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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Rekha, V. Sharmila, N. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Naveen, K.B. Shilpa, M. Sheela, S. Department of Computer Science and Engineering Bangalore India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagara India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational *** paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a 0.25% improvement in energy utilization efficiency, a 0.20% reduction in operational costs, and a 0.30% increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids. © 2024 IEEE.

关键词： Cloud analytics

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Comparative Analysis of Camera Calibration Algorithms for Football Applications 4

Comparative Analysis of Camera Calibration Algorithms for Fo...

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4th International Workshop on information Technologies: Theoretical and Applied Problems, ITTAP 2024

作者： Sorokivskyi, Oleksandr Hotovych, Volodymyr Nazarevych, Oleg Shymchuk, Grygorii Department of Computer Science Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University Ternopil Ukraine

In solving the problem of automated analysis of football match video recordings, special video cameras are currently used. This work presents a comparative characterization of known algorithms and methods for video camera calibration, including those utilizing machine learning and neural networks, with the aim of identifying their shortcomings and forming a theoretical foundation for developing modern, more effective methods and algorithms. Specifically, it examines both algorithms that require more input data but operate quickly [1, 2] and more accurate algorithms using machine learning [3, 4, 5, 6, 5]. It is demonstrated that their main drawback is either accuracy or speed. More accurate algorithms using machine learning often do not specify the algorithm's operational speed, which precludes their use in real-time applications. The examined works that emphasize speed frequently lack the accuracy necessary for practical use in real-life scenarios. Copyright © 2024 for this paper by its authors.

关键词： Semantic Segmentation

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Identifying the Gender of Human Cyber Attackers Using Machine Learning Techniques

Identifying the Gender of Human Cyber Attackers Using Machin...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Almurayziq, Tariq S University of Ha'il College of Computer Science and Engineering Department of Computer Science and Information Ha'il Saudi Arabia

ISBN: (纸本)9798350347050

Ceyber-attacks can take various forms but they essentially entail a virus being installed on a target computer system with the aim of obtaining information, spying on the operator or acquiring remote control of the machine. It constitutes criminal activity and when conducted on a large scale, it has the ability to undermine entire national economies. If attempts to nullify cyber-attacks are to prove successful, it is necessary to develop a good understanding of the character of the perpetrators as well as how they typically behave. Therefore, the current study utilises a social science perspective to investigate the gender of those launching cyber-attacks. More specifically, this entails the use of machine learning methods including Support vector machine (SVM), K-nearest neighbour (KNN) and Artificial neural networks (ANN). The findings indicate that SVM offers a reliable means of determining the gender of cyber-attackers, thereby helping profilers when investigating such crimes. © 2023 IEEE.

关键词： Crime

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Data-driven control for nonlinear automated vehicles with multi-description coding for handling data dropouts 63

Data-driven control for nonlinear automated vehicles with mu...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Shuhua Ma, Lifeng Charalambous, Themistoklis Nanjing University of Science and Technology School of Automation Nanjing210094 China University of Cyprus School of Electrical Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland FinEst Centre for Smart Cities Tallinn Estonia

ISBN: (纸本)9798350316339

This paper proposes a data-driven control (DDC) strategy for nonlinear automated vehicles, employing a multidescription coding (MDC) mechanism based on scalar quantization to address the challenges of data dropouts and limited bandwidth in networked communication environments. The developed MDC-based communication protocol enhances system robustness by reducing the probability of data dropout. It achieves this by transmitting multiple descriptions of source data through diverse channels, incorporating quantization and index reassignment to efficiently alleviate bandwidth constraints. Based on the reconstructed real-time data, a novel data-driven controller and a parameter estimation algorithm are designed, offering adaptability to varying driving conditions and rapid response in dynamic environments. A numerical simulation showcases the potential of the proposed approach as a reliable and efficient solution for the speed problem in nonlinear automated vehicles with challenging communication environments. © 2024 IEEE.

关键词： Bandwidth

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Core-Intermediate-Peripheral Index: Factor Analysis of Neighborhood and Shortest Paths-Based Centrality Metrics 7th

Core-Intermediate-Peripheral Index: Factor Analysis of Neigh...

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Meghanathan, Natarajan Department of Electrical and Computer Engineering and Computer Science Jackson State University 1400 Lynch Street JacksonMS39217 United States

ISBN: (纸本)9783031535512

We perform factor analysis on the raw data of the four major neighborhood and shortest paths-based centrality metrics (Degree, Eigenvector, Betweenness and Closeness) and propose a novel quantitative measure called the CoreIntermediate-Peripheral (CIP) Index to capture the extent with which a node could play the role of a core node (nodes at the center of a network with larger values for any centrality metric) vis-a-vis a peripheral node (nodes that exist at the periphery of a network with lower values for any centrality metric). We conduct factor analysis (varimax-based rotation of the Eigenvectors) on the transpose matrix of the raw centrality metrics dataset, with the node ids as features, under the hypothesis that there are two factors (core and peripheral) that drive the values incurred by the nodes with respect to the centrality metrics. We test our approach on a diverse suite of 12 complex real-world networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Factor analysis

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Low Power and Enhanced Data Retention Time in DRAM in FinFET Technology

Low Power and Enhanced Data Retention Time in DRAM in FinFET...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Sathyasree, K. Kavin Kumar, K. Sivaselvi, S. Santhosh Babu, A.V. Deebika, R. Banumithra, B. Nandha Engineering College Department Of Electrical And Electronics Engineering Erode638052 India Kongu Engineering College Department Of Electronics And Communication Engineering Erode Perundurai638060 India Erode Sengunthar Engineering College Computer Science And Design Palakarai Perundurai Thuduppathi638057 India Vivekanandha College Of Engineering For Women Department Of Information Technology Namakkal Tiruchengode637205 India Kongunadu College Of Engineering And Technology Department Of Electrical And Electronics Engineering Tholurpatti Post Tamilnadu Trichy Dt621215 India

ISBN: (纸本)9798350352689

Modern technologies require huge amounts of storage space and fast access to data for uninterrupted high-quality audio and video processing. Random Access Memory (RAM) operates at higher frequencies and allows the CPU to access the files faster. It stores frequently required data so that it can be retrieved rapidly. RAM cells are classified into SRAM and DRAM. For storage SRAM cell uses two cross-coupled inverters and DRAM cell uses a capacitor. SRAM cells are more reliable but require more than 6 transistors for its storage, which consumes more area and power than the DRAM cell, where it uses less than 4 transistors for its storage. But DRAM suffers from frequent refresh issues, due to leakage across the capacitor. The leakage current is reduced to minimize the frequent refresh issue and for that several leakage reduction techniques are incorporated to design the DRAM cell to enhance the Data Retention Time (DRT). Modified Self Controllable Voltage Level Circuit (MSVL) based DRAM cells are proposed in this project. MSVL 3T and 4T DRAM cells have less leakage power and better hold performance than the conventional, Sleepy Keeper and SVL based DRAM cells. Various parameters such as dynamic power, leakage power, retention time and delay are measured and compared with other DRAM cells. 18 nm FinFET technology is designed using the CADENCE Virtuoso Tool. The proposed DRAM cell has superior hold performance than previous designs, according to simulation findings. The frequent refresh rate has been reduced and finally the power consumed due to it has also been minimized. © 2024 IEEE.

关键词： Dynamic random access storage

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HSPBCI: a robust framework for secure healthcare data management in blockchain-based IoT systems

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Multimedia Tools and Applications 2024年 1-25页

作者： Gupta, Sangeeta Chithaluru, Premkumar Stephan, Thompson Nafisa, Shaik Kumar, Sandeep Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad500075 India Department of Information Technology Mahatma Gandhi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Centre of Research Impact and Outreach Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Abstract: Online monitoring and counseling have helped patients and made digital health management possible. However, the vulnerability of medical data gathered via devices, especially in the Internet of Things (IoT) context, highlights the necessity of strong security protocols. IoT is essential, but there are still difficulties in managing vital data over a heterogeneous network. To reduce potential health hazards, it is essential to ensure that IoT sensors do not negatively impact human health. Maintaining the privacy of patient records is crucial to avoid unauthorized access or financial gain by third parties. This paper introduces the Healthcare System using Private Blockchain-based Cloud IoT (HSPBCI) services, providing a comprehensive framework to address these issues. The framework's main goals are to speed up block-based transaction encryption and decryption times and grant authorized access to patient records. The proposed HSPBCI model restricts access to patient records to those holding the appropriate key, ensuring secure block-based access. Nodes are randomly deployed in cloud locations to generate data, which is then processed by the cloud blockchain network to filter out unscrupulous users and service providers. The results show that the proposed HSPBCI model, with up to 50 nodes, has encryption and decryption times that are over five times faster than existing models. With an improvement of a factor of O(n) over the existing O(n^2), the proposed model's transaction processing and runtimes for different characteristics and nodes range from 0.15 ns to 0.44 ns and 1 ms to 470 ms, respectively, compared to 0.18 ns to 0.53 ns and 2 ms to 570 ms in existing works. In the context of blockchain-based IoT applications, this research advances the field of secure healthcare data management. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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Leveraging AI to Unlock the Potential of Data science 15

Leveraging AI to Unlock the Potential of Data Science

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management 8th

ECO-Guard: An Integrated AI Sensor System for Monitoring Wil...

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8th International Conference on information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Nikhilesh Krishna, Ch. Rauniyar, Avishek Kireeti Sai Bharadwaj, N. Raj, Sujay Bharath Valsan, Vipina Suresh, Kavya Ravikumar Pandi, V. Sathyan, Soumya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

In India, 12% of wild animals are threatened with extinction, and human activity threatens more than 80% of East and Southeast Asia's wetlands. Every year, an estimated 400 people are killed by wildlife in India, with Maharashtra alone seeing 88 human deaths and 9258 cattle killings by tigers in 2020. The ECO-Guard is an intelligent device which uses the You Only Look Once (YOLO) model and various sensors and machine learning models with algorithms that support and promote forest monitoring and tourist guidance in forest areas addressing Sustainable Development Goal (SDG) 12 (Life on Land). The paper presents a linear chain-like mesh of devices speeded across bio-area which are used in correlation for intended surveillance. Each device can multimodule apps and tally their sightings automatically to analyze coherence of the situation with the forest rangers, organizations, and figures. It catches from the information, the ill substitutes like human spotting with beasts. It suggests that the advice can be further put into practice by constantly monitoring any closeness of alternatives that match to create an unfavorable combination. Also having to help detect many forest crimes, some including illegal logging, preventing, and deforestation on a large scale. This paper highlights a novel ECO-Guard, an intelligent and autonomous monitoring system which includes artificial intelligence (AI)-enhanced strategies and algorithms to conserve wildlife and forest ecosystems by integrating various hardware and software components. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deforestation

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Near-field Localization for Coprime Array

Near-field Localization for Coprime Array

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Zhou, Cong Cheng, Hongqiang You, Changsheng Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350364774

In this paper, a novel two-phase near-field localization method for the symmetric coprime array is proposed which comprises two spare uniform linear arrays (ULAs). First, to extract the target angle information from the covariance matrix of the received signal, we utilize the one-dimensional (1D) multiple signal classification (MUSIC) method to obtain candidate target angles with respect to each decomposed subarrays. Then, the unique target angle is determined by exploiting the coprime property. Next, given the estimated target angle, the target rang is estimated by performing the 1D spectrum search with one decomposed sparse ULA. Finally, numerical results are provided to demonstrate the effectiveness of the proposed near-field localization method. © 2024 IEEE.

关键词： Multiple signal classification

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