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International Conference on Adaptive Science & Technology (ICAST)

作者： Elliot Kojo Attipoe Alimatu Saadia Yussiff Maame Gyamfua Asante-Mensah Dept. of Computer Science and Information Technology University of Cape Coast Cape Coast Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Cardiovascular disease is a life-threatening condition accounting for over 17 million deaths globally every year. Several studies have revealed the advancement of machine learning algorithms in predictive modeling and risk assessment, leading to improved chronic disease management and preventive measures. The algorithms analyze complex medical datasets to uncover hidden patterns, trends, and correlations that might be difficult for the medical practitioner, thereby improving the efficiency and accuracy of the medical decision-making process. This study utilizes a cardiovascular disease dataset comprising 70,000 instances to compare the performance of ten classification algorithms in predicting cardiovascular diseases. A new machine learning model is proposed, which performs better than all existing models using the same dataset. The results show the XGBoost classifier as the best model for predicting cardiovascular diseases, with an accuracy of 73.3% and an AUC score of 80%.

关键词： Training machine learning algorithms Accuracy Stacking Predictive models Prediction algorithms Classification algorithms Cardiovascular diseases Tuning Testing

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Comparison of Long-term Energy Demand Forecasting in Developing and Developed Countries Using machine learning-based algorithms

Comparison of Long-term Energy Demand Forecasting in Develop...

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Technology and Energy Management (ICTEM), International Conference on

作者： Hossein Kiani Sajad Golshaeian Mohammad Hassan Nazari G. B. Gharehpetian Seyed Hossein Hosseinian Jafar Sarbazi Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran

ISBN: (数字)9798350329797

ISBN: (纸本)9798350329803

As the world’s population grows, many nations grapple with the task of supplying sufficient energy. One effective way to manage and plan for this demand is through energy demand forecasting. In this research, we adopt a method employing machine learning algorithms to predict energy demand in various countries, both developed and developing, up to the year 2050. For our long-term forecast covering 2020 to 2050, two types of historical data are utilized: (i) energy consumption (EEC), and (ii) socio-economic indicators, such as Gross domestic product (GDP), energy import and export, and population. An Artificial Neural Network (ANN) based machine learning is employed utilizing 30 years’ worth of socio-economic data (1991-2020). In this manner, the utilization of the ANN facilitates the prediction of long-term energy consumption (EEC) for the planning period spanning from 2020 to 2050. Furthermore, we present an optimization model designed to enhance the precision of our predictions. The outcomes from machine learning algorithms serve as input for our comprehensive model, implemented through ANN across various sections. Ultimately, our forecasts indicate a projected increase in electric energy consumption by $130.1 \%$ for Iran, 37.4% for Portugal, and 58.6% for the United States in 2050 compared to 2020. Significant distinctions exist between developing and developed economies with regard to the anticipated trends in Energy Efficiency Compliance (EEC).

关键词： Energy consumption machine learning algorithms Economic indicators Sociology Demand forecasting Artificial neural networks Predictive models

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Comparative Evaluation of machine learning Classifier algorithms for Electric Load Disaggregation Based on Average Power Signatures

Comparative Evaluation of Machine Learning Classifier Algori...

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IEEE International Congress on Electronics, Electrical Engineering and Computing (INTERCON)

作者： Jose Bruno Gutiérrez César Beltran Castañon Pontificia Universidad Católica del Peru

ISBN: (数字)9798350378344

ISBN: (纸本)9798350378351

Non-Intrusive Load Monitoring (NILM) systems disaggregate energy consumption data to provide insights into appliance-level usage. This study addresses the challenge of accurately estimating energy consumption using machine learning algorithms, particularly with low-frequency data. We propose a simplified NILM methodology based on time windows to reduce computational load and enhance efficiency. This method extracts features using power centroids and binary coding for labeling, making it suitable for resource-constrained devices. We evaluate the performance of six machine learning algorithms, including Random Forest Classifier (RFC), K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Support Vector machines (SVM), using the Reference Energy Disaggregation Dataset (REDD). Our results demonstrate that RFC and SVC outperform other models in classifying and predicting electrical power, especially with imbalanced datasets. This research contributes to the development of practical NILM solutions for promoting energy efficiency in residential settings

关键词： Support vector machines Energy consumption machine learning algorithms Power demand Static VAr compensators Clustering algorithms Predictive models Nearest neighbor methods Prediction algorithms Random forests

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Towards Benthic Habitat 3D Mapping Using machine learning algorithms and Structures from Motion Photogrammetry

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REMOTE SENSING 2020年第1期12卷 127-127页

作者： Mohamed, Hassan Nadaoka, Kazuo Nakamura, Takashi Benha Univ Shoubra Fac Engn Dept Geomat Engn Cairo 11672 Egypt Tokyo Inst Technol Sch Environm & Soc Meguro Ku O Okayama 2-12-1 Tokyo 1528552 Japan

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.

关键词： machine learning algorithms benthic 3D mapping towed underwater video camera hybrid classifiers structure from motion

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An Evaluation Model for Intelligent Manufacturing Systems Based on machine learning and Improved algorithms

An Evaluation Model for Intelligent Manufacturing Systems Ba...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Deng Cheng Sun Tao Jiang Zhou Department of Mechanical and Electrical Engineering Anhui Vocational College of Grain Engineering Hefei China Mechanical and Electrical Engineering Department Anhui Automobile Vocational and Technical College Hefei China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

The evaluation model used in underlining intelligent manufacturing systems (IMS) and prospects based on improved algorithms and machine learning (ML) provides a systematized model for the optimization of production operations in order to optimize operational efficiency. ML, as viewed within this context, helps in the facilitation and the analysis of significant volumes of datasets primarily generated by IMS, efforts that help in the identification of insights and patterns that traditional models have widely overlooked. Through the integration of ML algorithms, a designed model may significantly predict the maintenance demands, significantly improve the quality of manufactured product quality, and significantly cut down on production downtimes through the use of advanced analytics. Improved and enhanced algorithms that include genetic algorithms and enhanced neural networks significantly refine the evaluation of the systems processes, thus providing adaptive and accurate predictions and capabilities. Overall, the findings of this study establish that a combination of ML with other improved algorithms may enhance the efficiency, flexibility, and productivity of intelligent manufacturing systems—thus driving advancements within the industry.

关键词： Productivity Analytical models Adaptation models machine learning algorithms Evaluation models machine learning Predictive models Prediction algorithms Intelligent manufacturing systems Optimization

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Adversarial machine learning-Based algorithms for Mobile Network Security Event Traceability and Responsibility Tracking

Adversarial Machine Learning-Based Algorithms for Mobile Net...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Renfei Zhang Cheng Zeng Shaanxi Corps of PAP Xi'an Shaanxi China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Conventional mobile network security event source tracing and responsibility tracking algorithms mainly use the DDoS (Distributed Denial of Service) distributed denial model to track attack sources, which are susceptible to the sensitivity of traffic changes, resulting in poor overall performance. Therefore, a method is proposed for Mobile network security event traceability and responsibility tracking algorithm based on adversarial machine learning, that is, using adversarial machine learning to build a mobile network security event traceability and responsibility tracking model, and designing network security event traceability and tracking processes, thereby realizing mobile network security event traceability and accountability tracking. Experimental results show that the designed adversarial machine learning traceability and responsibility tracking algorithm for mobile network security events has good comprehensive performance indicators and has a certain application value. It has made certain contributions to reducing mobile network security risks and improving mobile network transmission quality.

关键词： machine learning algorithms Sensitivity Computer network reliability Network security Reliability engineering Adversarial machine learning Internet Computer crime

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Simulation and Optimization of Venturi Injector by machine learning algorithms

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2020年第8期146卷 1-9页

作者： Wang, Haitao Wang, Jiandong Yang, Bin Mo, Yan Zhang, Yanqun Ma, Xiaopeng Chinese Acad Agr Sci Inst Environm & Sustainable Dev Agr 12 South Zhongguancun Rd Beijing 100081 Peoples R China China Agr Univ Coll Water Resources & Civil Engn 17 East Tsinghua Rd Beijing 100038 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River 20 West Chegongzhuang Rd Beijing 100048 Peoples R China Xinjiang Acad Agr Sci Inst Soil Fertilizer & Agr Water Saving 403 Nanchang Rd Urumqi 830091 Peoples R China

This paper discusses the problem of low injection rates from Venturi injectors. The optimal combination of key structural parameters for Venturi injectors was investigated using a simulation software platform based on machine learning algorithms. The research considered different nozzle diameters under an inlet pressure of 0.3 MPa and outlet pressure of 0.1 MPa. For the various nozzle diameters, the optimal ranges of the contraction angle (20 degrees-30 degrees), diffusion angle (8 degrees-10 degrees), throat length (40-50 mm), and ratio of throat diameter to nozzle diameter (1.5-1.66) were found, and the parameter combinations that maximized the injection rate were determined. A regression model was used to predict the maximum injection rate with different nozzle diameters. For a nozzle diameter of 4 mm, the maximum injection rate increased by about 200% compared with the original model. In addition, a regression model for the prediction of the injection rate based on injector structural parameters was construction using data from physical injector models and verified by a three-dimensional printer. The model may be used to quickly and effectively design or predict the injection rate for different structural parameters of the Venturi injector.

关键词： Venturi injector Maximum injection rate Simulation machine learning algorithms Optimal combination

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Hybrid machine learning algorithms for Predicting Academic Performance

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第1期11卷 32-41页

作者： Sokkhey, Phauk Okazaki, Takeo Univ Ryukyus Grad Sch Engn & Sci 1 Senbaru Nishihara Okinawa 9030123 Japan Univ Ryukyus Dept Comp Sci & Intelligent Syst 1 Senbaru Nishihara Okinawa 9030123 Japan

The large volume of data and its complexity in educational institutions require the sakes from informative technologies. In order to facilitate this task, many researchers have focused on using machine learning to extract knowledge from the education database to support students and instructors in getting better performance. In prediction models, the challenging task is to choose the effective techniques which could produce satisfying predictive accuracy. Hence, in this work, we introduced a hybrid approach of principal component analysis (PCA) as conjunction with four machines learning (ML) algorithms: random forest (RF), C5.0 of decision tree (DT), and naive Bayes (NB) of Bayes network and support vector machine (SVM), to improve the performances of classification by solving the misclassification problem. Three datasets were used to confirm the robustness of the proposed models. Through the given datasets, we evaluated the classification accuracy and root mean square error (RSME) as evaluation metrics of the proposed models. In this classification problem, 10-fold cross-validation was proposed to evaluate the predictive performance. The proposed hybrid models produced very prediction results which shown itself as the optimal prediction and classification algorithms.

关键词： Student performance machine learning algorithms k-fold cross-validation principal component analysis

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Quantitative Feasibility of Predictive machine-learning Optimization algorithms for Refugee Routing

Quantitative Feasibility of Predictive Machine-Learning Opti...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Pranav Kulkarni Luke Jeon Los Angeles Pierce College Los Angeles California USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Refugee migration patterns are highly non-linear and as such, difficult to predict and optimize. In consideration of the unpredictability of risks faced by refugees, dynamic machine algorithms are best suited for determining optimal routes. However, the implementation of deterministic predictive models has been historically challenging in anticipating behavior in the second-order chaotic systems which typically characterize social interactions. In this study, we assess the feasibility of a predictive machine learning model in second-order chaotic systems toward risk assessment for migrating refugees, as well as the potential for predictive routing to occur on a locally computed basis for public use through low-resource machine learning software. The focus of our research is on providing evidence that determines the theoretical limits of dynamic, lightweight predictive models in second-order chaotic environments.

关键词： machine learning algorithms Heuristic algorithms Computational modeling Software algorithms machine learning Predictive models Routing

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Performance Evaluation of machine learning Regression algorithms for Soil Nitrogen Estimation

Performance Evaluation of Machine Learning Regression Algori...

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International Conference on Advances in Electronics, Computers and Communications (ICAECC)

作者： Anandita Dey Utpal Sarma Dept. of Instrumentation & USIC Gauhati University Guwahati India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

Soil nitrogen is vital for healthy plant growth, and its proper balance is crucial for soil fertility. machine learning (ML) has gained popularity in agriculture due to its ability to analyse large datasets quickly and accurately, offering advantages over traditional time-consuming and costly laboratory approaches. This study compares five ML regression algorithms, namely, multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), k-nearest neighbour regression (KNN R), and support vector regression (SVR) to predict soil nitrogen (N) levels using electrical conductivity (EC), organic carbon content (OC), and pH values of soil. The trained models are assessed using performance metrics such as root mean squared error (RMSE), r-squared (R 2 ), and mean absolute percentage error (MAPE). Among these models, SVR demonstrates the most promising result, with an R2 value of 0.928, MAPE value of 15.90, and RMSE value of 1.053.

关键词： Support vector machines machine learning algorithms Linear regression Soil Predictive models Prediction algorithms Agriculture

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