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检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是551-560 订阅

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Benchmarking machine learning algorithms for Predicting Building Energy Performance 59

Benchmarking Machine Learning Algorithms for Predicting Buil...

引用

59th International Universities Power Engineering Conference, UPEC 2024

作者： Bampoulas, Adamantios Meagher, Naill Finn, Donal Mangina, Eleni School of Computer Science University College Dublin Dublin Ireland School of Mech. and Mat. Engineering University College Dublin Dublin Ireland

ISBN: (纸本)9798350379730

Current data-driven load-forecasting models for residential buildings often overlook the impact of evolving occupancy patterns, relying on default hyperparameters. This oversight may result in reduced predictive accuracy with behavioural changes. This paper introduces a benchmarking approach evaluating common data-driven models-random forests, multilayer perceptron neural networks, extreme gradient boosting, and support vector machines-under concept drift. The day-ahead heating load prediction models are trained based on a series of input variables, including, inter alia, weather conditions, calendar information, and historical data. Optimal training periods and hyperparameter tuning are determined using a development set, while predictive accuracy is assessed through cross-validation on an evaluation set. Simulation results demonstrate that machine learning models trained on datasets with varying occupancy profiles perform comparably to those trained and evaluated with consistent profiles. Although optimal hyperparameters may vary, the models maintain consistent performance across datasets with diverse occupancy patterns, indicating resilience to changes in occupant behaviour. © 2024 IEEE.

关键词： benchmarking methodology concept drift machine learning algorithms occupancy behaviour short-term load forecasting

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Disease Prediction Models Using machine learning algorithms 3

Disease Prediction Models Using Machine Learning Algorithms

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Srinivas, Inumarthi V. Alzubaidi, Laith H. Singh, Rahul Sundaram, N. Kalyana Nirmala, D. Kj Somaiya Institute of Management Data Science and Technology Mumbai India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Electrical & Electronics Engineering Bhopal India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical & Electronics Engineering India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350342338

Disease Prediction Models hold paramount importance in healthcare for enabling early intervention and improving patient outcomes. machine learning algorithms, with their predictive capabilities, offer promising avenues for enhancing disease prediction accuracy. In this research, we explore the application of various machine learning techniques for disease prediction. We begin by introducing the context of disease prediction and the significance of machine learning algorithms. Our study involves preprocessing data, selecting relevant features, and implementing diverse algorithms. Through rigorous experimentation and evaluation, we present comparative insights into algorithm performance. The outcomes underscore the potential of machine learning in revolutionizing disease diagnosis and prognosis. Ethical considerations and potential biases are discussed, highlighting the responsible integration of predictive models in healthcare practices. This research contributes to the growing body of knowledge, paving the way for future advancements in disease prediction and personalized medical interventions. © 2023 IEEE.

关键词： Biomedical informatics Disease prediction Early intervention Ethical considerations Feature selection machine learning algorithms Model development Predictive modeling

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Humans Psychological Traits Prediction from their Spending Patterns using machine learning algorithms 2

Humans Psychological Traits Prediction from their Spending P...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Arpitha, C.N. Sunitha, M.R. Department of Information Science and Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India Visvesvaraya Technological University Belagavi590018 India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Analyzing human behavioral data produced by new technology has gained popularity in recent years. Spending behavior is one kind of digital footprint that is ubiquitous worldwide but has gotten very little attention up to this point. In this research, the analysis of the extent to which psychological traits at the individual level may be deduced from bank transaction data, utilizing the transaction records of over 100 bank customers is performerd. Using (i) overall spending behavior (i.e., total number and total amount of transaction), (ii) category-related spending behavior (i.e., variety, persistence, and turnover), more extensive feature space is considered and these features are used to investigate how they relate to certain psychological traits, examine how well each feature family performs, and then attempt to determine how much psychological information can be deduced from expenditure records. Most popular supervised algorithms like, Random Forest, Support vector machine (SVM), Decision Tree and Logistic regression algorithms are used for classifying the humans different personality traits and their performance analysis are represented. And the experimantal results claims that better accuracy is attained by Random Forest and Decision tree approaches for categorization. © 2024 IEEE.

关键词： Support vector machines Logistic regression Privacy machine learning algorithms Psychology Prediction algorithms Classification algorithms Performance analysis Decision trees Random forests

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A Comparative Analysis of Supervised machine learning algorithms for Electricity Demand Forecasting 2

A Comparative Analysis of Supervised Machine Learning Algori...

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2nd International Conference on Power, Control and Computing Technologies, ICPC2T 2022

作者： Rawal, Keerti Ahmad, Aijaz NIT Srinagar Department of Electrical Engineering Srinagar India

ISBN: (纸本)9781665458580

Accurate electricity demand forecasting is crucial for efficient, economical and stable operation of power system grid. With the increasing integration of intermittent sources, need of reliable electricity demand forecasting has become more evident. In this paper, supervised machine learning (ML) algorithms are explored and analysed for medium-term prediction of electricity demand. The demand forecasting has been implemented for the New South Wales (NSW) area of Australian electricity market for data of one year ranging from January to December 2020. The machine learning algorithms compared in this study are - linear regression (LR), multivariate polynomial regression (MPR), support vector regression (SVR), elastic net regression (ENR), gradient boosting regression (GBR), decision tree regression (DTR), random forest regression (RFR), and K- nearest neighbour regression (KNNR). The evaluation criteria used for the performance judgement are coefficient of determination (R2), maximum error (ME), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and median absolute error (MedAE). The machine learning algorithms are compared on the basis of evaluation criteria, and nature of prediction algorithm. The non-parametric machine learning algorithms have performed better than parametric methods. © 2022 IEEE.

关键词： Support vector machines machine learning algorithms Power control Demand forecasting Prediction algorithms Power system reliability Reliability

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machine learning algorithms with Multi-Criteria-Decision-Making for cell outage detection in 5G / B5G

Machine learning algorithms with Multi-Criteria-Decision-Mak...

引用

International Symposium on Telecommunications, IST

作者： Reza Moammer Yami Ali Akbar Khazaei Saeed Rahati Quchani Department of Electrical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Department of Biomedical Engineering Faculty of Engineering Islamic Azad University Central Tehran Branch Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

Self-organizing communication networks in 5G&B5G telecommunications are automatically optimized, configured and improved without human intervention, reducing costs (OPEX and CAPEX) in new generation networks. Self-healing is critical to 5G and B5G networks because it anticipates and resolves anomalies, optimizes performance, and adapts to changes in cell density and architecture. This process is done by managing out-of-service cells called COM. And COM itself is divided into two parts: COD to detect out-of-service cells and COC to compensate for out-of-service cells. Timely prediction of the work status of the cells increases the network performance. To achieve this goal, three law-based, algorithm-based and machine learning approaches are used in self-healing networks. This research uses combined machine learning algorithm based on multi-criteria decision making techniques to identify and estimate defective cells. Data were classified using a combination of three Bayesian learning algorithms, logistic regression method and AdaBoost classification method. The challenge of unbalanced training data was successfully solved with a hybrid approach and multi-criteria decision-based algorithm. machine learning and TOPSIS improve efficiency and accuracy in decision-making and detection of defective cells.

关键词： Logistic regression machine learning algorithms Accuracy Microprocessors Computer architecture Prediction algorithms Classification algorithms Telecommunications Bayes methods Labeling

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Comparative analysis of machine learning algorithms for Intrusion Detection

Comparative analysis of Machine Learning algorithms for Intr...

引用

2020 International Conference on Futuristic Trends in Mechanical Engineering, ICOFTIME 2020

作者： Pai, Vasudeva Devidas Adesh, N.D. NMAM Institute of Technology Nitte Udupi Karnataka India Manipal Institute of Technology Manipal Academy of Higher Education Manipal Udupi Karnataka India

In this modern era, the network related applications, programs and services are growing enormously but the network security issues also grow along with them. Keeping the network secure is a challenging and a crucial task. To maintain the secure network there must be some system which can detect and identify any malicious activity happening in network. This system is called as Intrusion Detection System. There are many traditional network security tools and techniques of preventing intrusion like firewalls, anti-virus, encryption-decryption, access control etc. But all are not effective in protecting network from increasing attacks. The network traffic can be categories into normal and intrusive traffic using machine learning (ML) algorithms. Here, the preliminary comparative study regarding which type of machine learning algorithm performs better in identifying the attacks namely Denial of Service, Probe, User to Root and Remote to Local. The NSL-KDD dataset is used to study features and behavior of malicious attacker using machine learning techniques. This study can be taken as reference for mechanical engineers for developing a safe automation in industrial atmosphere and automation in automobile. © Published under licence by IOP Publishing Ltd.

关键词： feature selection Intrusion Detection System machine learning algorithms NSL-KDD dataset

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Performance Analysis of Class Imbalance Handling Techniques for Early Sepsis Prediction using machine learning algorithms 8

Performance Analysis of Class Imbalance Handling Techniques ...

引用

8th International Conference on Smart Structures and Systems, ICSSS 2022

作者： Shenoy, Aparna Babu Rao, K. Cmr University School of Research and Innovation Bangalore India

ISBN: (纸本)9781665497619

Sepsis is a life-threatening condition that may cause mortality in ICU patients. Researchers have formerly developed machine learning (ML) models to predict Sepsis and other medical conditions. Performance of ML models dependent on the quality of the data set used for training. Class imbalance is one of the problems observed in medical data sets. In this paper, we have presented a comparative study of three class imbalance handling techniques to identify the best among the three. We made a comparison on performance metrics of predictive models developed using ML classification algorithms. We proposed a four-phase approach for predictive model development. Consequently, we used healthcare parameters available at any peripheral basic health facility and do not require laboratory investigations. Further, we proposed a novel machine learning predictive model, 'Sepsis Prediction Model for Peripheral Hospitals (SPMPH), ' for sepsis prediction in ICU patients. SPMPH provides better accuracy (.95), precision (.98), recall (.91) and AUROC (.978) as compared to other available models. © 2022 IEEE.

关键词： Measurement Training Radio frequency machine learning algorithms Hospitals machine learning Predictive models

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SPATIAL DOWNSCALING OF GPM IMERG V06 GRIDDED PRECIPITATION USING machine learning algorithms

SPATIAL DOWNSCALING OF GPM IMERG V06 GRIDDED PRECIPITATION U...

引用

ISPRS WG IV/7 Geoinformation Week on Broadening Geospatial Science and Technology

作者： Sathianarayanan, Manikandan Hsu, Pai-Hui Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

According to recent studies, Remote sensing data plays a significant role in filling gaps in the poor gauge station, particularly at high elevations and with complex underlying surface features. In order to provide high-resolution precipitation estimates over the poor gauge and with complex terrain areas, downscaling low-resolution satellite precipitation estimates using various environmental variables. In this paper, we tried to downscale the GPM IMERG V06 with a resolution of (0.1 degrees x0.1 degrees) nearly 10km to (1km x 1 km) using four machine learning algorithms namely, Decision Trees, Multiple Linear Regression, Support Vector Regressor and random forest. Vegetation indices Normalized difference in vegetation index (NDVI), Topography, Land Surface Temperature (LST), and latitude and longitude. This framework can downscale the 0.1 degrees resolution of the GPM IMERG precipitation product to 1 km, by determining the importance of features, and automatically optimizing the model parameters. Additionally, ground recorded data from rain gauge stations have validated downscaled precipitation products. Spatial downscaling can generally increase the accuracy of GPM IMERG gridded precipitation data and results reveal that spatial downscaling is an acceptable way of investigating the precipitation over Taiwan.

关键词： Spatial downscaling machine learning algorithms Satellite Precipitation estimation

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Computer Science Data Structure System with machine learning algorithms 2

Computer Science Data Structure System with Machine Learning...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Yu, Xinting Michigan State University Computer Science East Lansing United States

ISBN: (纸本)9798350317558

In current computer science, data structure analysis is undoubtedly one of the most important tasks. With the development of the times and technology, data structure analysis is facing many challenges. The main issue in this process is feature selection. Therefore, this article chose machine learning algorithms to optimize it. In the end, this article also verified the feasibility of the method of this paper through a comparative experiment between an intelligent data structure analysis system based on machine learning algorithms and a conventional system. The final result was that the average resource utilization rate of the intelligent system was 93.13%, while the average resource utilization rate of the conventional system was 86.45%, with a significant difference. Moreover, the resource utilization rate of the conventional system continued to decline over time, leading to lower work efficiency. Therefore, this study found that machine learning algorithms can optimize the efficiency of data structure analysis systems, thus completing the design and construction of a mature data structure analysis system. © 2024 IEEE.

关键词： Computer Science Data Structure Analysis Feature Selection machine learning algorithms

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Exploration on Risk Assessment and Prediction of Digital Economy under machine learning algorithms 2

Exploration on Risk Assessment and Prediction of Digital Eco...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Mei, Lulu Cao, Yimei Xiao, Chaoying Shaanxi Fashion Engineering University School of Economics and Management Xi'an China

ISBN: (纸本)9798350317558

The digital economy is becoming the main force driving the development of the world economy, and its sustainable development cannot be achieved without sound digital governance. Establishing an objective and scientific evaluation index system is of great significance for accurately understanding the development level of the digital economy and promoting global digital governance. On the basis of analyzing the connotation of the digital economy, it was summarized as four major characteristics: "economies of scale", "technology driven", "ecological development", and "dynamic management". Guided by the characteristics of the digital economy and theories such as innovation systems, ecosystems, and new institutional economics, a systematic framework for risk assessment of the digital economy was established based on systematic principles. Starting from the phenomenon, causes, and era value of digital economy risks, this article analyzed the necessity and governance starting point of digital economy risk prevention and digital governance in China, and provided countermeasures for legal governance of digital economy risk prevention. It also explored the construction of a digital economy risk warning system combining BPNN (Back propagation neural network) and GA (Genetic Algorithm) algorithm. Finally, the experiment proved that the accuracy of predicting "good economy"and "severe economic risk warning"was over 94%. © 2024 IEEE.

关键词： Back Propagation Neural Network Digital Economy Risk Evaluation and Prediction machine learning algorithms

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