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检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是571-580 订阅

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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...

引用

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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Epileptic State Prediction using Phase Space Domain and machine learning algorithms

Epileptic State Prediction using Phase Space Domain and Mach...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Boluwatife Faremi Yedukondala Rao Veeranki Hugo F. Posada-Quintero Biomedical Engineering University of Connecticut Storrs USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Epilepsy is a disease of the brain that causes unprovoked or reflex seizures that affects millions of individuals worldwide. Traditionally, identifying epileptic states involves assessing neuroimaging scans or brain electrical signals recorded by EEG devices. However, due to the complex nature of these signals, there are growing demands for developing predictive systems that can improve the detection of this brain condition through unseen discriminating features. This study investigates predicting and detecting epileptic states by transforming 2-dimensional EEG time series data to the Phase space domain. The angular distance and probability density function between phase vectors were computed in the new domain to extract features. Renyi and Tsallis complex features were mainly extracted to train probabilistic, discriminatory, tree, and kernel-based models. The performance of the learning algorithms was evaluated using leaveone-subject-out cross-validation. Results revealed that the probabilistic models combined with complex features from the phase domain had a 91.5% accuracy compared to other algorithms. This result indicates the efficacy of the phase space domain for detecting and predicting epilepsy states.

关键词： machine learning algorithms Accuracy Computational modeling Epilepsy Feature extraction Brain modeling Probabilistic logic Prediction algorithms Electroencephalography Vectors

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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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A Comparative Analysis on a Diabetic Disease Prediction Considering Various machine learning algorithms

A Comparative Analysis on a Diabetic Disease Prediction Cons...

引用

Sustainable Expert Systems (ICSES), International Conference on

作者： Dilip Kumar Das Suman Chowdhury Mohammed Motaher Hossain Department of Mathematics International University of Business Agriculture and Technology Dhaka Bangladesh Department of Electrical and Electronic Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Quantitative Science International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Diabetes is a serious health concern almost all over the countries as it impaired many organs in human body and rate of affected person is gradually increasing. Several significant factors have been incorporated for prediction of diabetic diseases through the machine learning attribute. In this study a non-communicable disease, diabetes, is predicted based on several machine learning approaches like Support vector machine (SVM), Decision tree (DT), Logistic regression (LR), K-Nearest Neighbours (KNN), Gaussian Naive Byes(GNB),Random Forest(RF). Data set of Sylhet Diabetic Hospital, Sylhet in Bangladesh containing 520 samples is collected from online source. Comparing the classifiers SVM shows the maximum accuracy 99%. Since a diabetic patient suffers immensely throughout the life, diagnosis along with proper medication is important.

关键词： Support vector machines Radio frequency Logistic regression Accuracy machine learning algorithms Nearest neighbor methods Diabetes Random forests Medical diagnostic imaging Diseases

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Prediction of Voltage Discharge for Electric Tricycle Using machine learning algorithms

Prediction of Voltage Discharge for Electric Tricycle Using ...

引用

Cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV), International Conference on

作者： Salava V Satyanarayana Pillalamarri Madhavi Vadla NavaKishore Pedda Suresh Ogeti Electrical and Electronics Engineering Hyderabad Institute of Technology and Management Hyderabad India Computer Science and Data Science Hyderabad Institute of Technology and Management Hyderabad India

ISBN: (数字)9798350388664

ISBN: (纸本)9798350388671

The application of Supervised machine learning algorithms for the prediction of Voltage discharge in Electric Tricycles will be explored in this paper. This research investigates the effectiveness of machine learning in predicting the voltage discharge based on the various tricycle operational data. This includes collecting data on discharge voltage, potentially alongside factors like Motor temperature, power, and terrain. machine learning algorithms will predict the voltage discharge by training with data. This will ultimately enhance the user experience for electric tricycle riders by providing them with a reliable indication of their remaining range.

关键词： Training Temperature distribution machine learning algorithms Voltage Discharges (electric) User experience Reliability Forecasting Regression tree analysis Random forests

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EV Speed Tracking Using the Regression-Based Supervised machine learning algorithms

EV Speed Tracking Using the Regression-Based Supervised Mach...

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Women in Computing (InCoWoCo), International Conference for

作者： Mary Ann George Anna Merine George Dattaguru V. Kamath Ciji Pearl Kurian Department of E&C Manipal Academy of Higher Education Dubai University of Wollongong Dubai Department of E&C BMS Institute of Technology & Management Bengaluru India Department of Mechatronics MIT Manipal Academy of Higher Education (MAHE) India Department of E&E MIT Manipal Academy of Higher Education (MAHE) India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Electric vehicle (EV) technology is an emerging eco-friendly solution that has reshaped the transportation sector. This paper aims to design a controller for EV speed tracking using three regression-based supervised machine learning (ML) algorithms. Regression techniques such as Ensemble learning (EL), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) are used to develop ML-based controllers to predict the EV speed. The training data set for the ML models, including the predictors and response data, is extracted from the fuzzy-based controller scheme. Statistical measurement metrics, including the correlation coefficient (R), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE), are used to evaluate the performance of the models. The simulation of the proposed controllers is carried out in a MATLAB-Simulink environment. Simulation results demonstrate the GPR model's advantage over other ML models for tracking EV speed. From the statistical performance of the EL, GPR, and SVR models, it is observed that GPR model with Bayesian optimization gives the best performance with the lowest RMSE of 3.31 and MAE of 0.0026 compared to other models.

关键词： machine learning algorithms Biological system modeling Predictive models Prediction algorithms Electric vehicles Data models Real-time systems Bayes methods Data mining Optimization

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Soil Monitoring Using machine learning algorithms

Soil Monitoring Using Machine Learning Algorithms

引用

Smart Devices (ICSD), International Conference on

作者： Jyoti Agarwal Ishita Bhatt Sahaj Goyal Prabhdeep Singh Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Soil monitoring is important aspect of agriculture to grow good quality food. It is crucial aspect for famers and the environmental scientists as well. If soil is monitored properly then chances of good quality crop increased which reduces farmer’s tension and increase their income. This research paper is a step towards applying machine learning algorithms for soil monitoring. Decision Tree Regression, Random Forest Regression, Gradient Boosting Regressor and Stacking Regressor are such algorithms. Comparison analysis of these techniques are also done in this paper. This work will be helpful for farmers and land managers to accurately predict about the soil fertility which will further help them to take decision which crop should be grow as per the soil condition.

关键词： machine learning algorithms Stacking Crops Soil Prediction algorithms Vectors Smart devices Regression tree analysis Monitoring Random forests

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Automated Land Use Identification Using Hyper Spectral Data and machine learning algorithms

Automated Land Use Identification Using Hyper Spectral Data ...

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Govind Shay Sharma Lakshman K Arvind Kumar Pandey Department of Mathematics Vivekananda Global University Jaipur India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

this paper discusses the usage of devices, gaining knowledge of algorithms and hyper spectral facts to discover land use routinely. Hyper spectral information is used to broaden land use classifiers with the use of numerous machine-gaining knowledge techniques, which include aid Vector device (SVM) and Random forest (RF). These land use classifiers are then used to identify land use sorts from hyper spectral photographs. It is miles proven that the land use classifiers that are developed with the use of those gadget mastering algorithms provide correct and efficient identification of land use sorts with excessive type accuracy while compared to different conventional methods. The outcomes reveal the potential of the machine learning algorithms for automatic land use identity from hyper spectral pictures.

关键词： Support vector machines Radio frequency machine learning algorithms Medical services Vectors Classification algorithms Object recognition

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