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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是1211-1220 订阅

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A Comprehensive Comparison of machine learning algorithms for Breast Cancer Prediction

A Comprehensive Comparison of Machine Learning Algorithms fo...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Premkumar Duraisamy Yuvaraj Natarajan Ebin N L Jawahar Raja P Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

The root cause of death for women is breast cancer. Early detection is essential for rectifying cancer. In this study, we explored the use of machine learning models for predicting breast cancer. We evaluated logistic regression, K-neighbors classifier, support vector machine, decision trees, random forest, and voting classifier using a dataset of breast cancer patients and clinical and demographic factors. Our results showed that the voting classifier had the highest accuracy, followed by the random forest and support vector machine. Age, family history, and breast density were essential factors influencing the development of breast cancer. These findings suggest that machine learning can be helpful in early detection and diagnosis and highlight the importance of considering these risk factors in screeningprograms.

关键词： Support vector machines machine learning algorithms Refining Predictive models Prediction algorithms Breast cancer History

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Performance Analysis of machine learning algorithms to Predict Cardiovascular Disease

Performance Analysis of Machine Learning Algorithms to Predi...

引用

International Conference for Convergence of Technology (I2CT)

作者： Hridya V Ramesh Rahul Krishnan Pathinarupothi Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

Globally the rate of heart disease has increased drastically due to unhealthy eating habits and reduced physical activities. It has become one of the significant causes of death worldwide. As per the reports of the world health organization(WHO), 31% of all deaths worldwide are caused by cardiovascular diseases. This demands the development of a system capable of early detection of cardiovascular diseases at an affordable cost. With this as the objective, multiple machine learning algorithms have been selected to evaluate their performance in the early detection of cardiovascular diseases. This work utilizes available data sets of an individual’s vital parameters, demographic data, and exercise parameters for predicting cardiovascular diseases. An extensive evaluation is performed to identify the best-suited supervised machine learning classifier that could predict cardiovascular diseases using the available datasets. This research work details the nine different classification algorithms utilized for this analysis. For each algorithm, the F1-score, precision, recall, accuracy, and Area Under the Receiver Operating Characteristics (AUROC) values for each model have been determined and compared with the rest of the algorithms. The results show that random forest and gradient boosting models outperform others and demonstrate an F1-Score of 0.88 and an AUROC value of 0.92, respectively. This showcases that doctors could utilize this technique for the early identification of cardiovascular diseases. This will provide the opportunity to offer adequate medical treatments early, thus saving lives.

关键词： Heart machine learning algorithms Medical treatment Receivers Predictive models Prediction algorithms Classification algorithms

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A comparative evaluation of machine learning algorithms for network anomaly detection

A comparative evaluation of machine learning algorithms for ...

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作者： Zhuoting Jiang Dietrich college Carnegie Mellon University

In this era of digital transformation,the importance of network anomaly detection has been amplified to safeguard the security and integrity of various vital *** applications include the protection of critical infrastructures,prevention of cyber-attacks,and upkeep of network performance,among *** study presents a thorough evaluation of an array of machine learning algorithms for the proficient detection of network *** algorithms include,but are not limited to,Random Forest,Multi-Layer Perceptron(MLP),and Support Vector machine(SVM).We illustrate an in-depth comparison between these chosen algorithms,analyzing their performance metrics,strengths,and weaknesses,with a specific focus on their practical applicability and influence on network security *** investigation into these machine learning algorithms exhibits potential advantages and constraints of employing such methodologies for network anomaly *** further shed light on the determinants that affect their performance in diverse *** on the exhaustive analysis,we provide suggestive guidelines for choosing the most suitable algorithm depending on specific use cases or *** study thus serves as a comprehensive guide to understanding the role and impact of machine learning in the critical field of network anomaly detection.

关键词： network anomaly detection machine learning algorithms

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Intelligent Door Lock System using Ensemble machine learning algorithms

Intelligent Door Lock System using Ensemble Machine Learning...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Poonkuzhali Ramadoss Thilagavathi A Sakthisaran S Subalakshmi P Department of Information Technology K. S. Rangasamy College of Technology Tiruchengode Tamilnadu

The Internet of Things (IoT) is now a critical component of home automation and security, particularly for door lock systems. Face recognition powered by IoT is becoming more popular because it is dependable and simple to use. To prevent algorithmic errors and produce enhanced accuracy than existing systems, our system employs a facial recognition door lock with the integration of three algorithms on a single camera. The system detects unauthorized people near the entrance and notifies the user via a mobile app. For hardware communication, we used a camera module, a Raspberry PI, passive infrared motion sensors, and wireless internet access. The Python and OpenCV libraries are used in the algorithm's development. When the motion sensor detects activity in front of the door, the camera is activated, and the system gathers camera feeds and sends the images to three algorithms for analysis. If any two or all of the algorithm's results match the image in the database, the door unlocks; otherwise, it remains locked. The development of this system aims to enhance accuracy by combining three algorithms and to provide consumers with data about what is happening in front of their home via a smart door lock system.

关键词： Wireless communication Wireless sensor networks Home automation machine learning algorithms Face recognition Cameras Motion detection

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Solar Photovoltaic Panels Detection Using machine learning algorithms

Solar Photovoltaic Panels Detection Using Machine Learning A...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Dania Salameh Riham Dannoun Isra Zawahra Mutaz Rasmi Abu Sara Jawad H Alkhateeb Sameh Taqatqa Mohammad Kanan Faculty of Engineering and Information Technology Palestine Ahliya University Bethlehem Palestine College of Computer Engineering and Science Prince Mohammad Bin Fahd University Khobar Saudi Arabia University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Solar Photovoltaic Panels Detection Using machine learning algorithms. This research paper deals with designing and developing algorithms capable of automatically recognizing small-sized solar photovoltaic arrays in light of high-resolution atmospheric data. Achieving this automatic recognition is crucial to provide a faster and lower-cost alternative for obtaining vital in-formation about small solar, such as geographic location, capacity, and energy production efficiency. This paper exploits machine learning algorithms to achieve the most of aerial data. This research aims to provide an effective solution that improves efficiency and accuracy in identifying and inspecting small solar photovoltaic arrays. By using a dataset of measurements of aerial images, this research presents a variety of machine-learning algorithms that aim to verify whether the measurements of an object captured in the image are identical to a polygon measurement. The research begins by presenting the linear regression algorithm, the random forest algorithm, the decision tree algorithm, and the KNN algorithm. The results indicate that the linear regression algorithm significantly outperforms other algorithms, highlighting its promising potential in identifying solar cell arrays.

关键词： machine learning algorithms Accuracy Linear regression Solar energy Nearest neighbor methods Data models Decision trees

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machine learning algorithms in WSNs and its Applications

Machine Learning Algorithms in WSNs and its Applications

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Archana R Raut S. P. Khandait G H Raisoni College of Engineering Nagpur India KDK College of Engineering Nagpur India

ISBN: (纸本)9781665420419

Wireless sensor network (WSN) the unique and utmost encouraging tools for monitoring the real-time applications. It has been utilized in various areas particularly for offering real-time monitoring and control applications which attempts to monitor and record the environmental parameters and takes the appropriate decisions on time in a difficult situation. In recent enlargements machine learning (ML) techniques has been used to solve different problems in WSNs to ensure that good decisions can be made in the complex situations in time. Applying ML will help in boosting the efficiency of WSNs, as well as limiting humanoid intervention or re-programming. We have studied previous work for addressing the issues in Quality of Service (QoS) provisioning in WSNs. In addition we done the survey of ML based techniques used to address the issues in WSNs in the recent era.

关键词： Knowledge engineering Wireless sensor networks machine learning algorithms Limiting Mission critical systems Humanoid robots Quality of service

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Detecting Arrhythmia Heart Condition using Supervised machine learning algorithms

Detecting Arrhythmia Heart Condition using Supervised Machin...

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Engineering Informatics, IEEE

作者： Fadhil Ahmed Abdul Kadar S. Sharon Priya W. Aisha Banu G. Aarthi B. Yogesh Kumar Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338539

― Arrhythmia is a common heart condition commonly due to irregular heartbeats, which can have serious implications and significant threats on a person's health. The heart can beat excessively quickly, too slowly, or irregularly as a result of disturbances in the electrical impulses that regulate heart contractions, which is known as an arrhythmia. For effective medical intervention it is crucial to detect and diagnose arrhythmia at an early stage. machine learning algorithms have shown positive results in automating the arrhythmia detection process in recent years. The advantages of using machine learning algorithms include accuracy in handling and analyzing complex ECG patterns, early arrhythmia detection, personalized real-time monitoring, reduced human error, efficient big data handling and resource optimization. machine learning algorithms can provide clinicians with additional information and insights to aid in their decisionmaking process. This can facilitate more informed treatment plans and interventions. In order to achieve accurate detection rates, this research investigates the use of different machine learning algorithms on the MIT-BIH Arrhythmia Database. With a primary focus on accuracy, precision, recall, and F1-Score, this study compares multiple algorithms in detail, including Random Forest (RF) Classification, Support Vector machines (SVM), K-Nearest Neighbor (KNN), and Decision Tree Classification using the CART (Classification and Regression Trees) algorithm. For the automated strategy Random Forest Classifier can predict unexpected sudden cardiac death with an average accuracy of 98.91%. Our results demonstrate the potential of machine learning techniques in providing valuable assistance to healthcare professionals in diagnosing arrhythmia effectively and leading to more effective treatment approaches. They also contribute to research, innovation and continuous improvement in patient health care.

关键词： Heart Support vector machines Technological innovation machine learning algorithms Arrhythmia Medical services Prediction algorithms

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The Application of machine learning algorithms to the Classification of EEG Signals

The Application of Machine Learning Algorithms to the Classi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sasikumar Gurumoorthy Saritha Avikkal Rani M K Radha Rajesh N Department of Computer Science and Engineering J.J.College of Engineering and Technology Trichy India Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Electronics and Communication Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In order to study cognitive states and detect deficiencies in thinking, planning, and judgement, automated cognitive assessment systems have been developed. Having access to the opportunity to collaborate with experts in their working environment is another benefit of these platforms, particularly in services where decisions have direct effects on people's lives. This dissertation assesses the brain's reaction to mental tasks including motor imagery, attention, working memory, mental fatigue, and attention deficit hyperactivity disorder (ADHD). Electroencephalography (EEG), which encodes neuron activity as action potentials, is a popular method for analyzing these cognitive functions. These EEG data are processed using state-of-the-art machine learning algorithms, automating cognitive ability testing in the process. Recent state-of-the-art approaches have shown problems with high dimensionality, irrelevant channels, subject and task variability, generalizability, and interpretability. Because of this, classifications of intelligence are often inaccurate. The use of meta-heuristics to the solution of both continuous and binary optimization problems was found to be very fruitful in this scenario. Despite increased performance in EEG signal processing for other applications, these methods have seen very little exploration in the field of cognitive evaluation. This encourages more research into these meta-heuristics approaches to intelligence testing. It is important to look for the most appropriate optimization strategy for a certain task, since all of them will not work in every circumstance. In order to address problems in cognitive testing, this research looks on parameter-free optimization algorithms that are tailored to each kind of test. One of the main problems that hinders the effectiveness of ML models is its dimensionality. Another is its lack of generalizability. The overarching goal of this proposed work is to build and construct an effective channel select

关键词： Visualization machine learning algorithms Metaheuristics Brain modeling Feature extraction Electroencephalography Task analysis

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Comparison of machine learning algorithms for classification of Late Onset Alzheimer's disease

Comparison of Machine Learning Algorithms for classification...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Abbas Saad Alatrany Abir Hussain Saad S J Alatrany Jamila Mustafina Dhiya Al-Jumeily School of Computer Science and Mathematics Liverpool John Moores University Liverpool UK University of Information Technology and Communications Baghdad Iraq Department of Electrical Engineering University of Sharjah Sharjah UAE Imam Ja'afar Al-Sadiq University Baghdad Iraq Kazan Federal University Kazan Russia

Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.

关键词： Neuroimaging machine learning algorithms machine learning Benchmark testing Aging Genetics Linear discriminant analysis

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Detection of Credit Card Fraudulent Transactions Utilizing machine learning algorithms

Detection of Credit Card Fraudulent Transactions Utilizing M...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Asifuddin Nasiruddin Ahmed Ravinder Saini Dept. of Computer Science Chandigarh University Chandigarh India

Over the last several years, fraudulent credit card transactions have increased significantly. For financial institutions, credit card fraud is a significant problem, and reliable detection of fraud is generally challenging. According to annual study conducted in 2021, more than 50% of Americans have had experienced credit and debit card frauds, and beyond 13% of individual who utilize these cards do so on a regular basis. This corresponds to 127 million Americans that have roughly once been the victim of credit card fraud. Using the traditional technique, it is highly challenging and time-consuming to detect such fraud occurring across a large database. An effective technique to handle this kind of issue is by using technology such as AI and machine learning to create a fraud detection system which is well automated to identify and categorize such occurrences. This paper presents six supervised machine learning algorithms as Naive Bayes, SVM, Random Forest, KNN, Logistic Regression and XGBoost which is used create a classification model that correctly identifies such fraudulent. After evaluating all these algorithms, finding shows that Support vector machine is the most reliable model among all in terms of classifying fraudulent and non-fraudulent transactions gaining the highest accuracy results.

关键词： Support vector machines Deep learning Technological innovation machine learning algorithms Credit cards Fraud Classification algorithms

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