing the full context of functional movements - this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments. © 2024 IEEE.

关键词： Accelerometers

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Improving Online Education in Educational Institutions through the Integration of Internet of Things (IoT) 8

Improving Online Education in Educational Institutions throu...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Ramaraj, Kottaimalai Mathews, Ann Lynsha Helena Pratheeba, Hp Ramani, U. Sujitha, K.L. Thilagaraj, M. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India K Ramakrishnan College of Engineering Department of Electrical and Electronics Engineering Trichy India Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350367904

A key facilitating infrastructure for building intelligent structures that enable efficient in-person and virtual learning environments is the Internet of Things (IoT). The shift to smart learning, that includes IoT and Artificial Intelligence (AI) into the educational system, is enticing since it directly affects the motivation of learners, enrolment, participation, and in-depth understanding. Teaching and learning, management, examination, and class monitoring are only a few of the numerous issues that afflict conventional education. Many ingenious approaches to many facets of our lives are being made possible by modern advancements in information and communication technology (ICT);nonetheless, these imaginative concepts aren't adequately incorporated into the framework of education. The COVID-19 pandemic scenario, in specific, has brought out the need for innovative, creative approaches in the field of teaching. The present research examines previous investigations in the field and discusses the following topics: (i) issues with the current educational system and potential fixes;(ii) the shift to smart learning;and (iii) scientific hurdles (such as technological and cultural barriers) in making the move to smart learning. In accordance with these outcomes, creative approaches to the issues with the conventional system-such as smart teaching, smart evaluation, smart school environment, and smart management-are presented. The business and academic community may now include IoT, AI, and ICT into smart learning owing to the aforementioned experimental research's novel developments. © 2024 IEEE.

关键词： Federated learning

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State-based Sandbox Tool for Distributed Malware Detection with Avoid Techniques 13

State-based Sandbox Tool for Distributed Malware Detection w...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Rehida, Pavlo Markowsky, George Sachenko, Anatoliy Savenko, Oleg Khmelnytskyi National University Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine Kennesaw State University Department of Software Engineering and Game Development Marietta United States Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

ISBN: (纸本)9798350396119

This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technologies are described. Various anti-emulation techniques that are used in modern malware considered. Obfuscation as one primary approach to hide malware malicious actions described and discussed. State of emulator is presented, and the advantages of its usage are covered. Distributed model for malware detection is considered. Basic emulator and its current capabilities presented. Prepared files that represent malware are described. Experimental results for developed files that differs with included avoid techniques are presented. Disadvantages of proposed approach is described. Future research and sandbox improvement are described. © 2023 IEEE.

关键词： anti-emulation techniques malware detection obfuscation techniques sandbox

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

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computers, Materials & Continua 2023年第6期75卷 5697-5715页

作者： Seema Rani Anju Anupma Sangwan Krishna Kumar Kashif Nisar Tariq Rahim Soomro Ag.Asri Ag.Ibrahim Manoj Gupta Laxmi Chandand Sadiq Ali Khan Department of CSE Guru Jambheshwar University of Science&TechnologyHisar125001India Department of Hydro and Renewable Energy Indian Institute of Technology RoorkeeIndia College of Computer Science and Information Systems Institute of Business ManagementKorangi Creek RoadKarachi75190SindhPakistan Faculty of Computing and Informatics Universiti Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale *** of the major issues in such a network is the localization of underwater *** is required for tracking objects and detecting the *** is also considered tagging of data where sensed contents are not found of any use without *** is useless for application until the position of sensed content is *** article’s major goal is to review and analyze underwater node localization to solve the localization issues in *** present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes ***,a detailed subdivision of these localization schemes is ***,these localization schemes are compared from different *** detailed analysis of these schemes in terms of certain performance metrics has been discussed in this *** the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

关键词： Underwater wireless sensor networks localization schemes node localization ranging algorithms estimation based prediction based

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An Improved Hierarchical Control Strategy for Power Management in Islanded Hybrid Microgrids 2

An Improved Hierarchical Control Strategy for Power Manageme...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jeba Singh, O. Yamini, K. Anish Pon Rajesh Sharma, R. Mahapathra, Sheila Alliance University Centre for Academic Research Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Ece Tamil Nadu Chennai India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798350388916

Power management and control through a hierarchical control strategy in a Hybrid Micro Grid (HMG) consisting of multiple AC and DC small Micro Grids (MG) with different voltage ratings are focussed of this research. The ultimate aim of the proposed power management strategy is to limit the continuous operation of Interlink (IL)in different load conditions. Hence, the power loss at the IL converters are reduced, orelse it may affect the operational stability of the HMG. The hierarchical control strategy for power management and coordination for islanded mode of HMG is implemented and analysed. The designed system consists of primary and secondary control levels, that provides an accurate power transfer among the small MGs. The new methodology ensure a continuous power flow within the small MGs with minimal operation of IL converter. © 2024 IEEE.

关键词： Hybrid power

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Multiview Deep Learning-based Efficient Medical Data Management for Survival Time Forecasting

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IEEE Journal of Biomedical and Health informatics 2024年 PP卷 1-13页

作者： Yu, Keping Quan, Lijuan Chakraborty, Chinmay Shen, Yu Guo, Zhiwei Alfarraj, Osama Tolba, Amr School of Computer Science and Information Engineering Bengbu University Bengbu Anhui China Department of Rehabilitation the First Affiliated Hospital Jiangxi Medical College Nanchang University Nanchang China Birla Institute of Technology Mesra India National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Computer Science Department Community College King Saud University Riyadh Saudi Arabia

In recent years, data-driven remote medical management has received much attention, especially in application of survival time forecasting. By monitoring the physical characteristics indexes of patients, intelligent algorithms can be deployed to implement efficient healthcare management. However, such pure medical data-driven scenes generally lack multimedia information, which brings challenge to analysis tasks. To deal with this issue, this paper introduces the idea of ensemble deep learning to enhance feature representation ability, thus enhancing knowledge discovery in remote healthcare management. Therefore, a multiview deep learning-based efficient medical data management framework for survival time forecasting is proposed in this paper, which is named as “MDL-MDM” for short. Firstly, basic monitoring data for body indexes of patients is encoded, which serves as the data foundation for forecasting tasks. Then, three different neural network models, convolution neural network, graph attention network, and graph convolution network, are selected to build a hybrid computing framework. Their combination can bring a multiview feature learning framework to realize an efficient medical data management framework. In addition, experiments are conducted on a realistic medical dataset about cancer patients in the US. Results show that the proposal can predict survival time with 1% to 2% reduction in prediction error IEEE

关键词： Forecasting

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Progressive Motion Boosting for Video Frame Interpolation

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IEEE Transactions on Multimedia 2023年 25卷 8076-8090页

作者： Xiao, Jing Xu, Kangmin Hu, Mengshun Liao, Liang Wang, Zheng Lin, Chia-Wen Wang, Mi Satoh, Shin'Ichi The School of Computer Science Wuhan University Wuhan430072 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China The Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Video frame interpolation has made great progress in estimating advanced optical flow and synthesizing in-between frames sequentially. However, frame interpolation involving various resolutions and motions remains challenging due to limited or fixed pre-trained networks. Inspired by the success of the coarse-to-fine scheme for video frame interpolation, i.e., gradually interpolating frames of different resolutions, we propose a progressive boosting network (ProBoost-Net) based on a multi-scale framework to achieve flexible recurrent scales and then gradually optimize optical flow estimation and frame interpolation. Specifically, we designed a dense motion boosting (DMB) module to transfer features close to real motion to the decoded features from the later scales, which provides complementary information to refine the motion further. Furthermore, to ensure the accuracy of the estimated motion features at each scale, we propose a motion adaptive fusion (MAF) module that adaptively deals with motions with different receptive fields according to the motion conditions. Thanks to the framework’s flexible recurrent scales, we can customize the number of scales and make trade-offs between computation and quality depending on the application scenario. Extensive experiments with various datasets demonstrated the superiority of our proposed method over state-of-the-art approaches in various scenarios. © 2023 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Motion estimation

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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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