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Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique

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Health and Technology 2024年第1期14卷 109-118页

作者： Alizargar, Azadeh Chang, Yang-Lang Tan, Tan-Hsu Liu, Tsung-Yu Department of Electrical Engineering College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei 10608 Taiwan Department of Multimedia and Game Science Lunghwa University of Science and Technology Taoyuan 333326 Taiwan

Purpose: Hepatitis B, caused by the Hepatitis B virus (HBV), can harm the liver without noticeable symptoms. Early detection is crucial to prevent transmission and enhance recovery. The main goal is to predict Hepatitis B through cost-effective lab test data, by utilizing machine learning. The primary focus is on evaluating the effectiveness of various algorithms in predicting the disease and their potential to enhance early diagnosis capabilities. Methods: Six distinct algorithms (Support Vector Machine, K-nearest Neighbors, Logistic Regression, decision tree, extreme gradient boosting, random forest) were employed alongside an ensemble model. Analysis involved two rounds: considering all features and key attributes. The Synthetic Minority Oversampling Technique (SMOTE) was employed for data imbalance. Various metrics, including the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC) curve, area under the curve (AUC), and mean absolute error (MAE), were utilized to assess the efficacy of each predictive technique. The National Health and Nutrition Examination Survey (NHANES) dataset was employed. Results: The experimental results demonstrate that the ensemble model attained the highest accuracy (97%) and AUC (0.997) in comparison to existing models. The analysis revealed that specific crucial features possess substantial predictive significance within this model. Conclusion: The study underscores the potential of the ensemble model as a valuable tool for medical practitioners, leveraging cost-effective and readily obtainable laboratory test data to predict Hepatitis B with remarkable accuracy. By facilitating early diagnosis and intervention, this research presents a promising avenue to enhance patient outcomes in the context of Hepatitis B. © 2023, The Author(s) under exclusive licence to International Union for Physical and engineering sciences in Medicine (IUPESM).

关键词： Early detection Ensemble model Index terms— Hepatitis B Liver damage Machine learning SMOTE

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Handling Imbalanced Data Using a Cascade Model for Image-Based Human Action Recognition

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Journal of Computing science and engineering 2023年第4期17卷 207-215页

作者： Wahyono Suprapto Rezky, Adam Rokhman, Nur Jo, Kang-Hyun Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical Engineering University of Ulsan Ulsan Korea Republic of

Human action recognition plays a crucial role in intelligent monitoring systems, which are based on analyzing the possibility of anomalous events related to human behavior, such as theft, fights, and other incidents. However, by definition, anomalous events occur somewhat infrequently, thus leading to small and unbalanced data compared to data on other events. Such a data imbalance causes the human action recognition model to fail to produce optimal accuracy. To overcome the problem of imbalanced data, the typical methods used are oversampling and undersampling. However, these two methods are not considered to be very effective, because they cause the loss of a significant amount of information or deviations from reality. Therefore, the current paper proposes a cascade modeling strategy to address data imbalance problems, particularly in the case of human action recognition. The proposed strategy consists of several steps: preprocessing, feature extraction, modeling, and evaluation. The BAR dataset experiment found that the cascade model outperformed the other examined methods with an accuracy of 56.38%. However, there is still potential for further improvement through continued research. Copyright © 2023. The Korean Institute of Information Scientists and Engineers

关键词： Support vector machines

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Improved Coronary Artery Disease (CAD) Prediction using Machine Learning Method with Ensemble Technique 4

Improved Coronary Artery Disease (CAD) Prediction using Mach...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Mehmood, Waqas Khaliq, Abdul Qamar, Zoha Qudsia, Mammona Raza, Syed Saqib Capital University of Science & Technology Computer Science Department Islamabad Pakistan Capital University of Science & Technology Electrical Engineering Department Islamabad Pakistan Institute of Space Technology Electrical Engineering Department Islamabad Pakistan National University of Science & Technology Computer Science Department Islamabad Pakistan

ISBN: (纸本)9798350391862

Predicting Coronary Artery Disease (CAD) presents a critical and intricate challenge within medical science. Late-stage detection of CAD can gravely affect cardiac and vascular health, often leading to obstructions in the veins and perilous conditions. The application of Machine Learning (ML) in assessing CAD risks is pivotal for the early identification and mitigation of these severe consequences. Our study introduces a voting classifier that integrates eight distinct classification algorithms, employing an ensemble method to refine the accuracy of CAD diagnosis. This approach incorporates supervised learning algorithms such as Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees, Bagging, Boosting, with multi model approach to construct a more generalized and robust predictive framework. A comparative evaluation with existing methodologies was performed to ascertain effectiveness of our proposed model. The experimental findings reveals that our proposed technique markedly enhances diagnostic precision, achieving an accuracy rate of 99% by leveraging the benefits of multi model aggregation in ensemble learning. The high accuracy of our ensemble and more generalized model not only improves diagnostic precision but also holds potential for better patient outcomes by facilitating earlier intervention and treatment. © 2024 IEEE.

关键词： Risk assessment

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A Measurement Study of the Ethereum Underlying P2P Network

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computers, Materials & Continua 2024年第1期78卷 515-532页

作者： Mohammad ZMasoud Yousef Jaradat Ahmad Manasrah Mohammad Alia Khaled Suwais Sally Almanasra Electrical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Mechanical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Computer Science Department Al-Zaytoonah University of JordanAmman11733Jordan Faculty of Computer Studies Arab Open UniversityRiyadh11681Saudi Arabia

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying *** was applied because it pioneered distributed applications,smart contracts,and ***,its application layer language“Solidity”is widely used in smart contracts across different public and private *** this end,we wrote a new Ethereum client based on Geth to collect Ethereum node ***,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine *** collected data has been compared with two other services that harvest the number of *** has collectedmore than 30% more than the other *** data trained a neural network model regarding time series to predict the number of online nodes in the *** findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change *** poses a question of the stability of the ***,historical data shows that the top ten countries with Ethereum clients have not changed since *** popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node *** results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other *** opens the door for developing new mechanisms to encourage users from these regions to contribute to this ***,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

关键词： Ethereum measurement ethereum client neural network time series forecasting web-scarping wayback machine blockchain

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A Host-based Intrusion Detection: Using Signature-based and AI-driven Anomaly Detection for Enhanced Cybersecurity∗ 4

A Host-based Intrusion Detection: Using Signature-based and ...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Rehman, Fazalur Mushtaq, Farhan Zaman, Hafsah National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan Department of Electrical Engineering Taxila Pakistan

ISBN: (纸本)9798350391862

This research introduces a Hybrid Intrusion Detection System (HIDS) that merges signature-based detection, with AI-powered anomaly detection to enhance the accuracy and effectiveness of identifying cyber threats. The proposed HIDS demonstrates an ability to detect uncommon and sophisticated cyber threats with an accuracy rate of 90.37%. By combining Gradient Boosting and K-Nearest Neighbors (KNN) algorithms the system improves detection precision, speeds up response times, and expands coverage across network traffic. This comprehensive approach overcomes the limitations of traditional methods by enabling threat responses while reducing false positive rates. The study highlights the potential of integrating signature-based and AI-driven techniques to strengthen cybersecurity defences which emphasizes the benefits of this approach. When the system detects a potential threat, alerts are sent to the Security Operations Center (SOC) or Network Operations Center (NOC), with details such as nature of the threat, and the affected system. This study establishes a foundation for real-time cyber threat detection and intelligence sharing in cloud environments, with future Hybrid IDS versions operating across multiple hosts and supported by a web service for cross-platform compatibility and centralized alert system. © 2024 IEEE.

关键词： Network intrusion

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DSTL:A dual-step transfer learning-based prediction model for next-generation intelligent cellular networks

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Intelligent and Converged Networks 2025年第1期6卷 82-101页

作者： Waqar A.Aziz Iacovos I.Ioannou Marios Lestas Vasos Vassiliou Department of Computer Science University of CyprusNicosia 2109Cyprusand also with CYENS Centre of ExcellenceNicosia 1076Cyprus Department of Electrical Engineering Frederick UniversityNicosia 1036Cyprus

Traffic modeling and prediction are indispensable to future extensive data-driven automated intelligent cellular *** contributes to proactive and autonomic network control operations within cellular *** methodologies typically rely on established prediction models designed for univariate and multivariate time series ***,these approaches often demand a substantial volume of training data and extensive computational resources for prediction model *** this study,we introduce a dual-step transfer learning(DSTL)-based prediction model specifically designed for the prediction of multivariate spatio-temporal cellular *** technique involves the categorization of gNodeBs(gNBs)into distinct clusters based on their traffic pattern *** of training the prediction model individually on each gNB,a base model is trained on the aggregated dataset of all the gNBs within a base cluster using a combination of recurrent neural network(RNN)and bidirectional long-short term memory(RNN-BLSTM)*** the first-step transfer learning(TL),the base model is provided to the gNBs within the base cluster and to the other clusters,where it undergoes the process of fine-tuning the intra-cluster aggregated *** the model is trained on the aggregated dataset within each cluster,it is provided to the gNBs within the respective cluster in the second-step *** model received by each gNB through the proposed DSTL technique either necessitates minimal fine-tuning or,in some cases,requires no further *** conduct extensive experiments on a real-world Telecom Italia cellular traffic *** results demonstrate that the proposed DSTL-based prediction model achieves a mean absolute percentage error of 2.97%,9.85%,and 9.73%in predicting spatio-temporal Internet,calling,and messaging traffic,respectively,while utilizing less computational resources and requiring less training time than traditional model training and

关键词： dual-step transfer learning(DSTL) multivariate spatio-temporal cellular traffic prediction bidirectional long-short term memory(RNN-BLSTM)

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Offloading Using Traditional Optimization and Machine Learning in Federated Cloud-Edge-Fog Systems: A Survey

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IEEE Communications Surveys and Tutorials 2023年第2期25卷 1199-1226页

作者： Kar, Binayak Yahya, Widhi Lin, Ying-Dar Ali, Asad National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering and Computer Science Hsinchu300 Taiwan University of Brawijaya Department of Computer Science Malang65145 Indonesia National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan

The huge amount of data generated by the Internet of Things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons. Cloud computing provides enhanced data storage and computing power but causes high communication latency. Edge and fog computing provide similar services with lower latency but limited capacity, capability, and coverage. A single computing paradigm cannot fulfill all the requirements of IoT devices and a federation between them is needed to extend their capacity, capability, and services. This federation is beneficial to both subscribers and providers and also reveals research issues in traffic offloading between clouds, edges, and fogs. Optimization has traditionally been used to solve the problem of traffic offloading. However, in such a complex federated system, traditional optimization cannot keep up with the strict latency requirements of decision-making, ranging from milliseconds to sub-seconds. Machine learning approaches, especially reinforcement learning, are consequently becoming popular because they could quickly solve offloading problems in dynamic environments with some unknown information. This study provides a novel federal classification between cloud, edge, and fog and presents a comprehensive research roadmap on offloading for different federated scenarios. We survey the relevant literature on the various optimization approaches used to solve this offloading problem and compare their salient features. We then provide a comprehensive survey on offloading in federated systems with machine learning approaches and the lessons learned as a result of these surveys. Finally, we outline several directions for future research and challenges that have to be faced in order to achieve such a federation. © 1998-2012 IEEE.

关键词： Fog computing

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Data-Driven Detection of Data Falsification Cyber-Attacks in Distributed Generation

Data-Driven Detection of Data Falsification Cyber-Attacks in...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Anin, Johnson Abdelsalam, Omar Nabil, Mahmoud Hu, Fei Alsharif, Ahmad University of Alabama Department of Electrical and Computer Engineering AL35487 United States Tennessee Tech University Department of Computer Science TN35405 United States University of North Carolina A&T Electrical and Computer Engineering NC27411 United States University of Alabama Department of Computer Science AL35487 United States Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt

ISBN: (纸本)9798350376715

The advancement of smart grid technology has enabled consumers to become active participants in electricity generation, particularly using renewable-based distributed energy resources. While this evolution offers economic benefits by allowing consumers to sell excess energy back to the grid, it has also opened the door to fraudulent activities. Generation theft, a malicious act where consumers report falsified higher energy generation readings for financial gain, has emerged as a significant challenge, leading to estimated annual losses of approximately $6 billion in the United States alone. In response to this issue, our paper introduces a novel approach to the detection of fraudulent electricity generation within the grid. Our primary contribution is the creation of a benchmark dataset, based on actual power usage and generation data from the Ausgrid dataset, which now includes simulated fraudulent activities alongside normal usage patterns. Our investigation further explores the statistical properties of power consumption patterns among fraudulent users, revealing a distinct lack of periodicity and predictability. By employing the day-to-day correlation matrix as an input to a 2D Convolutional Neural Network (CNN), our detection system significantly improves its ability to distinguish between legitimate and fraudulent electricity use. Additionally, we propose a hybrid deep learning model that merges the strengths of 2D CNNs with the sequential pattern recognition capabilities of Long Short-Term Memory (LSTM) networks. This approach effectively captures both spatial and temporal dynamics within the dataset, leading to a remarkable performance with a detection rate of 97.04%, a false alarm rate of 0.67%, and an overall accuracy of 99.70%. © 2024 IEEE.

关键词： Cyber attacks

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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A Reference Voltage Loop Operation Based ZQ Calibration Technique for Multi-Load High-Capacity NAND Flash Memory Interface

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IEEE Access 2025年 13卷 95563-95573页

作者： Lee, Jun-Ha Park, Jun-Eun Shin, Dong-Ho Lee, Kang Yoon Sungkyunkwan University Department of Semiconductor and Display Engineering Suwon Korea Republic of Samsung Electronics Co. Ltd Memory Division Hwaseong Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

This paper proposes a novel ZQ calibration method based on a reference voltage loop operation. ZQ calibration technology improves the integrity of signals transmitted on the channel by calibrating on-die termination (ODT) and output driver strength, which vary with process, voltage, and temperature (PVT), and plays an important role in the high-speed memory environment. However, the existing ZQ calibration method was designed for a single-die configuration, which can lead to calibration errors and potential operational failures in high-load environments driven by recent high-speed, high-capacity NAND Flash memory demands. To address this issue, this paper proposes a new reference voltage loop operation based ZQ calibration method that mitigates calibration errors caused by heavy loads. The proposed technique significantly reduces the impact of load fluctuations and enables stable and accurate impedance matching by switching the calibration reference node from the heavily-loaded ZQ node to a low-load reference voltage node within the die. In addition, a 2b/Cycle SAR-based ZQ code calculation method has been introduced to increase the calibration efficiency and speed. The proposed circuit is designed using a 28nm CMOS process with a supply voltage of 1V. As a result of measurements on prototype chips, it achieved a maximum voltage error of 13mV and power consumption of 8.2mW, even under a capacitive load of 4.7μF. © 2013 IEEE.

关键词： CMOS integrated circuits

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