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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是561-570 订阅

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A Review of machine learning algorithms for Detection of Breast Cancer

A Review of Machine Learning Algorithms for Detection of Bre...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Abhay Tale Aditya Barhate Prateek Verma Palash Gourshettiwar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of machine learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice

关键词： Support vector machines machine learning algorithms Sensitivity Reviews Precision medicine Ubiquitous computing Breast cancer Vectors Convolutional neural networks Random forests

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Digital Essay Assessment using machine learning algorithms

Digital Essay Assessment using Machine Learning Algorithms

引用

International Conference on Science Technology Engineering and Management (ICONSTEM)

作者： R. Johnsi G. Bharadwaja Kumar School of Computer Science and Engineering Vellore Institute of Technology Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Essay are considered as the most prominent way in assessing the student performance. Manual grading of the essay are considered to be a hectic tasks for both students and instructors due to various reasons such as time consuming, prone to subjectivity, inconsistencies and lack of reliability may be influenced by biases, such as prior knowledge of the student or their back ground. The solution to all these problems has been rectified by using Automated Essay Scoring (AES) system. This technical paper proposes a novel AES system that utilizes various machine learning (ML) algorithms such as Linear Regression, Lasso Regression, Ridge Regression, Gradient Boosting Regression, Bayesian Regression, Elastic Net Regression, Random Forest Regression, Decision Tree Regression, Gaussian Process Regression, Support Vector Regression, Polynomial Regression, XG Boost regression and XGBoost with Polynomial Features and discovered that XGBoost with Polynomial features achieved the best results with an average Quadratic Weighted Kappa (QWK) value of 0.68.

关键词： Support vector machines machine learning algorithms Manuals Polynomials Mathematics Reliability Task analysis

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Bird Species Classification and Prediction Using machine learning algorithms

Bird Species Classification and Prediction Using Machine Lea...

引用

IEEE Punecon

作者： Penchala Abhinav Sunitha Dhavale Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India School of Computer Engg. & Mathematical Sciences Defence Institute of Advanced Technology Pune

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

The BirdClef-2024 dataset, which includes acoustic recordings of 182 Western Ghats species, was used to classify and predict bird species in this study. Three machine learning algorithms were analyzed for their accuracy, precision, recall, and F1 score – K Nearest Neighbors (KNN), Random Forest, and Support Vector machine (SVM). The Random Forest model outperformed the other models in all metrics. The paper contains sections on dataset composition, preprocessing steps, algorithm implementations as well comparisons of the model’s performances. Such issues have been successfully addressed through advanced pre-processing techniques coupled with complex machine-learning algorithms despite the intricate nature of the sound signals. A Streamlit GUI that is user-friendly has been developed to facilitate real-time recognition of birds by breed; this can contribute to surveillance for environmental biodiversity conservation efforts.

关键词： Support vector machines machine learning algorithms Accuracy Biological system modeling Computational modeling Nearest neighbor methods Birds Recording Random forests Graphical user interfaces

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Comparative Analysis of Diagnosis of Non-Alcoholic Fatty Liver Disease (NAFLD) Using machine learning algorithms

Comparative Analysis of Diagnosis of Non-Alcoholic Fatty Liv...

引用

Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： Manshee Agarwal Sapna Sinha Laxman Singh Mehlawat Amity Institute of Information Technology Noida Uttar Pradesh India KIET Group of Institution Ghaziabad Uttar Pradesh India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Liver disease is one of the leading cause of deaths in the world. It could be reversed if detected at early stage and many lives could be saved thereby. Therefore, it is crucial to detect the liver disease at an early stage. The primary goal of this review is to present a thorough summary of recent research, which have been carried out using machine learning (ML) techniques. These techniques have been developed by researchers to help healthcare professionals to detect non-alcoholic fatty liver disease (NAFLD). In this study, several ML techniques viz., supervised, unsupervised, and semi-supervised reinforcement learning are covered, whose performance is compared in terms of sensitivity, accuracy, and precision. The prime objective of this study is to present a comparative analysis of different ML methods for the diagnosis and early prediction of liver disease in the medical images.

关键词： Sensitivity machine learning algorithms Accuracy Liver diseases Reviews Reinforcement learning Medical services

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A machine learning algorithms for Drug Identification: Enhancing Medication Safety in Thailand

A Machine Learning Algorithms for Drug Identification: Enhan...

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International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Sorayus Kittisorayut Sarayut Nonsiri Pichitchai Kamin Supawee Makdee Artificial Intelligence and Internet of Things Research Laboratory Thai-Nichi Institute of Technology Bangkok Thailand Faculty of Computer Science Ubon Ratchathani Rajabhat University Ubon Ratchathani Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Medication errors are one of the leading causes of patient harm in the healthcare system. Nowadays, there are many tools and solutions available to reduce the occurrence rate of medication errors that might be caused by Look-Alike, Sound-alike (LASA) drugs. This study focuses on the problem of drug identification by using a machine learning approach. First, a dataset is manually collected of commonly used drugs in Thailand. Then the object detection models are trained and evaluated based on the different version of pretrained YOLO models. The objective of this study is to develop and compare the performance of different versions of the trained model. The results show that YOLOv8-nano achieves the highest accuracy in drug identification, with a mAP score of 99.5%, a recall of 99.7%, and an F-l score of 99.4%, compared to other versions.

关键词： Drugs YOLO machine learning algorithms Software algorithms Medical services Safety Object recognition

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Detecting Counterfeit Indian Currency A Comparative Analysis of machine learning algorithms and Image Processing

Detecting Counterfeit Indian Currency A Comparative Analysis...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： M R V Vyshnavi N.L. Varshitha Konda V. S. Harshith Kumar Tripty Singh Mansi Sharma Suman Chatterjee Department of Computer Science Engineering Amrita School of Computing Bengaluru India Graphics Era (Deemed to be university) Dehradun India Department of Mechanical Engineering Kongju National University Cheonan India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify which precise time frame this fake process was prepared in, complex fakes render the use of validation techniques quite challenging. This paper has aimed at displaying and evaluating the overall outcome of the different strategies of image, and the acquired classifiers in the identification of the fake FI currency. The following are the subsequent of the other operation that was applied in order to achieve the objectives of the intended work; Image capture This is whereby the images to be used are taken by the appropriate camera or scanner. Others used include the Naïve Bayes, Support Vector Classifier and Gradient Descent and Artificial Neural Network in this paper. As in the conclusion, with the help of the mathematical formulation, it can be confirmed that in fact, within an experiment, Neural Networks do have a higher level of optimization, much more than an average image processing if it is provoked to identify fake notes. In other words if used and implemented with the best of the machine learning approach and best of the image processing method then solitaire for detection of the counterfeit currency can be made more dependable than all the combined methods stated above. The objective of this study is to build and compare the performance of different machine learning algorithms and image processing methodologies for the identification of fake Indian currency notes.

关键词： Economics Training machine learning algorithms machine learning Artificial neural networks Vectors Real-time systems Bayes methods Reliability Currencies

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Adaptive Methodology for Determining Frequency Control Law in Isolated Power Systems Based on machine learning algorithms

Adaptive Methodology for Determining Frequency Control Law i...

引用

IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Mihail D. Senyuk Viktor V. Klassen Vladislav V. Popovtsev Ural Power Engineering Institute Ural Federal University Ekaterinburg Russia

ISBN: (数字)9798331532024

ISBN: (纸本)9798331532031

Modern energy systems are undergoing significant changes due to the active implementation of renewable energy sources, control devices based on power electronics, the development of transient processes control systems and monitoring, as well as digitalization of production, transmission and distribution electricity processes. These changes contribute reduce the inertia of power systems and increase the speed of transient processes, as well as increase their stochasticity. Under these conditions, traditional power system emergency control devices can lead not only to non-optimal implementation of control actions, but also contribute to the development of cascading failures. The study presents a methodology for selecting a frequency control law in isolated power systems based on machine learning algorithms. This class of algorithms has significant speed and adaptability, which satisfies the conditions of modern power systems. For the case study, the standard IEEE39 digital power system model was used, for which a dataset was generated. Next, the task of feature selection were solved based on the Boruta algorithm and the k-nearest neighbor, random forest, extreme gradient boosting and artificial neural network algorithms were trained. As a result, the extreme gradient boosting algorithm showed the greatest accuracy and performance. The conclusions provide recommendations for the practical use of the developed methodology and directions for further research.

关键词： Training machine learning algorithms Accuracy Process control Power system stability Control systems Boosting Classification algorithms Transient analysis Frequency control

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Comparative Analysis of machine learning algorithms in Traffic Mainstream Control on Freeway Networks

Comparative Analysis of Machine Learning Algorithms in Traff...

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IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Mehran Amini Laszlo T. Koczy Department of Information Technology Szechenyi Istvan University Gyor Hungary

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Efficient management of mainstream traffic flow on freeway networks is a critical challenge in urban transportation, with significant implications for congestion mitigation and environmental sustainability. The purpose of this study is to address the problem of predicting traffic volumes and maintaining flow rates below critical densities, thereby preventing the onset of congestion on interconnected freeway systems. Motivated by the need for real-time traffic control strategies, this research employs machine learning algorithms to forecast traffic volumes, leveraging a comprehensive dataset of traffic patterns on freeways. In our approach, we conducted a comparative analysis of two advanced machine learning algorithms: Long Short-Term Memory (LSTM) networks, which are adept at modeling time-series data with long-range temporal dependencies, and Random Forest regression, known for its robust performance across diverse datasets. We enriched the traffic data through feature engineering, incorporating temporal variables, vehicular counts, and a calculated measure of proximity to critical density for the targeted freeway. Our findings indicate a markedly disparate performance between the algorithms. The LSTM model showed a moderate ability to capture the variance in traffic flow, with an R 2 score of 0.619. In contrast, the Random Forest model demonstrated exceptional predictive accuracy, achieving an R 2 of 0.998, and substantially outperforming the LSTM model in terms of both Mean Squared Error and Root Mean Squared Error.

关键词： machine learning algorithms Prevention and mitigation Green products Transportation Traffic control Predictive models Prediction algorithms

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Binary Classification of Epilepsy Using Classical machine learning algorithms and Ensemble learning Techniques

Binary Classification of Epilepsy Using Classical Machine Le...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Samarth Agrawal Krishnay Mudgal Shreya Agarwal Sanidhya Sharma Department of Computer Science Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh India Department of Mathematics Indian Institute of Technology Ponda Goa India Department of Computer Science Jaypee Institute of Information Technology Noida Uttar Pradesh India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Epilepsy is a neurological disorder characterized by recurrent seizures. Seizures are caused by abnormal electrical activity in the brain. Symptoms of epilepsy can vary widely, depending on the type of seizure and the part of the brain affected. Some people with epilepsy may experience only occasional seizures, unconsciousness, and temporary confusion while others may have frequent seizures that are difficult to control. Early detection of Epilepsy is possible through several form of input data, ranging from Video, Audio, Text, several physiological data (such as Heartbeat & Blood pressure) and ECG. In this paper we have used ECG recoding data and several machine learning algorithms to detect Epilepsy. In our Experiment, we have used ‘UCI Epileptic Seizure Recognition Dataset’ and to counter the Problem of Imbalance data set we have used SMOTE analysis. We have also done various Hyper parameter tuning processes to improve the overall result of the performance of the model. We have found that the Gradient Tree Boosting Algorithm Has Outperformed K- Nearest Neighbor (K-NN), Naive Bayes, Logistic Regression, Decision Tree (D.T.), Random Tree classifiers with 97.41% Accuracy, ROC = 0.974, and RMSE = 0.16.

关键词： machine learning algorithms Accuracy Epilepsy Electrocardiography Classification algorithms Ensemble learning Kernel

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Improving Space and Time Efficiency in Hadoop Architecture Using machine learning algorithms

Improving Space and Time Efficiency in Hadoop Architecture U...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Murali Vivekanandam Kiran Karasala Aptimized

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study explores the application of Hadoop Distributed File System (HDFS), MapReduce, and machine learning algorithms, along with Social Spider Optimization (SSO), to effectively analyze and utilize large volumes of unstructured data generated by social media platforms. The study focuses on analyzing user behavior and preferences within the context of online shopping, considering individual and business perspectives. The research proposes novel approaches, including the derby model, virtual local file system, a refined recommender system, and parallelized SSO, to efficiently handle various data formats and improve the performance of data processing tasks. The proposed methods demonstrate improved efficiency in terms of space and time requirements compared to existing approaches. This research contributes to the advancement of big data analytics techniques and has the potential to revolutionize how businesses understand and leverage user behavior in the digital age.

关键词： Training machine learning algorithms File systems Computational modeling Predictive models Parallel processing Boosting Data models Load modeling Business

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