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检索条件"主题词=Machine Learning Algorithms"

共 25882 条记录，以下是1021-1030 订阅

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Advancing Farming Practices: machine learning algorithms for Optimal Crop Selection

Advancing Farming Practices: Machine Learning Algorithms for...

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： Shaleen Bhatnagar Archit Chadalawada Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

This study underscores the critical importance of the agricultural sector for ensuring global food security and economic resilience, emphasizing the role of machine learning (ML)-enhanced crop recommendation systems. These systems are increasingly crucial for guiding farmers in choosing crops that are best suited to their unique soil and environmental conditions. Through the analysis of a comprehensive dataset, including soil composition, climate variations, historical crop yields, and agricultural practices, this research evaluates the performance of several ML algorithms–such as neural networks, decision trees, random forests, and support vector machines–in forecasting the most appropriate crops. Training on historical data allows these algorithms to decipher the complex dynamics between environmental factors and crop results. The success of these models is gauged by metrics including accuracy, precision, recall, and F1-score. This study also delves into the models' interpretability, offering crucial insights to both agricultural practitioners and researchers. Moreover, it presents the creation of a user-friendly web application that applies these ML models to provide personalized crop recommendations, requiring user inputs like geographical location, soil characteristics, and weather data. The findings reveal that ML algorithms can significantly empower farmers with knowledge to select suitable crops, thereby boosting agricultural efficiency, optimizing the use of resources, and enhancing the sustainability of agricultural operations. The research highlights the necessity of selecting the right ML algorithms and preprocessing techniques for achieving superior results. Notably, we have enhanced the accuracy of the random forest algorithm to 99.92% and ADABOOST with the Decision Tree Classifier to 99.02%. This contribution to precision agriculture showcases the practical use of ML in crop recommendation systems as a technological approach to addressing agricultural

关键词： Accuracy machine learning algorithms Heuristic algorithms Biological system modeling Crops Soil Boosting

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Intelligent Digital Construction Component Systems Based on machine learning algorithms

Intelligent Digital Construction Component Systems Based on ...

引用

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Yalong Mao Zhe Lai Lukui Huang School of Architecture South China University of Technology Guangzhou China School of Accounting & Auditing Guangxi University of Economics & Finance Nanning China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Building Information Modeling (BIM) has gained significant attention in traditional construction, and its importance has been amplified by the integration of machine learning algorithms. However, existing component systems struggle to achieve efficient automatic planning with complex assembly tasks and information data. Insufficient interaction among functional modules leads to mismatched assembly information within the system and the requirements of actual construction projects. To address these challenges, we propose an intelligent digital construction component system that uses machine learning algorithms for automatic classification of architectural components during assembly data collection, categorization, and production processes. Our system testing on Bai ethnic wooden house components dataset reveals that the system achieves an average feasible probability of 83.0775% for the classification planning of corridor components and 81.3625% for path planning. These results demonstrate the great potential and operational effectiveness of our system in the preservation of historical heritage.

关键词： System testing machine learning algorithms Systems operation Production Data collection Path planning Planning

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A Various Approaches of machine learning algorithms for Kidney Disease Prediction

A Various Approaches of Machine Learning Algorithms for Kidn...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： G. Gomathi Sumathy V P. Sivakamasundari R. Deepa Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur TN Department of Information Technology St. Joseph's Institute of Technology Chennai TN

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Chronic kidney disease is a serious health issue that affects people around the world. Proper diagnosis and treatment must be initiated as soon as feasible. The primary purpose of this research is to detect the existence or absence of CKD in the human body by analyzing a number of features obtained from a few medical tests. This research presents an ML-CKDP model that aims to improve dataset preprocessing for CKD classification and provide a web-based application for prediction. The proposed model includes a full data pretreatment protocol for transforming categorical variables to numerical values, imputing missing data, and normalizing using Min-Max scaling. To improve the datasets, a variety of techniques are used, including correlation, chi-squared, variance threshold, recursive feature elimination, sequential forward selection, lasso regression, and ridge regression. To predict CKD, the model uses four classifiers: K-Neighbors (KNC), Random Forest Classifiers (RFC), XgBoost (XgB), and Gausian Naive Bayes (GNB). The models' effectiveness is evaluated by testing their accuracy, examining the confusion matrix, and computing the Precision, Recall, F1 score, and support. In contrast to other algorithms performing on the same dataset, K-Neighbors classifiers (KNC) achieve an 89% accuracy rate, Random Forest Classifiers (RFC) and Gausian Naive Bayes (GNB) produce an 87% accuracy rate, and XgBoost (XgB) achieve a 99% accuracy rate to predict CKDs. The XgBoost classifier has the highest performance indicators. This study concludes that current advances in machine learning, as well as the use of predictive modeling.

关键词： Accuracy machine learning algorithms Computational modeling Predictive models Prediction algorithms Feature extraction Chronic kidney disease Numerical models Bayes methods Random forests

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Strategies for Effective Malware Analysis and Detection Using machine learning algorithms

Strategies for Effective Malware Analysis and Detection Usin...

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

作者： Abin Roy Sadia Hossain Fahmida Afroze Dipti Bahauddin Ahmmed Md. Safayet Islam Md. Sultan Moheuddin Ahmed Wasif Reza Mohammad Shamsul Arefin Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

This research explores the issues that modern digital environments face due to advanced and widespread malware. The study focuses on issues related to feature selection, model efficiency, and the effectiveness of machine learning algorithms in categorizing Android malware because it acknowledges the drawbacks of conventional detection techniques in keeping up with the rapid evolution of threats. The main goals are identifying malware features, and creating optimized machine-learning models like Lightgbm, Xgboost, Decision-Tree, Random-Forest, etc. for real-world deployment. This study has achieved early successes in precise model development, significant feature identification, and optimization for a balanced accuracy-efficiency by utilizing the dataset, feature extraction, and cross-validated machine learning algorithms. Notably, the introduction of the HybridVRKD machine learning approach in this research lays the foundation for future developments in the field of cybersecurity making a significant contribution to threat identification. In every category, the Hybrid-VKRD model demonstrated 98% improved accuracy, making it the top performer.

关键词： Performance evaluation Analytical models machine learning algorithms Accuracy machine learning Feature extraction Malware Computer security Random forests Testing

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Performance Comparison of machine learning algorithms for Online Handwriting Recognition: A Brief Analysis

Performance Comparison of Machine Learning Algorithms for On...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Mamta Gurpreet Singh Punit Soni Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

An online handwriting recognition system is a software application that enables the conversion of handwritten text or symbols into digital form. This system utilizes various technologies such as the processing of images, recognition of patterns, the context of machine learning, and computational intelligence to interpret handwritten content accurately. In this system, users input their handwriting through a stylus or mouse, and the software recognizes information and converts it into machine-readable text. The system can be used for a wide range of applications, including the processing of documents, note-taking, and signature verification that impacts the online handwriting system. The accuracy and efficiency of online handwriting recognition systems have improved due to technological breakthroughs in research development, making them a popular choice for various industrial innovation.

关键词： Handwriting recognition Technological innovation Accuracy machine learning algorithms Image recognition Text recognition Symbols machine learning Software Expert systems

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Improving Weather Forecast Accuracy Using Hybrid machine learning algorithms

Improving Weather Forecast Accuracy Using Hybrid Machine Lea...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Naga Charitavya Madala Sai Durga Saradhi Pranu Deepak Tallapudi Venkata Srikari Malladi Durga Mahesh Muthinti Srilatha Tokala Murali Krishn Enduri Department of CSE SRM University-AP

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Weather prediction, particularly for rainfall, temperature and relative humidity (RH%), is critical for climate-sensitive industries such as agriculture and disaster relief. This paper introduces a predictive modeling framework based on a range of machine learning (ML) methods. In addition, hybrid models such as Catboost-Logistic Regression, XGboost-k-Nearest Neighbors (KNN) and Gaussian ProcessDecision Tree have been investigated to improve prediction accuracy. Using a 10-year Gannavaram dataset, we focus on multi-label classifications (2 to 5 labels) of weather characteristics such as rainfall, temperature, and RH%. Notably, the Gaussian Process consistently predicted rainfall with 100% accuracy, while hybrid models such as CatboostLogistic Regression and XGBoost-KNN performed well across a variety of criteria. The combination of these hybrid models and standalone ML algorithms has considerably increased the resilience of weather forecasting. Our research highlights the effectiveness of combining machine learning models to improve predictive accuracy, offering a valuable contribution to realtime weather prediction systems.

关键词： Accuracy Rain machine learning algorithms Computational modeling Weather forecasting Humidity machine learning Gaussian processes Predictive models Data models

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An Ensemble of machine learning algorithms for Detecting Fake News

An Ensemble of Machine Learning Algorithms for Detecting Fak...

引用

Future machine learning and Data Science (FMLDS), IEEE International Conference on

作者： Chayan Paul Nandan Banerji Bikash Debnath Baisakhi Chakraborty Dept. of CSE Swami Vivekananda University Barakpore Kolkata West Bengal India Dept. of CSE Sikkim Manipal Institute of Technology SMU Sikkim India Dept. of CSE School of Engineering Brainware University Kolkata India Dept. of CSE National Institute of Technology Durgapur Durgapur India

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

The collection of false updates propagated through various media in disguise of genuine information is considered fake news. Society might be negatively impacted by the unmonitored propagation of these updates. A recent area of interest for machine learning and natural language processing academics is the identification of fake news. The main objective of this research is to develop systems to determine the news items that are fake in the large collection of news updates everyone receives in their day-to-day life. There have been a good number of research works in the area, but most of them are done using classical machine learning algorithms. This work aims to solve this problem using an ensemble of three machine learning algorithms: SVM, random forest classifier, and extra tree classifier. An open-source data set was taken to train the ensemble of the classifiers. Before training the model, the data set was passed through a sequence of state-of-the-art data preprocessing steps. The data set was found to be imbalanced and for that, the standard class balancing was done to make sure that the models were not biased to a particular class. The accuracy obtained through the ensemble classifier is 94.6%. A comparison of the result was done with the pre-existing research works in this field and it was found the current work produces better accuracy.

关键词： Deep learning Support vector machines Training machine learning algorithms Accuracy Data models Classification algorithms Fake news Random forests Standards

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App Based Cardiovascular Disease Prediction using machine learning algorithms

App Based Cardiovascular Disease Prediction using Machine Le...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： B M Manjula C O Dhanush G H Gurudeep M Y Likhith Department of Electronics and communication Engineering Nitte Meenakshi institute of Technology Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Study investigates machine learning (ML) algorithms for early detection of cardiovascular diseases (CVDs), given their significant global impact. Utilizing demographic data, medical history, and lab results, algorithms like Logistic Regression, Support Vector machine, Decision Tree, Random Forest, and K-Nearest Neighbors are examined. An ensemble model, incorporating Random Forest, is developed for improved accuracy, initially achieving 95%. Hyperparameter optimization boosts accuracy to 99%. An Android app interface facilitates easy access to the predictive model, empowering users for CVD risk assessment. These findings underscore ML’ s potential in early CVD detection, offering advancements in predictive healthcare. This contributes to addressing CVD burdens and improving public health outcomes.

关键词： Support vector machines machine learning algorithms Predictive models Prediction algorithms Cardiovascular diseases Risk management Public healthcare

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Comparative Analysis of machine learning algorithms and Neural Networks in Early Detection of Breast Cancer

Comparative Analysis of Machine Learning Algorithms and Neur...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： Lokesh Mane Veera Reddy Aduru Vamsi Siliveru Hemanth Kollu Hiranya Vardhan Gogulamudi Electrical and Electronics Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

The fact that breast cancer disproportionately affects women is one of the reasons why it leads to death among a growing number of them. The process of identifying breast cancer is rather time-consuming which makes it crucial to have an appropriate system that can automatically detect the disease when still at its first stage due to lack of adequate resources. Today, Computer-Aided Design (CAD) has been implemented to make the detection of breast cancer easier. However common computer-aided design systems rely on features designed manually, which means they reduce the overall result. Deep learning-based techniques have recently been studied, using machine learning and AI approaches; A lot of machine learning and deep learning algorithms have particularly proven very useful in recent times to discriminate between benign tumors and malignant ones. In contrast to the ML approaches, deep learning and neural networks are better and need less human involvement in pattern recognition schemes. The data used for this analysis is the Wisconsin Breast Cancer Dataset with 569 samples and 32 features. This research aims at a comparative analysis of the results of various machine learning algorithms like Random Forest (RF), Decision Tree (DT), Naïve Bayes and the Feed Forward Neural Network (FNN) and to draw some advantageous insights from using a neural network in the diagnosis of the deadly cancer.

关键词： Deep learning Solid modeling machine learning algorithms Design automation Neural networks Medical services Breast cancer Bayes methods Decision trees Random forests

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Performance Analysis of machine learning algorithms for the Detection of Epilepsy

Performance Analysis of Machine Learning Algorithms for the ...

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Computer Vision and machine Intelligence (CVMI), International Conference on

作者： Rabel Guharoy Nanda Dulal Jana Suparna Biswas Suparna Karmakar SITAICS Rashtriya Raksha University Gandhinagar India CSE Dept. National Institute of Technology Durgapur India ECE Dept. Guru Nanak Institute of Technology Kolkata India IT Dept. Guru Nanak Institute of Technology Kolkata India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Manual detection of epileptic seizure is a critical task by monitoring electroencephalogram (EEG) signals. It is also a very laborious job and requires highly trained experts. machine learning based approaches are widely used to solve the above said problem due to its capability to detect seizure by processing long length of data sequences and it also provides a high detection rate. This study provides a machine learning based approach in the spatial domain to find the epileptic seizure patient. We have used three different models and two various dimensionality reduction techniques; Principal Component Analysis, and Linear Discriminant Analysis (LDA). After dimensionality reduction features are fed to the different classifiers, Support Vector machine (SVM), Logistic regression (LR), and K-Nearest Neighbors (K-NN) to diagnose the epilepsy automatically. This proposed method yielded a maximum accuracy of $\mathbf{9 6 \%}$ for the combination of PCA with SVM.

关键词： Support vector machines Dimensionality reduction machine learning algorithms Epilepsy Manuals Nearest neighbor methods Performance analysis Monitoring machine intelligence Principal component analysis

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