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检索条件"主题词=Classification Algorithms"

共 26354 条记录，以下是4761-4770 订阅

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Improving Availability and Scalability for RDMA Load Balancing with In-network Reordering

Improving Availability and Scalability for RDMA Load Balanci...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jinbin Hu Ruiqian Li Shuying Rao Jin Wang Changsha University of Science and Technology Changsha China Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Remote Direct Memory Access (RDMA) is widely deployed in datacenter networks (DCNs) due to its ultra-low latency, high throughput, and low CPU overhead. Since RDMA is sensitive to out-of-order packets, the previous load balancing schemes designed based on TCP do not work well in RDMA networks. Recently proposed load balancing schemes focus on solving packet reordering within the network. However, the existing solutions cannot be extended in practice because the required queues far exceed common switch capabilities. In this paper, we propose a scalable and efficient load balancing called SELB to improve availability and scalability. SELB employs a clustering algorithm to categorize equal-cost paths and then reroutes traffic to the same cluster parallel paths to reduce the degree of out-of-order and improve queue utilization. The NS-3 simulation results demonstrate that SELB reduces the average flow completion time (FCT) and the 99th percentile FCT by up to 33% and 21%, respectively, compared to the state-of-the-art load balancing schemes.

关键词： Out of order Distributed processing Scalability Simulation Clustering algorithms Switches Load management Throughput classification algorithms

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Effectiveness of TLBO, SCA and WaOA for Epileptic Seizer Detection

Effectiveness of TLBO, SCA and WaOA for Epileptic Seizer Det...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Sumant Kumar Mohapatra Puspanjali Mallik K. P. Swain Alok Kumar Agarwal Rajat Bhardwaj Rajit Verma Department of ETC Trident Academy of Technology Bhubaneswar India Department of CSE Siksha ‘O' Anusandhan University Bhubaneswar India Dept.of ETC Trident Academy of Technology Bhubaneswar India School of Engineering and Technology Chitkara University Himachal Pradesh Dept. of Engineering and Technology ASET Amity University Bengaluru MM Institute of Management Maharishi Marksandeshwar (Deemed to be University) Mullana-Ambala Haryana India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Epilepsy is a brain-related disease caused due to imbalance of electricity flow in the brain. During the past two decades, researchers and neurologists have been trying their best to devise powerful EEG (electroencephalogram) devices. In the present scenario, metaheuristic optimization algorithms are integrated with machine learning algorithms to use in the seizure detection symptoms. This research studies three metaheuristic algorithms named teaching learning-based optimization algorithm (LBO), sine cosine algorithm (CSA), and Walrus Optimization algorithm (WaOA) with Wavelet Packet transform as the feature extraction technique, Analysis of Variance (ANOVA) as the feature selection technique and Least square SVM (LSSVM) as the classifier and concludes Walrus Optimization algorithm as the best among the three algorithms having average accuracy, sensitivity, specificity and area under curve as 98.52, 97.54, 93.88, and 0.98. To continue research, it uses the Bonn University EEG dataset. In the future, this result can be tested with other popularly used datasets and also integrated with the Internet of Medical Things (IoMT).

关键词： Support vector machines Machine learning algorithms Sensitivity Metaheuristics Feature extraction Wavelet analysis Electroencephalography Wavelet packets classification algorithms Analysis of variance

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Prediction of Technical Object State In Conditions of Unbalanced Classes on the Basis of Machine Learning Models

Prediction of Technical Object State In Conditions of Unbala...

引用

International Russian Automation Conference (RusAutoCon)

作者： Yuliya Kuvayskova Anastasiya Izyurova Olga Malenova Department of applied mathematics and informatics Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

This article describes the method of predicting the state of a technical object in conditions of unbalanced classes in order to provide timely warning about the violation of the objects operability and the possibility of an emergency situation on the basis of machine learning models. The method uses random forest and support vector algorithms as basic machine learning models. However, when applying machine learning models to predict critical situations in the operation of an object, there is often a problem of class imbalance in the source data, i.e., a small amount of information about the emergency state of the object. In such a case, the accuracy of predicting the state of the object is significantly reduced. To improve prediction accuracy, it is necessary to build the model in such a way as to minimize the number of errors in classifying critical situations in the operation of the object under conditions of class imbalance. To solve this problem, the proposed method uses sampling algorithms: the algorithm using Tomek links and the ADASYN algorithm. F- score and AUC ROC criteria are used to evaluate the quality of prediction. The study of the effectiveness of the method of predicting the state of a technical object in conditions of unbalanced classes is carried out on the example of assessing the state of a hydraulic system pump. It was shown that the application of the proposed method improved the value of quality metrics by 3.5%-21% compared to models without data balancing.

关键词： Training Measurement Machine learning algorithms Accuracy Predictive models Prediction algorithms Data models Vectors classification algorithms Random forests

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An Anti-electricity Theft Prediction Technique Based on Large-scale Electric Power Data

An Anti-electricity Theft Prediction Technique Based on Larg...

引用

Clean Energy and Electric Power Engineering (ICCEPE), International Conference on

作者： Heyang Sun Feng Sun Bin Zhang Yuhang Yan State Grid Liaoning Electric Power Research Institute Shenyang Liaoning Province China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

This research suggests a new approach to address the issue of low accuracy in conventional anti-theft prediction algorithms. To increase prediction accuracy, a constraint condition of line loss rate growth rate is first added and a sample of electricity theft data is built by using anomaly rules. Subsequently, the method utilizes four machine learning classification algorithms to establish predictive models for the datasets of voltage, current, and power factor, respectively. It is possible to predict potential future electricity theft behavior by learning patterns and features of historical data. This technique generates a list of customers suspected of electricity theft by combining users with abnormal data output with users with abnormal line loss. Taking into account a wide range of anomalous circumstances, this technique can identify customers suspected of electricity theft more precisely. The trial results show that this strategy works well in terms of prediction accuracy, particularly when it comes to effectively and practically detecting people who may be stealing electricity. This indicates that the method has great potential for practical application and can provide reliable anti-theft prediction methods for the power industry.

关键词： Electric potential Reactive power Accuracy Electricity Voltage Predictive models Prediction algorithms Data models classification algorithms Reliability

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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Al-Amain Fatema Tuj Janin Faizul Ahmed Robin Shifat Ahmed Khandaker Mohammad Mohi Uddin Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030.

关键词： Support vector machines Machine learning algorithms Accuracy Liver diseases Predictive models Prediction algorithms classification algorithms Viruses (medical) Medical diagnostic imaging Principal component analysis

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Analysis of Influencing Factors in Developing Exercise Prescription for Emergency Patients with Cardiovascular Diseases in the Elderly Based on Machine Learning

Analysis of Influencing Factors in Developing Exercise Presc...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Yan Sun Xiedong Song Xia Zeng Minerva De Ala Department of Emergency Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital Chengdu China School of Nursing Philippine Women's University Malate Manila Philippines School of Computer Science and Engineering Jining University Jining China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

The formulation of exercise prescription for elderly patients with emergency cardiovascular disease (CVD) is an important measure to prevent and slow down the incidence of CVD patients. Understanding the factors that influence exercise prescribing in elderly patients with emergency cardiovascular disease is essential for effective prevention and treatment strategies. This study employs machine learning techniques to analyze these factors. The dataset includes demographic information, lifestyle factors, and medical history. Data preprocessing addressed missing values and normalized features. The logistic regression algorithm classified individuals into risk groups based on similarity to others in the dataset. Additionally, the random forest and decision tree algorithms identified key features for predicting exercise prescription for emergency patients with CVD risk. The findings offer insights into exercise prescription for emergency patients with CVD factors in the elderly, aiding targeted interventions for better treatment outcomes.

关键词： Logistic regression Machine learning algorithms Prevention and mitigation Predictive models Prediction algorithms classification algorithms Cardiovascular diseases Older adults Random forests Regression tree analysis

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Malpractice Detection in Examination Hall using Deep Learning

Malpractice Detection in Examination Hall using Deep Learnin...

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International Conference on Inventive Computing and Informatics (ICICI)

作者： Aruna S K Madhumitha A Selvanayaki Kolandapalayam Shanmugam Senthil Kumar Thangavel Maiga Chang Department of Computer Science and Engineering School of Engineering and Technology Bangalore CHRIST University India Department of Mathematics and Computer Science Ashland University Ohia USA Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Computing Information and Systems Athabasca University Canada

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring.

关键词： YOLO Accuracy Plagiarism Real-time systems classification algorithms Object recognition Monitoring

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Enhanced ELMAN Neural Network Learning Using Sine Cosine Optimization

Enhanced ELMAN Neural Network Learning Using Sine Cosine Opt...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Ali Iqtidar Li Jianping Fatima Pervez Attaullah Narejo Jiaheng Luo Arafat Yeasin Abdus Saboor Liu Yulu School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

This paper explores an enhanced learning model of the Elman Neural Network with a view of addressing problems of local minima and slow convergence time by using the sine cosine algorithm. Elman Neural Network that is a sort of Artificial Neural Networks is also widely applied to most classification tasks due to its simplicity and effectiveness. However, it faces challenges like; it is slower in learning and when performing an optimization process it gets trapped in local Optima. The research presents a SCA-ElmanNN model which incorporates of exploration and exploitation aspect of sine cosine algorithm to improve Elman Neural Network performance. Using Thyroid dataset, the study examines the effectiveness of the proposed hybrid model with other models for the comparison purpose, including SCA-BPNN and SCA-ANN. The results clarify that upgraded SCA-ElmanNN model enhances the accuracy criteria and minimizes mean square error in both training and testing sets. The training set of Thyroid dataset had an accuracy of 99.9587% and mean square error of 0.000473411 with SCA-ElmanNN, outperforming all the assessed models. Thus, some amount of enhancement has been visible in the Thyroid dataset. This study shows that an integration of SCA with ElmanNN results to improved classification performance and highlights the applicability of meta-heuristic algorithms like Sine Cosine in handling of optimization problems within neural networks. The findings inform the future research on the integration of intelligent optimization algorithms for training artificial neural networks.

关键词： Training Accuracy Metaheuristics Artificial neural networks Mean square error methods classification algorithms Optimization Thyroid Genetic algorithms Testing

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Handwritten Signature Verification for Inspection Report of Special Equipment Based on Improved GANomaly

Handwritten Signature Verification for Inspection Report of ...

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Electronics and Communication, Network and Computer Technology (ECNCT), International Conference on

作者： Huaping Ren Heng Luo Jingbo Fu Chunyu Yan Zhangjian Wang Chongqing Special Equipment Testing and Research Institute Chongqing China School of Electrical Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

Special equipment in China is under the supervision of the special equipment inspection department, and the manufacturing and operation of special equipment are based on the special equipment inspection report. The inspection report, as the proof of inspection, represents the authority and credibility of the state. Currently, some of the special inspection reports have not yet adopted electronic reports, but have continued to use paper reports. In the paper reports, there are handwritten signatures of the inspectors, reviewers, and issuers. Where there are handwritten signatures, there is a possibility of incorrect signing, missing signing, and proxy signing. These situations are unacceptable. Based on this background, this paper designs a signature authenticity algorithm based on GANomaly. The algorithm, based on the baseline model, designs a multi-scale fusion mechanism, adopts the Inception structure, and improves the model's ability to identify details. Simultaneously, Coordinate Attention (CA) is added to the U-Net generator network. The improved GANomaly-based authenticity verification has an extremely high recall rate and advantages over other mainstream algorithms.

关键词： Training Heuristic algorithms Transfer learning Training data Inspection Generative adversarial networks Feature extraction Forgery classification algorithms Unsupervised learning

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Aqua algorithms Enhancement of Water Quality Prediction with ML Bagging Techniques

Aqua Algorithms Enhancement of Water Quality Prediction with...

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Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), International Conference on

作者： Vishnu Kant Kanwarpartap Singh Gill Sonal Malhotra Swati Devliyal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Ensuring the availability of clean water is crucial for sustaining life and enhancing public health. Accurate prediction of water quality parameters is vital for proactive water resource management and pollution control. We examine a broad range of water quality indicators, such as pH, dissolved oxygen, turbidity, and nutrient levels, across various regions. In this study, we employ bagging classifiers, a popular machine learning technique, to enhance the accuracy and reliability of our models. We anticipate achieving 76% accuracy with these classifiers. By utilizing different learning methods, including ensemble learning techniques like Bagging Classifiers, our research aims to improve water quality assessment. This work offers valuable insights for better environmental management and ensuring access to clean water for everyone.

关键词： Accuracy Water quality Turbidity Prediction algorithms Water pollution classification algorithms Ensemble learning Public healthcare Bagging Water resources

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