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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是851-860 订阅

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Detection of Food Quality Using machine learning algorithms

Detection of Food Quality Using Machine Learning Algorithms

引用

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

作者： Samir Rana Ritik Singh Sahil Gusain Saurabh Vij Bharti Saini Aditya Joshi Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Efficient food spoilage detection systems are important to limit the economic damage as the world's food consumption rises not only that it minimize losses and guarantee the safety of food. In our research paper we have given the analysis of the use of various machine learning algorithms, which includes K-Means with Binary Particle Swarm Optimization (KBPH), You Only Look Once (YOLO), Region-based Convolutional Neural Networks (RCNN), Faster RCNN, Fisher Face, and Convolutional Neural Networks (CNN), for the detection of food spoilage. This work makes a remarkable contribution to the field of food decaying detection by helping practitioners and academics choose the best machine learning algorithms for certain food industryapplications. Key Words : CNN , RCNN, KBPH , YOLO.

关键词： YOLO Location awareness Economics machine learning algorithms Accuracy Face recognition Real-time systems Safety Convolutional neural networks Particle swarm optimization

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Detection of Bloodstream Infections: A Comparative Analysis of machine learning algorithms

Detection of Bloodstream Infections: A Comparative Analysis ...

引用

Data, Computation and Communication (ICDCC), International Conference on

作者： Gayathiri E PCD Kalaivaani Gokulsundar S Dharshana V Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Bloodstream infections (BSIs) represent a critical threat to the health of hemodialysis patients, necessitating timely and precise detection for effective intervention and improved clinical outcomes. This paper explores the utilization of machine learning models for predicting BSIs in hemodialysis patients. Multiple algorithms, including Naive Bayes, Support Vector machines (SVM), K-Nearest Neighbors (KNN), XGBoost, LightGBM, and Random Forest, were employed to analyze patient datasets. The models were assessed based on their classification accuracy. To further enhance performance, ensemble techniques―such as bagging, boosting, stacking, and voting―were applied, along with hyperparameter optimization using methods like grid search, randomized search, and Bayesian optimization. The study found that ensemble models and optimized hyperparameters yielded significant improvements in prediction accuracy, with XGBoost and Random Forest consistently achieving superior performance, reaching an accuracy of up to 79.8%. These findings underscore the potential of machine learning algorithms to aid in the early detection of BSIs, offering a valuable tool for improving patient care and reducing the associated health risks.

关键词： Support vector machines machine learning algorithms Accuracy Stacking Nearest neighbor methods Predictive models Prediction algorithms Bayes methods Random forests Optimization

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Application of machine learning algorithms for Efficient Disease Prediction

Application of Machine Learning Algorithms for Efficient Dis...

引用

International Conference on Communication and Electronics Systems (ICCES)

作者： Krishna Kumar N. P. Venkateswarlu Reddy D. Ganesh Kotari Nirmala Modukuru Pradeep J. Suresh Babu Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad Telangana India CSE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Computer Science and Engineering(CS) Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad Telangana India CA Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research presents the development of a System for Predicting Diseases Using machine learning. The system leverages machine learning algorithms to predict the likelihood of individuals developing COVID-19., heart disease, or chronic kidney disease. By inputting relevant symptoms, medical history, and other relevant factors, users can receive preliminary disease predictions without requiring immediate in-person consultation. Powered by Python and Django, the system provides a user-friendly interface for accessing these predictive models. This system aims to improve access to healthcare by facilitating early identification of potential health risks, empowering individuals to make informed decisions about their health, and potentially reducing the burden on healthcare systems.

关键词： Training Radio frequency machine learning algorithms Accuracy Predictive models Prediction algorithms Bayes methods Safety Personnel Diseases

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Redefining Trust: Assessing Reliability of machine learning algorithms in Intrusion Detection Systems

Redefining Trust: Assessing Reliability of Machine Learning ...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hossein Sayadi Zhangying He Tahereh Miari Mehrdad Aliasgari Department of Computer Engineering and Computer Science California State University Long Beach CA USA Center for Information Systems and Technology Claremont Graduate University CA USA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The performance limitations of conventional software-based Intrusion Detection Systems (IDSs) have paved the way for the emergence of hardware-oriented approaches. These approaches harness the power of machine learning (ML) algorithms applied to processors’ hardware-related data, thereby enhancing the overall system’s security and efficiency. However, ensuring the dependability of ML models’ decisions is crucial, yet this aspect has been largely overlooked in previous studies. In this paper, we delve into the reliability of machine learning algorithms within hardware-oriented intrusion detection systems, focusing specifically on malware detection. Our investigation aims to bridge the existing gap by shedding light on the tradeoffs between performance vs. reliability and robustness levels exhibited by ML models in intrusion detection systems. We conduct a thorough evaluation of ML algorithms in hardware-oriented IDSs, considering factors such as training data size, number of hardware events used, and internal data separability (malware stealthiness). Additionally, we incorporate an effective model observer module to assess prediction probabilities in real-time; thereby, employing a threshold to determine the ML model’s confidence for enhanced reliable intrusion detection.

关键词： machine learning algorithms Intrusion detection Training data Predictive models Malware Robustness Real-time systems

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An Evaluation of machine learning algorithms for Predicting Closing Stock Prices in the Indian Stock Market

An Evaluation of Machine Learning Algorithms for Predicting ...

引用

IEEE Region 10 International Conference TENCON

作者： Vivek Kumar Anand Deeksha Chandawat NIIT University Neemrana Rajasthan India United World Institute of Management Karnavati University Gandhinagar India

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This study examines how effective machine learning algorithms are at forecasting the closing stock prices of five major FMCG companies listed on the National Stock Exchange of India (NSE). The focus is on methods such as Random Forest, Decision Trees, Linear Regression, and Support Vector Regression, aiming to analyze their performance against metrics like accuracy, precision, recall, and F1-score. The goal is to uncover insights regarding the reliability of different machine learning techniques for stock price prediction, ultimately assisting investors and financial analysts in making well-informed investment choices. The study will leverage historical stock price data, incorporating relevant technical indicators and fundamental features to train and test the predictive models. To ensure data quality, a meticulous preprocessing stage will address missing values, normalize features, and minimize potential biases. The models' performance will be evaluated using robust metrics such as Mean Absolute Error (MAE) and R-squared to assess accuracy and overall effectiveness.

关键词： Measurement Support vector machines machine learning algorithms Accuracy Predictive models Vectors Reliability Stock markets Regression tree analysis Random forests

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Application of machine learning algorithms to Predict New Mobile Customers

Application of Machine Learning Algorithms to Predict New Mo...

引用

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

作者： M. Jaya Ranjan D. Ganesh K. Saradhi Vemula Jasmine Sowmya Yelisela Rajesh M. Sunil Kumar Velagapudi Ramakrishna Siddhartha Engineering College Kanuru Vijayawada Andhra Pradesh India Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India School of Commerce and Management Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Dept. of CSE KLEF Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

Because of economic liberalization and globalization, the nature of business competition has undergone significant change. New technology’s rise in corporate operations has more competition and created fresh opportunities for service suppliers. Businesses are shifting their attention away from finding new customers and towards keeping their existing clients to deal with the escalating difficulties. This uses less energy since it is more cost-effective. In this part, we take a look at prospective mobile internet users by using machine learning as well as deep learning methods to data on consumer behavior and usage. This should help us identify new mobile internet users. This research looked at mobile internet users in the telecommunications sector using user behavior and comparable data from a provider of telephone services. Decision Trees and Random Forest ensembles for support vector machines are employed. To build a binary classification system for mobile internet use, these models serve as foundational statistical machine learning tools. In this research, machine learning algorithms are used to analyze user behaviors in a distributed computing environment. Users in these groups all exhibit similar behaviors. The user’s behavior-related events are recorded and added to a database.

关键词： Support vector machines Analytical models machine learning algorithms Consumer behavior Computational modeling Biological system modeling Prediction algorithms Classification algorithms Random forests Business

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The Role of machine learning algorithms in Smart Grid Cybersecurity

The Role of Machine Learning Algorithms in Smart Grid Cybers...

引用

Artificial Intelligence and machine learning for Energy Transformation (AIE), International Workshop on

作者： Sayawu Yakubu Diaba VTT Technical Research Center of Finland Espoo Finland

ISBN: (数字)9798350364965

ISBN: (纸本)9798350364972

In the rapidly evolving landscape of smart grid cybersecurity, the integration of artificial intelligence and machine learning algorithms has emerged as a pivotal element in fortifying infrastructures against sophisticated cyberattacks. This paper investigates the transformative role of artificial intelligence and machine learning in enhancing the security of smart grids, which are increasingly becoming targets for high risk cyberattacks. By contextualizing the significance of cyber-security within the energy sector, and by elucidating the fundamental principles of artificial intelligence and machine learning algorithms. An in-depth exploration of how these algorithms is revolutionizing cyberattack detection, predictive analytics, and automated response strategies in the smart grid domain are evaluated. Utilizing state-of-the-art data, this paper presents a comprehensive analysis of some leading machine learning algorithm responses, highlighting key trends and emerging patterns.

关键词： machine learning algorithms Accuracy Predictive models Prediction algorithms Smart grids Computer crime Protection

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Comparative Analysis of machine learning algorithms for LOS/NLOS Identification

Comparative Analysis of Machine Learning Algorithms for LOS/...

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Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Prachi Agarwal Rohit Kumar Dharmendra Kr. Jhariya Mahesh K. Singh Dept. of Electronics & Communication Engineering NIT Delhi

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Identification of Line of Sight (LOS) and Non Line of Sight (NLOS) is a challenging task since majority of physical effects that take place at distinct temporal/spatial conditions can affect the LOS propagation. Ultra Wide Band communication technology is the key technology in indoor positioning to identify LOS and NLOS communication channels in different environmental conditions. Different machine learning algorithms are used to identify on the basis of various training feature which basically exploits several static and time-varying features of the channel impulse response (CIR).In ML, the choice of training features plays a crucial role. The paper addresses machine learning techniques i.e. K-Nearest neighbour, Logistic Regression, Support Vector machine and Random Forest in terms of their performance on different parameters.

关键词： Deep learning Wireless communication Training Support vector machines Adaptation models Accuracy Recurrent neural networks machine learning algorithms Reliability Random forests

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A Comparative Study on the Performance of machine learning algorithms for Malware Detection and Classification

A Comparative Study on the Performance of Machine learning A...

引用

International Conference for Convergence of Technology (I2CT)

作者： P. Lavanya Peeta Basa Pati U. Kumaran Department of CSE Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

From cloud computing to the Internet of Things, there are several applications and frameworks that are susceptible to cyber-attacks. To safeguard critical systems and companies from cyber security assaults, malicious activities are automatically detected using machine learning (ML) techniques. Choosing a classifier that determines a file's threat or maliciousness using static signature-based analysis, we performed bi-class and multi-class classification using 15 ML algorithms. As a data preprocessing technique, we used Truncated singular value decomposition (SVD) which significantly improved the classifier results on multi class classification. Synthetic Minority Oversampling Technique (SMOTE) technique is used to balance the imbalanced datasets, applied feature selection and 5-fold cross validation to improve the classifiers performance. With the extensive analysis we made on a spectrum of datasets which are used for malware analysis, we found ensemble-based classifiers are consistent and the results are remarkable. Our findings on the optimal classifier can be utilized to create intrusion detection systems or signature-based antiviral software in real time.

关键词： Deep learning machine learning algorithms Data preprocessing Intrusion detection Feature extraction Malware Real-time systems

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Comparative analysis of machine learning algorithms for Lung Cancer Detection

Comparative analysis of Machine Learning Algorithms for Lung...

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

作者： Sonia Kukreja Vishvendra Singh Sirohi Lavya Aggrawal Department of Computer Science and Engineering Galgotias University GreaterNoida UP India Department of Computer Science and Engineering Galgotias University GreaterNoida UP

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Lung cancer stands as a significant global health challenge, demanding advancements in early detection methods to enhance patient outcomes. The research attempts to create accurate diagnostic tools by examining a variety of clinical characteristics, such as patient demographics, smoking history, genetic markers, and environmental exposures. After a comprehensive analysis of the literature, the study emphasizes the need for better detection techniques. The study assesses the effectiveness of various machine learning algorithms to decide the optimal course of action. To improve the model's performance, results from multiple methods are compared.

关键词： machine learning algorithms Accuracy Computational modeling Lung cancer Genetics History Regression tree analysis

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