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Digital Investigation Forensic Model with P2P Timestamp Blockchain for Monitoring and Analysis

Journal of Electrical Systems 2024年第1期20卷 9-17页

作者： Almahadeen, Layth Pecho, Renzon Daniel Cosme Raj, Murugananth Gopal Rajesh, Nichenametla Imneef, Zainab Mohammed Yelpale, Sayali Karmode Department of Financial and Administrative Sciences Al- Balqa' Applied University Jordan Department of Biochemistry Universidad San Ignacio de Loyola Lima Peru Department of Electrical and Electronics Engineering Ahalia School of Engineering and Technology Kerala India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Dentistry Al-Zahrawi University College Karbala Iraq Department of Information Technology MGM College of Engineering Maharashtra India

Forensic analysis of Blockchain data is a new field in police work. It's now one of the largest problems facing law enforcement. The paper discussed the worldwide need for digital forensics in law enforcement and Blockchain forensics to counteract crimes committed using Blockchain technology. It's been said that we've entered a new age of technology that's heavily dependent on the principles of Blockchain. The research produced a set of guidelines for Digital Investigators. on addition, a theoretical framework grounded on the concept of regular activity has been developed, and a legislative framework has been proposed to ensure that its illegal purpose will always be punished severely. © JES 2024 on-line: ***

关键词： Digital forensics

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A Novel Approach for Specification Testing on Heart Disease Detection Using Feed-Forward Neural Network

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SN computer Science 2023年第6期4卷 1-8页

作者： Naresh, E. Darshan, S. L. Shiva Srinidhi, N.N. Niranjanmurthy, M. Dayananda, P. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of AI&ML BMSITM Avalahalli Bengaluru India

The proposed work highlights the importance of testing in machine-learning applications and the ensuing need to increase model quality to decrease the likelihood of errors. The proposed work considers patient health information that may be utilized for decision-making or prediction utilizing various computations, and in this instance, it emphasizes the development of artificial neural networks with the multilayer perceptron method to forecast cardiac abnormalities. The dataset employed in this work includes information about the patient’s demographics, clinical measures, and medical background. Utilizing this labelled dataset, one may estimate if each patient has heart disease or not. We developed a GUI to collect data about a new patient to accomplish the task specified. After the model is generated, effective functionality testing was performed on the model. As a result of the testing report’s assistance in identifying faults and defects, the artificial neural network’s accuracy ultimately improved as a result of its improved accuracy following correction. After undergoing feature testing, the application’s accuracy ascended to 96.69%, which is a higher percentage than was attained using any alternative method. © 2023, The Author(s).

关键词： ANN Functional testing Heart disease Machine learning Multilayer perceptron technique

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Detection and Analysis of Lymphoma Cancer Through Machine Learning and Deep Learning 1

Enhanced Detection and Analysis of Lymphoma Cancer Through M...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Choudhary, Doddapaneni Meghana Ashwini, Bugata Sai Rasagna Devi, Kavuluri Leela Kedareswari, Kotte Kiran, Musunuru Hari Srinivas, Pvvs Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350367720

This study focuses on the use of ml techniques to unravel the complexity of lymph node classification, which is an important part of oncology due to its different subtypes and different treatments. This study aims to develop an accurate classification model for different types of lymphoma using image datasets in the "TRAIN_DIR"and "TEST_DIR"maps. The research combines methods such as support vector machine (SVM), decision trees, logistic regression, and naive Bayes and performs rigorous and effective training methods to optimize the performance of models. Advanced preprocessing methods and data augmentation techniques are used to improve the model's ability to detect subtle changes in biopsy images, ultimately aiming to increase diagnostic accuracy and support self-healing strategies. This approach has the potential to improve clinical decision-making and patient care in lymphoma management. Diagnose and fix problems. The study combined machine learning algorithms to analyze biopsy images to accurately classify lymphoma subtypes. The training model focuses on improving the sensitivity and specificity of subtype detection, which is important for creating an effective patient-based treatment plan. Using the computational power of support vector machines, decision trees, logistic regression, and naive Bayes, this research can help improve oncology diagnostic tools, reducing diagnostic uncertainty and supporting more treatments. The aim of this research is to advance the work of personalized medicine by creating a powerful computational model that can reliably distinguish between lymphoma subtypes. By automating and improving the accuracy of subtype classification through machine learning, the research aims to streamline diagnostic workflows and help make timely diagnostic decisions. © 2024 IEEE.

关键词： Biopsy

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Teaching Practice based on Smart Cloud Platform in Operating System Principles and Applications Course Ideology and Politics 14

Teaching Practice based on Smart Cloud Platform in Operating...

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14th International Conference on information technology in Medicine and education, ITME 2024

作者： Chen, Xiaowen Tang, Cuie Wu, Tao Guizhou Medical University School of Biology and Engineering Gui'an New District Guizhou China Guizhou Medical University Computer Education and Information Technology Center Gui'an New District Guizhou China

ISBN: (纸本)9798350356670

With the rapid development of information technology, the smart cloud platform, as an emerging educational tool, provides new opportunities for teaching reform. This paper explores the ideological and political education reform practice in the operating system principle and application course based on the smart cloud platform, aiming to improve the teaching quality of the course and the comprehensive quality of students through innovative teaching methods and integrated resources. First, the course design combines the basic principles and practical applications of operating systems, clarifies the goals of course ideological and political education, and strives to integrate ideological and political education into technical education. Secondly, by building a question bank on the smart cloud platform, students are provided with abundant opportunities for computer practice, which improves their hands-on ability and practical operation skills. In addition, through experimental projects, course case analysis, and other channels, the national science and technology strategy and social responsibility are integrated into the teaching content to enhance students' social responsibility and patriotism. Finally, through a diversified evaluation system and student feedback mechanism, the teaching content and methods are continuously improved to achieve continuous optimization of teaching effects. The research in this paper provides new ideas and practical experience for the ideological and political teaching of the operating system course, which is of certain significance to promoting curriculum reform and improving education quality. © 2024 IEEE.

关键词： Cloud platforms

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Adaptive residual convolutional neural network for moiré image restoration

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International Journal of information technology (Singapore) 2025年第2期17卷 783-791页

作者： Malagi, Vindhya P. Naresh, E. Mithra, C. Suresh, B. V. N. V. Krishna Department of AI & amp ML Dayananda Sagar College of Engineering Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai 600062 India Computer Science & amp Engineering KLEF KL University Andhra Pradesh Vaddeswaram India

Restoring Moiré images presents significant challenges due to the complex interference patterns that obscure image details. These patterns often degrade the quality of images, making accurate restoration crucial for various applications. Effective Moiré image restoration requires advanced techniques to overcome the difficulties posed by these intricate artifacts. This study introduces an Adaptive Residual Convolutional Neural Network (RCNN) for Moiré image restoration, augmented with the Zebra Optimization Algorithm (ZOA) to enhance both feature extraction and restoration accuracy. The hybrid model leverages ZOA's capability to balance exploration and exploitation, optimizing network parameters to improve learning efficiency. Tested on the DIV2K dataset, this approach demonstrates significant improvements in handling complex Moiré patterns, as evidenced by enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. © The Author(s) 2024.

关键词： Adaptive residual convolutional neural network Moiré images Peak signal-to-noise ratio Structural similarity index measure Zebra optimization algorithm

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Fault-tolerant Hamiltonian cycle strategy for fast node fault diagnosis based on PMC in data center networks

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Mathematical Biosciences and engineering 2024年第2期21卷 2121-2136页

作者： Zhao, Zhipeng Hu, Zhenyu Zhao, Zhiyu Du, Xiaoyu Chen, Tianfei Sun, Lijun Minister of Education Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China College of Engineering Economics Henan Finance University Henan Zhengzhou450001 China Luoyang Railway Station of China Railway Zhengzhou Bureau Group Co. LTD Henan Luoyang450001 China College of Computer and Information Engineering Henan University Henan Kaifeng475000 China

System-level fault diagnosis model, namely, the PMC model, detects fault nodes only through the mutual testing of nodes in the system without physical equipment. In order to achieve server nodes fault diagnosis in large-scale data center networks (DCNs), the traditional algorithm based on the PMC model cannot meet the characteristics of high diagnosability, high accuracy and high efficiency due to its inability to ensure that the test nodes are fault-free. This paper first proposed a fault-tolerant Hamiltonian cycle fault diagnosis (FHFD) algorithm, which tests nodes in the order of the Hamiltonian cycle to ensure that the test nodes are faultless. In order to improve testing efficiency, a hierarchical diagnosis mechanism was further proposed, which recursively divides high scale structures into a large number of low scale structures based on the recursive structure characteristics of DCNs. Additionally, we proved that 2(n−2)nk−1 and (n−2)tn,k/tn,1 faulty nodes could be detected for BCuben,k and DCelln,k within a limited time for the proposed diagnosis strategy. Simulation experiments have also shown that our proposed strategy has improved the diagnosability and test efficiency dramatically. © 2024 the Author(s), licensee AIMS Press.

关键词： Failure analysis

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Big Mart Sales using Hybrid Learning Framework with Data Analysis 2

Big Mart Sales using Hybrid Learning Framework with Data Ana...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Phani Praveen, S. Chaitanya, Polavarapu Mohan, A. Shariff, Vahiduddin Ramesh, Janjhyam Venkata Naga Sunkavalli, Jayaprakash Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada India Computer Science and Engineering Sir C R Reddy College of Engineering Eluru India Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350340235

The retail industry has undergone a rapid transformation in recent years, owing to technological advancements and the availability of massive amounts of data. In this context, predictive analytics is critical in assisting retailers in making informed decisions, optimising operations, and increasing profitability. This study employs a hybrid learning framework combining machine learning and traditional statistical methods to predict sales in the Big Mart retail chain. The first stage of the proposed approach entails a thorough data analysis in which this study examines historical sales data, product attributes, store demographics, and other relevant variables to gain insights into sales patterns and correlations. Exploratory data analysis and feature engineering are used to prepare the data for model development. The new algorithm analyses customer data and provides insights to help businesses make better business decisions. Using this technology, Big Mart hopes to increase sales and improve customer satisfaction. Big Mart used the Ensemble algorithm in this paper, a type of artificial intelligence that can learn from data and make predictions based on that data. The ensemble algorithm is a hybrid algorithm that combines the two algorithms used to find Big Mart sales, such as Random Forest (RF) and S upport Vector Machine (S VM). The algorithm is trained using historical customer data, such as purchase history, demographic information, and other variables that may influence purchasing behavior. The dataset is obtained from Kaggle to evaluate the model's performance. The algorithm can use this data to identify patterns and trends in customer behaviour, which can be used to forecast future sales. © 2023 IEEE.

关键词： Machine Learning Random Forest (RF) Support Vector Machine (SVM)

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Pulse-Based Smart Electricity Meter Using Raspberry Pi and MEFN 1

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5th International Conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Tadiparthi, Eswar Abisheak Kumari, Majji Prasanna Sai, Basanaboyana Vamsi Kalyan, Kollana Bharat Dinesh Reddy, B. Rao, N. Thirupathi Bhattacharyya, Debnath Department of Computer Science and Engineering Vignan’s Institute of Information Technology Andhra-Pradesh Visakhapatnam India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra-Pradesh Vaddeswaram Guntur522502 India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

As the computation power of microcomputers increases over time, we need to put them to proper use. Green energy generation and consumption are evident problems that need to be prioritized. We know that green energy is not produced at a scale for us to rely on but if we know how electricity is consumed, we might be able to hack our way. In order to know that we are using light-dependant resistor (LDR) to count pulses from the electricity meter and update it in the database using a Raspberry Pi connected to the Internet which hits the server, the end user can see the utilization in real time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Flutter LDR MERN Raspberry Pi Smart electricity meter

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Enhancing Cloud Infrastructure Security with GraphSAGE-Based Intrusion Detection Systems 1

Enhancing Cloud Infrastructure Security with GraphSAGE-Based...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, G. Nanda Kishor Syed, Shakir Mogal, Amit Karbhari Abid, Tahera Perada, Anvesh Suneetha, Sampathirao University of South Florida United States Malla Reddy University Department of Computer Science and Engineering Hyderabad India Bargersville IN United States Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India Nawab Shah Alam Khan College of Engineering and Technology Department of Information Technology Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798331529246

Cloud computing provides scalable and adaptable infrastructure however is significantly susceptible to cyber-attacks. This work presents a hybrid methodology that integrates the GOA and GA for feature selection, alongside the Residual GraphSAGE model for intrusion detection. The hybrid GOA-GA method enhances feature selection by optimizing the balance between exploration and exploitation, mitigating overfitting, and augmenting classification performance. The Residual GraphSAGE model alleviates over smoothing while effectively capturing node and edge interactions for precise intrusion classification. Assessment on a benchmark dataset reveals exceptional performance, attaining an accuracy of 91.74%, surpassing established models such as GCN and ResNet. This study emphasizes the need of hybrid optimization and sophisticated graph-based learning in improving cloud infrastructure security. © 2025 IEEE.

关键词： Network intrusion

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