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检索条件"主题词=Machine Learning Algorithms"

共 25882 条记录，以下是1031-1040 订阅

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Evaluating the Performance of Different machine learning algorithms for Crop Recommendation

Evaluating the Performance of Different Machine Learning Alg...

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Mudit Kumar Parth Sharma Radhika Pal Preeti Saini Supriya Shukla Department of AI and ML COER University Roorkee India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

A significant portion of Indians view agriculture as their main source of revenue. Crop production is a significant industry in our nation. Applications of machine learning are changing the data processing and decisioning process that have a significant influence on the world economy. The natural nutrients or physiochemical characteristics of the soil aid in the understanding of the dynamics of the soil ecosystem by the researchers and are essential in assisting farmers in making daily choices. In order to forecast and evaluate soil properties and make better decisions on agricultural soil health management, our work focuses on closely examining and assessing machine learning (ML) *** learning has entered the picture with the introduction of big data technologies and high-performance computers, creating new opportunities for data-intensive research in the interdisciplinary field of agricultural technology. On the other hand, single-component soil property predictions were not as accurate. In the long run, combining these technologies can have revolutionized contemporary agriculture by raising crop yields and reducing waste. Helping someone cultivate crops successfully to obtain a high yield is the aim of this activity. Estimating enhanced crop output through the application of machine learning algorithms, which are utilized in crop yield estimates, is one of the challenging issues facing the agricultural sector. We have implemented the five supervised learning models. Random Forest give the best accuracy score of 97%.

关键词： Computers Support vector machines machine learning algorithms Accuracy Profitability Soil properties Supervised learning Agriculture Random forests Crop yield

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A Review and Comprehensive Analysis of Recent Research in Crop Yield Prediction using machine learning algorithms

A Review and Comprehensive Analysis of Recent Research in Cr...

引用

IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Selvakumar R. K. Salehundam Sai Tanoj Tigulla Hrushitha Goud Department of Computer Science and Engineering CVR College Of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) CVR College Of Engineering Hyderabad India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Traditional farming based on conventional methods has been the pillar of agricultural practices for a long time. But, due to the escalating global population, unstable climatic patterns and limited resources, the efficiency of traditional methods is facing challenges. The introduction of data-driven and artificial intelligence based approaches involving machine learning and deep learning have gained significant popularity capturing the interest of numerous researchers. This review examines various algorithms, including Random Forest, Decision Trees, K-Nearest Neighbours, Lasso Regression, Stacked Regression, and XGBoost, along with deep learning techniques like LSTM, RNN, and SVM. The study highlights their performance across multiple metrics such as accuracy, RMSE, MAE, R 2 , and MSE. Notably, Random Forest emerges as a highly effective model due to its excellent performance in accuracy, RMSE, MAE, and R2. Lasso Regression and XGBoost also show remarkable results in RMSE and MSE, respectively. Deep learning models like LSTM, RNN, and SVM are recognized for their high accuracy, underlining their potential in handling complex agricultural data. Through an examination of diverse approaches and data sources, this review seeks to explain and define the capabilities of these innovative technologies for sustainable agrarian applications.

关键词： Deep learning Support vector machines machine learning algorithms Accuracy Reviews Prediction algorithms Crop yield

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A Study on machine learning algorithms Selection for Estimating the Impact of FIFA World Cups

A Study on Machine Learning Algorithms Selection for Estimat...

引用

International Research Conference on Smart Computing and Systems Engineering (SCSE)

作者： Napitiporn Manoli Masakazu Takahashi Yoshiyuki Matsuura Graduate School of Science and Technology for Innovation Yamaguchi University Yamaguchi Japan Graduate School of Innovation and Technology Management Yamaguchi University Yamaguchi Japan

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

This study aims to identify the most efficient and accurate machine learning models for precisely estimating the impacts of major sporting events (MSEs). To address these complex challenges, this study broadens the horizons of machine learning applications by integrating sophisticated selection techniques including Stepwise Regression, Support Vector machines (SVMs), Boosted Neural Networks, Generalized Regression Lasso, Bootstrap Forest, and K-Nearest Neighbors (KNN). This study highlights the diverse nature of variables, including long-term investments, visitor numbers, GDP, and sporting factors such as team scores and performance, using various machine learning models to identify the most suitable models for such datasets. It assessed the contribution of predictors and the significance of variables during the model training and validation phases. Results revealed that the stepwise regression method exhibited robust performance during the training phase. The neural-boosted model exhibited limitations in generalization, demonstrating the intricate nature of and obstacles associated with forecasting the diverse consequences of mega sports events.

关键词： Training Support vector machines machine learning algorithms Neural networks machine learning Forestry Predictive models

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Enhancing Security and Privacy of machine learning algorithms in Agricultural Automation

Enhancing Security and Privacy of Machine Learning Algorithm...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Amit Kumar Singh Vikas Kumar Kharbas S B Vinay Kumar Department of Journalisim & Mass Communication ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper specializes in enhancing the safety and privacy of machine-mastering algorithms in agricultural automation. With the emergence of digital generation in agricultural production, system mastering algorithms have emerged as essential to offer precision control in farm control operations. However, their security and privacy may be compromised due to assault vectors inclusive of opposed inputs, facts leakage assaults, and facts poisoning attacks. Several ability solutions are studied, which include the introduction of statistics anonymization strategies, getting the right of entry to manage mechanisms, cryptographic strategies, and software program and hardware protection tactics. This paper analyses the one-of-a-kind security and privacy enhancement techniques and affords a comprehensive discussion of their effectiveness. Sooner or later, the authors advise a complete solution that encompasses all the security and privacy enhancement approaches to shield the gadget studying algorithms in agricultural automation effectively.

关键词： Privacy Data privacy Automation machine learning algorithms Software algorithms Vectors Software Stakeholders Artificial intelligence Protection

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Assessment of Lead Contaminant in Ground Water using Image Processing and machine learning algorithms

Assessment of Lead Contaminant in Ground Water using Image P...

引用

International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Kalist V S. Poornapushpakala S. Barani M. Subramoniam School of EE Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Water is a precious gift from the nature which is very essential for every living being on the earth. However this valuable source is polluted by various activities of human which adversely affects the eco system. Hence monitoring the quality of the water is necessary in global scenario. The assessment of water quality is done through laboratory procedure which is time consuming and requires expertise. Hence this study attempts to develop simplistic methodology for the assessment of water quality. As an initial phase, the research focuses on assessing the toxicity level of the ground water particularly Lead. To estimate the metal contamination, Araceae species such as Lemna Minor which has phytoremediation property is grown in the synthetically prepared water sample of various concentrations of Lead. The growth of the plant is analyzed using image processing techniques and the toxicity level is classified using k-NN, SVM and Logistic regression algorithms. The contamination is classified in to two - low and high level. The classification accuracy achieved with these methodologies is 90 % for all the classifiers.

关键词： Support vector machines Toxicology machine learning algorithms Accuracy Image processing Water quality Lead

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Weather Station using Internet of Things and machine learning algorithms

Weather Station using Internet of Things and Machine Learnin...

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

作者： Parveen Badoni Suresh Kumar Utpal Shrivastava Manoj Wadhwa Parul Datta CSE Chandigarh University Mohali India CSE & CA Geeta University Panipat India CUSOET Chitkara University Solan HP India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

Weather uncertainty poses significant challenges to industries, including construction, major events (e.g., Wimbledon, IPL), and public health (e.g., heat events) this paper addresses these challenges by a new solution that introduces an "automated weather forecast station". Leverages IoT) technology The system uses advanced forecasting techniques such as ARIMA (Auto Regressive Integrated Moving Average) and deep learning to provide real-time weather data These by continuously collecting data from resources controlling the environmental sensors that have been deployed within a specific area It meets objectives and then the collected data is processed and made available through a user-friendly interface. While the concept of IoT-based weather forecasting is not new, this research goes beyond traditional applications. It extends its utility to smart cities and diverse industries, making it a valuable resource for individuals, organizations and government departments. By addressing climate-related challenges first, the program ensures safety and resilience in the face of climate change. The paper highlights the potential of these new solutions to improve decision-making processes, reduce climate-related problems, and enhance overall quality of life in urban and industrial settings as the world faces climate uncertainty increasing emphasis.

关键词： Internet of Things Deep learning Weather forecasting Data analysis Random forests Convolutional neural networks Climate change machine learning algorithms

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Role of machine learning algorithms in Analysis of Crop Disease Detection

Role of Machine Learning Algorithms in Analysis of Crop Dise...

引用

Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Bhanu Devi Danish Gupta Yash Rai Harsh Jindal Abhinav Sehgal Shreyansh Rai Department of Computer Science and Engineering Chandigarh University Mohali India Department of Apex Institute of Technology Chandigarh University Mohali India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

Crop disease detection is a vital field of agricultural research with the potential to have a big influence on sustainability and food security. The use of machine learning algorithms for agricultural disease identification has gained popularity recently because they provide a quicker and more precise way to recognise and diagnose crop illnesses. With an emphasis on several techniques including image processing, spectral imaging, and deep learning, this review article explores current advancements in crop disease diagnosis using machine learning. The study presents potential remedies to increase each approach’s precision and dependability while highlighting the difficulties and drawbacks of each. The review paper also examines dataset accessibility and usability, which are essential for developing and testing machine learning models. To address the issues of sustainable agriculture and food security, the review article underlines the need of ongoing research in crop disease detection.

关键词： Deep learning machine learning algorithms Reviews Image processing Crops Food security Agriculture

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Evaluating machine learning algorithms for Time Series Classification Using Complex Network Features

Evaluating Machine Learning Algorithms for Time Series Class...

引用

International Conference on Advanced Technologies for Communications, ATC

作者： Quan Tran Duc Hinh Nguyen Van Trung-Anh Do Posts and Telecommunications Institute of Technology Hanoi Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Time series classification is a crucial step in many scientific and engineering fields because it allows us to identify and predict trends or patterns of behavior in data over time. This study aims to explore the efficacy of five popular machine learning algorithms - Support Vector machine (SVM), K-Nearest Neighbors (KNN), Linear Regression (LR), Naive Bayes (NB), and Decision Tree (DT) - in the task of time series classification, with a new approach to feature extraction based on complex networks. The research method involves applying feature extraction techniques using complex networks to graph and compare the performance of these algorithms on prepared datasets. The results show significant differences in classification efficacy among the algorithms, with the highest accuracy in this time series classification study reaching 96.92%. The study concludes that selecting the appropriate algorithm and feature extraction method can significantly improve the quality of time series classification, paving the way for more effective future developments in time series processing models.

关键词： Support vector machines machine learning algorithms Accuracy Heuristic algorithms Time series analysis Linear regression Complex networks Nearest neighbor methods Feature extraction Classification algorithms

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Tomato Disease Prediction Model using machine learning algorithms and Image Processing Techniques

Tomato Disease Prediction Model using Machine Learning Algor...

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International Conference on Computational Intelligence and Networks (CINE)

作者： M O Pallavi Anushree Raj MCA Department Acharya Institute of Technology Bangalore Karnataka India MCA Department Mangalore Institute of Technology and Engineering Moodabidri Karnataka India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

Tomato cultivation is essential for global agricultural production, but the occurrence of diseases can lead to significant yield losses. To address this challenge, this paper proposes a tomato disease prediction model that leverages machine learning algorithms and image processing techniques. The model aims to accurately classify tomato leaves as healthy or diseased based on visual characteristics. The methodology involves image segmentation and disease classification. Image segmentation is performed using Kmeans clustering and Otsu thresholding to isolate the tomato leaf region of interest. Various image features, such as color, texture, and shape, are extracted from the segmented regions. For disease classification, a combination of convolutional neural network (CNN), support vector classifier (SVC), k-nearest neighbor (KNN), decision tree, and random forest classifier algorithms are employed. The CNN algorithm excels in capturing complex patterns, while the other algorithms provide alternative approaches. The dataset consists of annotated tomato leaf images, including healthy and diseased samples, divided into training and testing sets. Evaluation metrics such as accuracy, precision, recall, and F1 score are used to assess predictive capabilities. The experimental results demonstrate the model’s effectiveness in accurately predicting tomato leaf health status. CNN achieved the highest accuracy, followed by SVC, KNN, decision tree, and random forest classifier. The model shows promise as a reliable tool for tomato disease detection and management in agricultural practices.

关键词： Image segmentation machine learning algorithms Predictive models Nearest neighbor methods Prediction algorithms Feature extraction Classification algorithms Convolutional neural networks Random forests Diseases

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Prediction of Foreign Exchange Rates Using machine learning algorithms

Prediction of Foreign Exchange Rates Using Machine Learning ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Shivangi Mishra Alka Chaudhary Amity Institute of Information Technology Amity University Noida Uttar Pradesh

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

This research study focuses on the years 2006 to 2023 and briefly examines the changes in the Indian Rupee (INR) foreign exchange rate from 2000 to 2023. The research underlines the impact of global events and governmental activities and examines significant elements that influence the INR's volatility against major international currencies through an analysis of daily exchange rate data. The findings offer helpful information for governments, businesses, and investors navigating the difficult INR foreign exchange market.

关键词： Exchange rates machine learning algorithms Market research Solids Reliability Stakeholders Risk management

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