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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是621-630 订阅

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Rainfall Prediction using machine learning algorithms

Rainfall Prediction using Machine Learning Algorithms

引用

Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Pallem Sumanth Sai T.M. Thiyagu Pallem Geetha Soumya Division of CSE Karunya Institute of Technology and Sciences Coimbatore India Division of AI and ML Karunya Institute of Technology and Sciences Coimbatore India Division of CSE Sumathi Reddy Institute of Technology for women Hasanparthy India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In several industries, including agriculture, water resource management, and disaster planning, rainfall prediction is essential. Because machine learning algorithms can examine historical rainfall data and provide precise projections, they have attracted a lot of interest. In this work, several meteorological characteristics including temperature, humidity, wind speed, and pressure are used to forecast rainfall patterns using machine learning algorithms. To determine which method best fits the prediction goal, a series of steps including feature selection, model training, assessment, and data pretreatment are involved. The models may be used to provide precise and timely forecasts by using historical rainfall data to study the intricate correlations between meteorological factors and rainfall patterns. Decision-making in a variety of weather-dependent industries is supported by the use of machine learning approaches into rainfall prediction, which provide a data-driven approach that improves forecast accuracy and dependability.

关键词： Rain machine learning algorithms Accuracy Decision making machine learning Predictive models Prediction algorithms

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Analysis and Ranking Resume using machine learning algorithms and NLP

Analysis and Ranking Resume using Machine Learning Algorithm...

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratik Chandrasen Deshmukh M. R. Bendre Dept. of Data Science Engineering Pravara Rural Engineering College Loni India Dept. of Computer Engineering Pravara Rural Engineering College Loni India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited.

关键词： machine learning algorithms Resumes Employment Natural languages machine learning Software Portals Recruitment Testing Qualifications

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Performance Analysis of machine learning algorithms for Breast Cancer Prediction in Medical Sectors

Performance Analysis of Machine Learning Algorithms for Brea...

引用

International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Vijay Shankar Sharma Atul Srivastava Siddhanta Kumar Singh Kamlesh Kumar Singh Department of Computer and Communication Engineering Manipal University Jaipur Amity School of Engineering and Technology AUUP Lucknow

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Breast cancer is a major health issue worldwide, with millions of new cases diagnosed annually. Early and accurate diagnoses help guide treatment and improve patient outcomes. machine learning, especially logistic regression, aids medical diagnosis. Various machine learning classifier algorithms have been utilized to detect the breast cancer prediction. Various classifiers have been compared in terms of Accuracy, Recall and F1-Score, in that Extra tree classifier results with the best accuracy of 99.17 % among all classifiers.

关键词： Logistic regression Accuracy machine learning algorithms Software algorithms Prediction algorithms Breast cancer Software Classification algorithms Random forests Medical diagnostic imaging

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Smart Pricing and Replenishment Decision Making Based on machine learning algorithms

Smart Pricing and Replenishment Decision Making Based on Mac...

引用

Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Jiacheng Miao Yangzhou University Yangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In the management of fresh food supermarkets, making optimal replenishment decisions for various vegetable categories daily is crucial when the specific items and purchase prices are not accurately known. To assist supermarkets in selecting reasonable sales combinations, this article formulates intelligent pricing and replenishment strategies for vegetable products using clustering, random forest models, and machine learning algorithms for modeling and problem solving. The main research contents are as follows: For problem one, data preprocessing is conducted initially. The distribution patterns of various categories are explored using wavelet analysis, leading to the conclusion that most data exhibit strong periodicity. The interrelationships among various categories are determined using the elbow method to identify the optimal parameters, followed using K-means clustering and hierarchical clustering to roughly divide the sales volumes of various categories into two classes. Upon finding that the data does not conform to a normal distribution, the Spearman correlation coefficient analysis method is applied for data processing. To analyze the distribution patterns of individual vegetable products, seasonal decomposition is used to analyze their periodic characteristics and certain anomalies. The interrelationships among individual vegetable products are evaluated using the Granger causality test, which shows that there is a significant impact on the sales volumes of most vegetable products, with only a small portion of the data showing weaker sales volume impacts between products. For problem two, a weighted sales unit price model is established based on the differences in sales unit prices of individual vegetable products. This is followed by constructing an XGBoost machine learning algorithm embedded with grid search to predict the sales volumes of various vegetable categories in the coming week, and the model is evaluated for six categories, showing excellent fit

关键词： Solid modeling Adaptation models machine learning algorithms Costs Time series analysis Pricing Predictive models Wavelet analysis Real-time systems Random forests

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Breast Cancer Detection Using Nanoparticle Sensor with machine learning algorithms

Breast Cancer Detection Using Nanoparticle Sensor with Machi...

引用

Intelligent Technologies (CONIT), International Conference on

作者： J.N.V.R. Swarup Kumar Charishma Karri Naga Srihitha Vatsavayi Harshitha Lekkala Saaketh Choudarapu Dept. of CSE GST GITAM Deemed to be University Visakhapatnam India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

This research is mainly focused on the early detection of breast cancer in women by their urine samples with nanoparticle sensors. It detects certain enzymes and proteins that can be the main cause of cancer by machine learning algorithms, correlation analysis, and logical regression methods and created a web-based breast cancer prediction website using started nanoparticle urine analysis data, which contains the DNA barcode sequences that match with DNA of patient urine which is user friendly to use for every person. This study mainly focuses on non-invasive cancer. We analyze non-invasive cancer by using urine sample test data collected from the patients, utilizing advanced technology to access DNA signatures associated with breast cancer biomarkers. Our approach involves the barcode of nanoparticle sensors, which matches the urine samples. After the urine samples match, they are applied to the sensors, translated into digital data, and transmitted to a centralized system. Now, the centralized system collaborates with datasets derived from previous breast cancer cases. The algorithm now analyses the urine data and identifies the patterns. Then, it correlates with different stages of breast cancer.

关键词： Analytical models machine learning algorithms Biological system modeling DNA machine learning Biomarkers Breast cancer

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An Analysis of Irrigation Management for Crops using machine learning algorithms

An Analysis of Irrigation Management for Crops using Machine...

引用

Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： R. Thamilselvan P. Natesan R. R. Rajalaxmi Prasanth D Nithish Swaminathan S Yuvapriya R Computer Science and Engineering Kongu Engineering College Perundurai Erode India Department of Computer Applications Kongu Engineering College Perundurai

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

In every nation, agriculture has boosted the economy. Agriculture is currently dealing with a number of difficulties, such as irrigation and water management. Crop irrigation plays a crucial role in agricultural production by providing water to crops in regions where rainfall alone is insufficient to meet their needs. This abstract provides an overview of crop irrigation, its importance, and various irrigation methods used in agriculture. Irrigation is a critical component of agricultural production, and advancements in machine learning algorithms have the potential to optimize irrigation practices for crop cultivation. Additionally, machine learning has developed in several industries to improve quality. machine learning algorithms like Random Forest, K-Nearest Neighbor, Support Vector machine and Decision Tree are used to find the various results. Internet of Things also plays a vital role in irrigation by making use of the sensors. This review's investigation was conducted using a variety of machine learning algorithms.

关键词： Performance evaluation Support vector machines Irrigation machine learning algorithms Production Vectors Real-time systems

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A Comparison of Earthquake Prediction using machine learning algorithms

A Comparison of Earthquake Prediction using Machine Learning...

引用

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

作者： Subashree Rath Gurulakshmi A B Aditya S Vaibhav R Naik Amith Pawar Vikram Karoor Computer Science and Engineering New Horizon College of Engineering Bengaluru India Electronics And Communication Engineering New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Predicting earthquakes is one of the most challenging scientific endeavors due to the complex and dynamic nature of Earth's crust and the multitude of factors influencing seismic activity. Earthquake prediction research remains an essential scientific frontier, offering hope for better mitigation strategies and reduced impact in the future. This study examines the prediction of earthquake magnitudes using machine learning models, namely Random Forest Regression, Decision Tree Regression, and Support Vector Regression. Data such as latitude, longitude and depth were used to test and train the models to forecast magnitude. The performance of each model was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). According to the results, the Support Vector Regression model outperforms the others.

关键词： Support vector machines machine learning algorithms Prevention and mitigation Earthquakes Predictive models Vectors Robustness Root mean square Regression tree analysis Random forests

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Optimizing Mental Health Status Prediction Models Using machine learning algorithms

Optimizing Mental Health Status Prediction Models Using Mach...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Dong Liu Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In contemporary society, mental health concerns are gaining heightened attention, and accurately forecasting individuals’ mental well-being has become a pivotal research focus. Traditional predictive models for mental health often struggle with issues like the complexity of data processing and insufficient prediction precision. However, the application of machine learning algorithms introduces new approaches to address these challenges. In the age of artificial intelligence, creative works are high-quality data sources for machine learning, raising crucial questions about copyright regulations. Balancing these rules to foster innovation in both cultural and technological sectors is a pressing issue. This study employs various machine learning techniques, such as support vector machines, decision trees, and deep learning algorithms, to thoroughly examine and evaluate mental health data. As artificial intelligence technology evolves rapidly and reshapes its applications, new research themes and methodologies continuously emerge within machine learning, with advances in deep learning and reinforcement learning driving the field forward. This paper not only reviews the essential techniques in the optimization process but also explores future research avenues and potential applications, aiming to provide valuable insights for ongoing research and practical efforts in mental health prediction.

关键词： Training machine learning algorithms Soft sensors Mental health Predictive models Feature extraction Data models Stability analysis Optimization Thermal stability

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Enhancing Kyphosis Disease Prediction: Evaluating machine learning algorithms Effectiveness

Enhancing Kyphosis Disease Prediction: Evaluating Machine Le...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Mr. K. Praveen Kumar Ramya Dondapati HemaLatha Kalavakollu Nanditha Sri Vyshnavi Patagarla Tanveer Basha Sheikh Peddiboyina T.V.V.K.P.Ch.Raju Department of Computer Science and Engineering Sri Vasavi Engineering College(A) Tadepalligudem

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Kyphosis, characterized by the inward arching of the upper back, is often colloquially referred to as “roundback” or “hunchback” when a noticeable curvature is present. This condition typically arises due to weakened spinal bones, leading to compression or fractures. In children or adolescents, other forms of kyphosis may be attributed to spinal abnormalities or a progressive twisting of the spinal bones. While kyphosis can manifest at any age, it is most prevalent in teenagers. Multiple factors, ranging from poor posture and developmental issues to structural abnormalities in the spine, can contribute to its occurrence. This research introduces a machine learning approach aimed at predicting kyphosis disease, with a focus on enhancing early detection and improving patient outcomes. This research intends to incorporate different machine-learning approaches to biological data, such as Decision Trees and Random Forests, evaluate and assess the granularity of these algorithms. The findings underscore the significance of machine learning (ML) as a valuable tool for addressing biological problems in a broader context.

关键词： Radio frequency machine learning algorithms Accuracy Biological system modeling Surgery Predictive models Prediction algorithms

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Analysis of Tree-based Ensemble machine learning algorithms for Determining Covid-19 Survival Rate

Analysis of Tree-based Ensemble Machine Learning Algorithms ...

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Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Manas Bisht Sulaksh Bisht Manisha Aeri Kumar Shantanu Ayushi Rathore Kamlesh C. Purohit Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To be University Dehradun India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

COVID-19 is a member of the Coronaviridae genus that has spread throughout the entire world. A virus that is not preventable causes unanticipated damage to human life, finances, and economic frameworks seen in all nations worldwide. Physicians and other health professionals have a lot of work associated with this disease. As of October 27, more than 1.1 million individuals had died from this viral epidemic, which has infected over 43 million people worldwide. The COVID-19 pandemic is rapidly spreading throughout India, where there are currently the 2 nd highest number of cases and third-highest number of fatalities worldwide. A model is created in this study to look at and assess how this sickness spreads. machine learning and the creation of computer-aided diagnostic tools can lessen this workload. Any scientific investigation into this illness will aid in its early management. The COVID-19 dataset was used in this experiment to build decision tree ensemble model. In order to handle the missing values and categorical data, we first carried out data pretreatment. Four different machine learning algorithms namely Random Forest Classifier, XG Boost Classifier, LGBM Classifier and Cat Boost Classifier have been considered. The numbers of confirmed cases, patients who recovered, and individuals who passed away from COVID-19 in Mexico within a specific time period were taken from a Kaggle dataset.

关键词： COVID-19 Training machine learning algorithms Accuracy Computational modeling Boosting Prediction algorithms Approximation algorithms Classification algorithms Random forests

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