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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是591-600 订阅

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FertiForecast: Identification of Fertilizer Based on NPK Levels using machine learning algorithms

FertiForecast: Identification of Fertilizer Based on NPK Lev...

引用

Computing and Data Science (ICCDS), International Conference on

作者： M. Vimaladevi Thangamani R Dhivyasubashini K Kandavel G Aravind S Department of Artificial Intelligence Kongu Engineering College Tamil Nadu India Department of AI-DS Kongu Engineering College Erode Tamilnadu India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

For many people in India, especially those living in rural areas, agriculture is their main source of income. Approximately 50% of the labour force in the nation is employed by it, either directly or indirectly. Fertilizers playa crucial role in modern agriculture by enhancing soil fertility and promoting optimal plant growth. They provide essential nutrients to plants, including nitrogen, phosphorus, potassium, and micronutrients. To boost crop yields, commercial fertilizers are sprayed on agricultural crops. The majority of farming took place on tiny family farms with little usage of chemicals before to the 1950s. Since then, the usage of chemical fertilizers in contemporary agricultural methods has paralleled with the move toward larger corporate farms. Farmers may lack knowledge about the various types of fertilizers available, their formulations, and the appropriate selection for specific crops and soil conditions. This can lead to the use of inappropriate or imbalanced fertilizers. machine learning algorithms can play a significant role in fertilizer prediction in agriculture, offering several advantages that contribute to more efficient and sustainable fertilizer management. ML algorithms analyze diverse datasets, including soil characteristics, weather patterns, crop types, and historical yield data. This data-driven approach enhances decision-making by providing insights into the factors influencing fertilizer needs.

关键词： machine learning algorithms Plants (biology) Rural areas Soil Prediction algorithms Phosphorus Agriculture

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Towards Skin Lesion Classification Using machine learning algorithms and Stokes Imaging Polarimetry

Towards Skin Lesion Classification Using Machine Learning Al...

引用

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Oscar I. Rodríguez-Cortés Geminiano Martínez-Ponce Centro de Investigaciones en Óptica A. C. Loma del bosque 115 Lomas del campestre León Guanajuato México

Melanoma, the deadliest skin cancer, demands enhanced detection methods. A handheld device integrating polarimetric imaging and machine learning for lesion classification via the ABCDE method is proposed. Preliminary ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Stokes parameters machine learning algorithms Imaging Lighting Melanoma machine learning Medical services

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Advanced Pattern Detection and Trend Forecasting in European Carbon Markets Using machine learning algorithms

Advanced Pattern Detection and Trend Forecasting in European...

引用

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Seyed Ali Hosseini Alessandro Niccolai Marco Lorenzo Fabio Casamatta Francesco Grimaccia Department of Energy Politecnico di Milano Milan Italy A2A S.p.A. BU Generazione e Trading Milan Italy

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

This paper investigates the detection and prediction of patterns in European Carbon Emission price trends using machine learning on high-frequency tick-by-tick data, resampled at 5-minute intervals. After data preprocessing to manage outliers and ensure data integrity, trends in close prices were identified through pivot points and percentage change thresholds. Five machine learning models, including, Linear Regression, Decision Tree Regressor, K-Nearest Neighbors (KNN) Regressor, Random Forest Regressor, and Gradient Boosting Regressor were trained and evaluated for pattern prediction and classification tasks. The Gradient Boosting Regressor emerged as the top performer, achieving the lowest test Mean Squared Error (MSE) and the highest R2 score, outperforming KNN and Decision Tree models by approximately 20% in MSE. It also achieved the highest F1 Score in pattern classification, underscoring its superior accuracy and reliability in analyzing complex financial data. This study offers valuable insights for future financial data analysis, presenting robust methods for predicting and classifying price trends.

关键词： machine learning algorithms Accuracy Europe Carbon dioxide Predictive models Nearest neighbor methods Market research Boosting Robustness Regression tree analysis

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Prediction of Heart Stroke using machine learning algorithms

Prediction of Heart Stroke using Machine Learning Algorithms

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： R. Kiruba S.S. Rithish P. Jagatheeswaran Ra. Gokul S. Santhiya EIE Sri Ramakrishna Engineering College Coimbatore India Department of EIE Sri Ramakrishna Engineering College Coimbatore India Department of ECE KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Heart strokes remain a significant global health burden, emphasizing the need for early detection and preventive measures. This research investigates the application of machine learning techniques to predict the occurrence of heart strokes. A prediction model was developed using age and cholesterol levels as key criteria, leveraging a dataset of medical records with lipid profiles and demographic data. The model, trained using MATLAB’s classification learner tool, achieved an impressive accuracy rate of $\mathbf{9 5 - 9 7 \%}$. Confusion matrices and scatter plots were employed to evaluate the model’s effectiveness in differentiating between individuals at high risk and those at low risk. The results demonstrate the potential of machine learning to significantly improve predictive accuracy in clinical settings, enabling prompt intervention and reducing the overall incidence of heart strokes.

关键词： Heart Accuracy machine learning algorithms machine learning Medical services Stroke (medical condition) Predictive models Mathematical models MATLAB Medical diagnostic imaging

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BotNet-Inspired HTTP-Based DDoS Attack Prevention Using Supervised machine learning algorithms in Internet of Things Devices

BotNet-Inspired HTTP-Based DDoS Attack Prevention Using Supe...

引用

Annual IEEE Systems Conference

作者： Jide Ebenezer Taiwo Akinsola Sunday Adeola Ajagbe Azeezat Oluwayemisi Lawal Emmanuel Ajayi Olajubu Ganiyu Adesola Aderounmu Matthew Olusegun Adigun Department of Computer Sciences First Technical University Ibadan Nigeria Department of Computer Science University of Zululand Kwadlangezwa South Africa Department of Computer Science and Engineering Obafemi Awolowo University Ile–Ife Nigeria

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

DDoS attacks have emerged as the most serious Internet risks, causing significant damage to businesses and governments. This study utilized the BoT-IoT dataset obtained from the University of New South Wales (UNSW) website on machine learning (ML) algorithms to prevent HTTP-based DDoS attacks in the Internet of Things environment. Character encoding was performed to convert the strings such as DDoS attack to 1 and Non-malicious to 0. Feature scaling and normalization were carried out to normalize numerical data with large values using a Python module called standard scaler to avoid model overfitting or underfitting. According to the performance evaluation metrics results, decision tree is the best model with 99.96% accuracy, 0.9979 R 2 , 0.0003 MAE, 0.0003 MSE, 0.0195 RMSE, 1.0 precision, 0.9994 recall, 0.9997 F1_score after dimensionality reduction. After comparing various ML models, this study concludes that the decision tree is the golden model for HTTP-based DDoS attack prevention in IoT devices. The study recommends the utilization of other performance evaluation metrics such as Matthew Correlation Coefficient (MCC), Kappa Statistic and Huber loss for further studies to avoid bias and subjectivity in the model selection where there are surrogate models. Also, deep learning paradigms can be implemented for the prevention of HTTP-based DDoS attacks in cloud environments.

关键词： Performance evaluation Deep learning Correlation coefficient machine learning algorithms Accuracy Denial-of-service attack Numerical models

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Fake News Detection Using machine learning algorithms

Fake News Detection Using Machine Learning Algorithms

引用

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： E. Bharat Babu Kaki Archana J Rakesh Goud Kadapa Dileep Hussain Santhosh Kumar Veeramalla Electronics and Communication Engineering B V Raju Institute of Technology Narsapur India Electronics and Communication Engineering BVRIT Hyderabad College of Engineering for Women Hyderabad India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Fake news detection is a news which is not real. Now a days, with the internet everywhere, everyone gets their news from several online sources. As more people used online media sites like Facebook, Twitter, and others. Wide-range of false news dissemination include the formation of prejudiced viewpoints and the manipulation of election results to favor candidates. Additionally, spammers utilize attention-grabbing news headlines as clickbait to monetize their ads. In this research, we use principles as neural networks as well as linguistic processing concepts for the binary classification of different types of online news stories that categorize news articles of various kinds that are available online using binary classification. In addition, apart from enabling the consumer to ascertain the legitimacy of the headlines, we also want to provide them with the option to flag news as fake or real. In this case, as an attribute harvesting strategy, we as a species deployed a technique called term frequency of an inverse document as frequency spanning an assortment word pool plus an N gram and Radom Forest (RF) as a classifier. The outcomes obtained from analyzing the real-world data demonstrate that our algorithm is 94% accurate than the baselines in identifying bogus news a few minutes after it spreads.

关键词： Logistic regression machine learning algorithms Social networking (online) Voting Neural networks Linguistics Classification algorithms Information and communication technology Fake news Random forests

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Automating Forensic Analysis: Integrating machine learning algorithms for Forensic Evidence and Serial Killer Profiling

Automating Forensic Analysis: Integrating Machine Learning A...

引用

Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Hana Ahmed Mohamed Hassan Mariam Mohamed Abouelwafa Amira Alaa Saber Ahmed Ehab Ibrahim Nada Shorim Diaa Salama AbdElminaam Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

Serial murder investigations pose significant challenges due to the complexity of criminal patterns and behaviors. This study presents an integrated forensic analysis system using advanced artificial intelligence (AI) techniques to enhance the efficiency and accuracy of criminal investigations, particularly in serial murder cases. By applying various machine learning algorithms, including logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), convolutional neural networks (CNN), and generative adversarial networks (GAN), the system analyzes key forensic evidence such as serial killer patterns, fingerprint recognition, face sketching, bloodstain analysis, and autopsy reports. Each module in the system contributes to narrowing down suspect profiles and linking potential cases using AI-driven pattern recognition and data analysis. The system demonstrated high performance across multiple forensic tasks, with logistic regression and SVM achieving 85% accuracy in serial killer pattern recognition, GAN achieving 99% accuracy in face recognition, and LSTM achieving 99.3% accuracy in bloodstain analysis. The integration of these AI techniques ensures robust, unbiased, and efficient forensic analysis, significantly reducing the time and labor required in traditional investigative methods. The findings of this study underscore the transformative potential of AI in forensic science, providing law enforcement with powerful tools to profile serial killers, analyze crime scenes, and identify critical evidence with precision. This unified AI-based approach holds promise for improving investigative outcomes and enhancing public safety by assisting in the timely resolution of complex murder cases.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Forensics Face recognition Fingerprint recognition Nearest neighbor methods Generative adversarial networks Artificial intelligence

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English Translation Assistance System Integrating machine learning algorithms

English Translation Assistance System Integrating Machine Le...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Alex Gao Beijing Polytechnic Beijing China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

As computer-assisted translation systems become integral to daily life and learning, various issues have emerged, such as whether existing systems can deliver accurate translations across different sentence structures and lengths. This study focuses on optimizing neural machine translation technology to tackle challenges like low-resource language pairs and limited parallel corpora. We introduced an unsupervised neural machine translation model and assessed its performance based on BLEU scores, translation speed, loss value, translation accuracy, and average vocabulary similarity. The results revealed that the model excelled in semantic understanding and alignment, with an average similarity between 0.76 and 0.81. The model showed substantial improvements in translation performance, offering a novel approach to advancing neural machine translation technology.

关键词： Vocabulary Technological innovation Accuracy machine learning algorithms Limiting Semantics Neural machine translation Optimization methods Manuals Linguistics

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A Comparative Analysis of machine learning algorithms: K-Nearest Neighbor (KNN) and Random Forest for Predicting Sports Poses

A Comparative Analysis of Machine Learning Algorithms: K-Nea...

引用

International Conference on Wireless and Telematics (ICWT)

作者： Nur Syifa Maulida Lia Kamelia Rin Rin Nurmalasari Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Bandung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

The aging process often leads to a decline in physical function, emphasizing the importance of maintaining physical activity, mainly through exercise. However, a significant portion of the elderly tends to become less physically active. In response to this challenge, solutions such as using artificial intelligence, specifically machine learning, have been proposed for monitoring exercise activities within the household environment. This study compares the effectiveness of machine learning algorithms, specifically K-Nearest Neighbor (KNN) and Random Forest, in predicting exercise poses. The dataset used consists of a total of 15,372 images merged into a single video frame. Subsequently, training was conducted to identify body landmarks. The dataset was divided into two variables, labeled C and L. Variable C contains 8,238 images representing correct exercise poses. In contrast, variable L contains 7,134 images illustrating incorrect exercise poses. The results indicate that the Random Forest model exhibited the highest accuracy during the training phase at 99%. Conversely, the KNN model demonstrated the highest accuracy at 97% during evaluation. This difference can be attributed to various factors, including model complexity.

关键词： Training Wireless communication Accuracy machine learning algorithms Nearest neighbor methods Predictive models Prediction algorithms

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Predicting Beam Switching Directions in Ultra Wideband Spiral Antennas Using machine learning algorithms

Predicting Beam Switching Directions in Ultra Wideband Spira...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Oratai Nikornpon Pichaya Chaipanya Department of Electrical Engineering Srinakharinwirot University Nakhon Nayok Thailand

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

This paper presents the application of machine learning techniques to predict beam switching directions based on slot positions and the turn number of the ring in wideband spiral antennas operating at a frequency of 2.6 GHz for 5 G communication. A dataset of 200 samples was generated through simulations on CST Studio, varying the slot angle positions and the turn number of the ring while keeping other parameters constant. This dataset was used to evaluate the performance of three machine learning algorithms: decision trees, random forests, and K-nearest neighbors (KNN). The results indicate that decision tree and random forest models were the most effective in accurately predicting beam-switching positions.

关键词： Spirals machine learning algorithms Accuracy Switches Nearest neighbor methods Directive antennas Predictive models Ultra wideband antennas Decision trees Random forests

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