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DDPG Algorithm with Elastic Replay Buffer Assisting in the Optimization of the NOMA System 5

DDPG Algorithm with Elastic Replay Buffer Assisting in the O...

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5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zheng, Wenjing Wang, Jinghan Fu, Wenwen Li, Jun Lin, Fei Zheng, Xuehan Gao, He Jinan China School of Information and Electrical Engineering Shandong Jianzhu University Jinan China Shandong Zhengchen Technology Co. LTD Jinan China

ISBN: (纸本)9798350368284

Non-orthogonal multiple access networks inspired by cognitive radio technology can enhance energy efficiency and address the issue of spectrum resource scarcity. However, resource allocation strategies are still needed for optimization. This paper applies deep reinforcement learning (DRL) algorithms to optimize transmission strategies. We propose a DDPG algorithm integrated with elastic experience replay, which helps the agent strike a better balance between exploring new strategies and utilizing existing experiences. Simulation results demonstrate that the proposed algorithm achieves a higher transmission rate compared to the standard DDPG algorithm, random algorithm and greedy algorithm. © 2024 IEEE.

关键词： Deep reinforcement learning

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An SGX and Blockchain-Based System for Federated learning in Classifying COVID-19 Images 21

An SGX and Blockchain-Based System for Federated Learning in...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Qin, Sihang Gu, Xianjun Jun, Xing Li, Haitao Huang, Mengqi Dai, Weiqi Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China State Grid Wuhan Power Supply Company China

ISBN: (纸本)9798350329223

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes. © 2023 IEEE.

关键词： Blockchain

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Network-Based Multi-Protocol Attack Detection in Internet-of-Medical-Things Using Explainable Machine learning 17

Network-Based Multi-Protocol Attack Detection in Internet-of...

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17th international conference on Developments in eSystems engineering, DeSE 2024

作者： Alani, Mohammed M. Department of Electrical Engineering and Computing Sciences Rochester Institute of Technology - Dubai Dubai United Arab Emirates

ISBN: (纸本)9798350368697

Internet of Things applications are on the rise within different areas of health and medical service. With this rapid rise of these applications, threat actors become more interested in targeting such devices. Within the health and medical context, there are particular challenges in privacy and security. In this paper, we present an explainable machine learning model designed to detect multi-protocol network-based attacks on Internet-of-Medical-Things with high accuracy. The proposed system was trained and tested using CICIoMT-2024 dataset. The proposed system delivered an accuracy exceeding 99.9%, with an F1 score exceeding 0.99. To increase trust in the obtained results, the proposed system was explained using SHAP values to provide insights into the most impactful features, and the nature of their impact on the system’s decisions. © 2024 IEEE.

关键词： Network intrusion

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Analysing BP Disease Prediction through Transfer learning (machine learning) - A Conceptual Framework and Case Study 4

Analysing BP Disease Prediction through Transfer Learning (m...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bisht, Rajendra Singh Al-Jawahry, Hassan M. Alsailawi, H.A. Lamba, Anil Kumar Dhabliya, Dharmesh Mohsen, Abdul Hussein Al-Khuzaie, Mohammed Y. Mezaal, Ali Abdulkareem Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq Geeta University School of Computer Science and Engineering Delhi NCR Panipat India Vishwakarma Institute of Information Technology Pune India Alkunooze University College Computer Engineer Department Basra Iraq National University of Science and Technology College of Technical Engineering DhiQar64001 Iraq Kut University College 'Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

In the evolving landscape of the clever healthcare industry, the detection and prognostication of ailments thru technological improvements present each possibility and demanding situations, mainly within the realm of lethal tumors. This paper delves into the application of switch getting to know in the framework of the Internet of Medical Things (IoMT) to enhance the accuracy and reliability of disease predictions. Utilizing the modernA capabilities of the Google Net deep device-learning version, we introduce a relaxed IoMT-based totally technique that substantially outperforms conventional methodologies in predicting lethal most cancers ailments. Our have a study demonstrates a incredible accuracy price of 90 seven.5%, underscoring the ability of switch learning to revolutionize predictive diagnostics within the clever healthcare area. By integrating superior IoMT gadgets and secure information protocols, this method no longer most effective improves illness prediction quotes but moreover addresses important privacy and protection issues inherent in modern scientific information handling. © 2024 IEEE.

关键词： fatal tumors Internet of Medical Things (IoMT) privacy security smart healthcare industry

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Ethical Design and Implementation of AI in the Field of learning and Education: Symmetry learning Technique 4

Ethical Design and Implementation of AI in the Field of Lear...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Elantheraiyan, P. Priya, K.M. Gamadia, Dr Rimple Abdulhasan, Maki Mahdi Abood, Baydaa Sh. Z. Al-Khalidi, Ayadh Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Chennai62 India Sri Eshwar College of Engineering Department of English Coimbatore India C K Shah Vijapurwala Institute of Management Gujarat Vadodara India AL-Nisour University College Baghdad Iraq National University of Science and Technology Dhi Qar Iraq Al-Hadi University College Baghdad Iraq

ISBN: (纸本)9798350360165

The infusion of Artificial Intelligence (AI) in primary education promises the increased learning experiences and achievement among students, but in the face of these pluses, consideration of child privacy ethically comes as a major concern. This paper seeks to look into the nuanced role that leveraging AI technologies play in a manner that augments primary education while, at the same time, ensuring in the process protection to children's privacy rights. The education sector has also been covered with artificial intelligence lately, which brings a place where facilities for individual learning, according to the exact requirement and style of learning of the student, can be put in place. More so, the basic level of education-that is from where the actual foundation of skills lies-a huge potential remains in terms of AI-based applications among students, in their engagement, autonomy, and academic attainment. Examples of such AI tools will be adaptive learning platforms, intelligent tutoring systems inspired towards practice, and educational games focusing on the diverse needs and preferences of the learner, hence redefining traditional approaches in pedagogy. However, the very penetration of AI into the primary education system raises mostly ethical questions, connected with child privacy. Using the capacities of AI-driven platforms, a child literally emits plenty of sensitive data from personal identifiers up to learning preferences and behavioral patterns. © 2024 IEEE.

关键词： Contrastive learning

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Medical Prediction App using Random Forest 15

Medical Prediction App using Random Forest

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Benitha Christinal, J. Sneha, N. Brindha, S. Panimalar Engineering College IT Department Chennai India

ISBN: (纸本)9798331300579

This study proposes a machine learning- based diseases prediction algorithm to predict multiple diseases within a single prediction system. This algorithm analyzes an undisclosed patient datasets and applies various features and selection techniques to determine the most notable factors for diseases prediction. Three different machine learning models like logistic regression, Random forest andSVM are used in the process of training and testing the datasets. The indicated by the random forest model is more effective than the other models, achieving an overall accuracy of 85%. The accurate result provided by this model can help the healthcare to identify the high-risk patients and offer targeted prevention and treatment plans. However, further validation and refinement are required toensure the algorithm's effectiveness across different healthcare settings and diverse populations. © Grenze Scientific Society, 2024.

关键词： Prediction models

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A Proactive Method Using Machine learning Models to Detect Phishing Attacks in Thread Sharing Network 10

A Proactive Method Using Machine Learning Models to Detect P...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Ajay Kumar, K.V. Deepalakshmi, R. Balasubramaniyamoorthy, P.S. Murugan, K.R. Senthil Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798350384369

Phishing attacks present an enduring and pervasive threat in the digital realm, with cybercriminals continuously devising innovative techniques to deceive users and compromise sensitive information. This paper focuses on the crucial task of detecting phishing using advanced machine learning techniques. Recognized as a critical challenge in the digital landscape, phishing detection is imperative due to its potential to undermine trust, compromise security, and inflict financial and reputational damage. The paper employs feature extraction from diverse datasets as a pivotal step in the process, ensuring that relevant attributes are identified and utilized effectively. Moreover, it highlights the system's capability for real-time interpretation of websites, enhancing its responsiveness and efficacy in detecting phishing attempts promptly. The approach integrates machine learning models to enhance accuracy and robustness, achieving a notable accuracy of 97.5% through the utilization of the Gradient Boosting algorithm. This paper contributes to ongoing cybersecurity efforts by offering a multifaceted approach to phishing detection, leveraging the capabilities of machine learning. The findings underscore the importance of continuous research in adapting to evolving phishing tactics and improving the overall security posture of digital environments. Moving forward, the integration of real-time interpretation and adaptive machine learning models promises to further strengthen cybersecurity measures, ensuring a safer digital environment for users worldwide. © 2024 IEEE.

关键词： Phishing

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Simulation Technology in the Development of Mechanics Digital Instructional Resources: A Case Study 6

Simulation Technology in the Development of Mechanics Digita...

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6th international conference on Educational Technology Management (ICETM)

作者： Wang, Shang Mao, Xing Zhu, Qingsong Beijing Polytech Sch Automot Engn Beijing 100176 Peoples R China Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China

ISBN: (纸本)9798400716676

The application of simulation technology in education is an emerging field that has shown promising results in improving the quality of teaching and learning. In this paper, we explore the potential of simulation technology in the development of digital teaching resources for engineering Mechanics (EM) using the metal tensile fracture experiment as an example. Through the development of digitized materials such as colorful stress nephograms, animations, videos, and interactive simulations, students could better understand the fundamental concepts of EM. The effectiveness of the approach was evaluated through a teaching quality evaluation that included student surveys. Results showed that the use of simulation technology significantly improved students' engagement, comprehension of abstract concepts, and overall satisfaction with the course. These findings highlight the potential and advantages of simulation techniques in the development of digital teaching resources for complex courses such as EM, which can benefit from visual and dynamic representations of theoretical concepts.

关键词： Simulation technology Simulation Digital instructional resources teaching

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HMLM: An Intelligent Artificial Intelligence Assisted Strategy to Identify UPI Frauds Based on Hybrid Markov learning Methodology

HMLM: An Intelligent Artificial Intelligence Assisted Strate...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bharath, S. Prasad, G. Lakshmi Vara Sujatha, V. Hemajothi, S. Mani, D. Sharada Merlin, N.R. Gladiss Rajalakshmi Institute of Technology Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem AndhraPradesh Ongole India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem Andhra Pradesh Ongole India R.M.K. Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331543617

The proliferation of Unified Payments Interface (UPI) transactions in the digital age has considerably improved financial inclusion, but it has also increased the vulnerability to fraud. This manuscript introduces a novel approach that employs Artificial Intelligence (AI) to detect UPI frauds. The approach is based on deep learning and is referred to as the Hybrid Markov learning Methodology (HMLM). To evaluate the suggested system, it is cross-checked against the traditional Hidden Markov Model (HMM). Our approach combines the benefits of modern artificial intelligence methods with Markov models to improve the accuracy of fraud detection. Combining deep learning algorithms with probabilistic state transitions allows the hybrid Markov learning Methodology to dynamically analyze transaction patterns and behaviors. By modeling both historical and real-time transaction data, our approach improves the identification of unusual activity and fraud patterns that conventional algorithms may ignore. From experimental data, we prove our approach minimizes false positives and improves the rate for fraud detection against the current options available. Our work advances the current state of fraud detection systems using more flexible and robust fraud detection systems, thus enabling security and confidence in UPI transactions. © 2024 IEEE.

关键词： Hidden Markov models

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An Alternative Deep learning Approach for Early Diagnosis of Malaria 15

An Alternative Deep Learning Approach for Early Diagnosis of...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vrindavanam, Jayavrinda Mary Jasmine, E. Kamath, Gaurav Narayanan, Anirudh Bengaluru India Christ University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detection of Malaria using traditional investigation methods through blood samples and expert judgments was found to be time-consuming. In this paper, the authors introduced a Machine learning automated system to eliminate the need for human intervention, which in turn enables early detection of malaria. The study has used various Deep learning techniques such as traditional Convolutional Neural Network (CNN), VGG19, ConvNeXtXLarge, ConvNeXtBase, ConvNeXtSmall, ConvNeXtTiny, InceptionResnetv2, Xception, DenseNet169, EfficientNetB7, MobileNet, ResNet50, and NasNetLarge as base models. These models have been trained and tested with microscopic blood smear images dataset and observed that ConvNeXtXLarge detects malarial parasites with an accuracy of 96%. The proposed method outperforms the existing approaches in terms of both accuracy and speed. The findings of this work can contribute to the development of more accurate and efficient automated systems for early detection of Malaria. © 2024 IEEE.

关键词： ConvNeXt Deep learning Malaria detection ResNet Transfer learning

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