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检索条件"主题词=Machine Learning Algorithms"

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A Combined Ontology and machine learning Framework for Predicting Asbestos in Buildings

A Combined Ontology and Machine Learning Framework for Predi...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Sarah Ayad Fatimah Alsayoud Lydia Chibout Khelifa Faculty of Computer Studies Arab Open University Saudi Arabia Centre Scientifique et Technique du Bâtiment Paris France

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

Asbestos remains a serious public health concern, particularly in older buildings, despite its ban in France in 1997. While both ontology and machine learning techniques have been used independently in various domains, their combination offers a novel approach for enhancing the detection and classification of asbestos-containing materials. This research introduces an innovative framework that integrates an ontology-based semantic model with decision tree algorithms and Semantic Web Rule Language (SWRL) rules to address the challenges of accurate asbestos identification. The use of ontology allows us to incorporate domain knowledge and semantic relationships, improving model interpretability and classification precision. In this study, we compared three approaches: a decision tree model without ontology, SWRL rules applied to a numerical ontology (where categorical variables were encoded), and SWRL rules applied to a categorical ontology (preserving the original categorical values). Our results show that the categorical ontology, when combined with SWRL rules, significantly outperforms the other models in classifying buildings as containing or not containing asbestos. However, we also observed that missing data in the dataset negatively impacts the performance of the ontology-based approach, highlighting the limitations of semantic models in data-incomplete environments. This work demonstrates the potential of combining ontology and machine learning to enhance asbestos detection while identifying the challenges that arise from incomplete data.

关键词： Semantic Web Electric potential machine learning algorithms Semantics Buildings machine learning Ontologies Numerical models Decision trees Public healthcare

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A technique utilizing machine learning and Convolutional Neural Networks (CNN) for the identification of SQL Injection Attacks

A technique utilizing Machine Learning and Convolutional Neu...

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Science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Andri Setiyaji Kalamullah Ramli Zulkifli Yasin Hidayatulloh G.S. Budhi Dharmawan Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok Indonesia Center Of Network Communications Meteorological Climatological and Geophysical Agency Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Analyzing logs can aid in debugging or optimizing system performance. A comprehension of system efficacy is frequently linked to an awareness of the manner in which the system’s resources are utilized. SQL injection attacks, specifically targeting web applications, have become a significant risk in the realm of cybersecurity. SQL injection attacks primarily result in the unauthorized disclosure of user data, allowing for data manipulation, updating, and deletion within web applications. Conventional methods employed to mitigate SQL injections encompass rule-based matching and other associated techniques that have a narrow scope in addressing only a few types of SQL injections. This research study examines code specifically designed to identify and prevent SQL injection attacks. Various supervised machine learning techniques and Convolutional Neural Network (CNN) models are employed to assess the model. The CNN model that was suggested exhibited a notable accuracy rate of 91% and outperformed other machine learning algorithms. Furthermore, the report gives a comprehensive analysis of several machine learning algorithms employed for the detection of SQL injection attacks. The study evaluates the effectiveness of different methods in detecting SQL injection attacks using F1 Score, accuracy, recall and precision metrics. Also compares the performance of machine learning and CNN models.

关键词： learning systems Accuracy machine learning algorithms Codes Computational modeling System performance machine learning SQL injection Convolutional neural networks Regression tree analysis

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An Empirical Review of Supervised and Reinforcement learning algorithms for Personalized Exoskeleton Robot Training Systems in Neurorehabilitation

An Empirical Review of Supervised and Reinforcement Learning...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Sweety Narula R. S. Pol R. V Patil Dept of ENTC Engineering Vishwakarma Institute of Technology & Science Pune India PDEA COE Manjari Pune India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

There is an increasing need for developing more personalized machine learning algorithms for the training system of exoskeleton robots that are in use for the purpose of human neurorehabilitation patients. With increased neurological disorders, demand for effective and individually tailored rehabilitation solutions parallels. Traditional methods of rehabilitation are inadequate to keep up with the adjustment of treatment based on the needs of a patient that may result in very poor recuperation results. Current reviews on the applicability of machine learning in neurorehabilitation rarely fully consider the needs and complexities involved in the design of personalized exoskeleton training systems. There are several major limitations in the literature reviews: very often, they do not consider the possibility of integrating machine learning personalization into the processes of exoskeleton training; besides, in most cases, they lack a methodical way to compare algorithm effectiveness in various patient profiles; lastly, they do not give an adequate meta-analysis of the strong and weak points for the existing methodologies. These gaps impede the construction of more effective and more individualized rehabilitation programs. In this respect, the systematic review that will be presented herein will involve a systematic literature review, meta-analysis, and critical appraisal following machine learning algorithms specific for exoskeleton robot training systems. The methods include a systematic literature review by searching, categorizing, and the relevant studies; then meta-analysis is carried out based on the data gathered to give a quantitative assessment of the performance and effectiveness of these algorithms. This review is guided by the PRISMA guidelines for rigorous and transparent reporting. Furthermore, a critical appraisal framework enhances the review so that strengths and weaknesses of the many different machine learning techniques—from supervised learning to r

关键词： Training machine learning algorithms Systematics Exoskeletons Supervised learning Reinforcement learning Neurorehabilitation Appraisal Robots Systematic literature review

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Optimization of New Distribution Network Operation Based on Artificial Intelligence machine learning under the Dual Carbon Background

Optimization of New Distribution Network Operation Based on ...

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Clean Energy and Electric Power Engineering (ICCEPE), International Conference on

作者： Yan Zheng Xu Dan Cheng Xin Weiwei Li Qingbo Tu Ping Huang Man Zhang State Grid Economic and Technological Research Institute Co. Ltd Beijing China State Grid Shandong Electric Power Company Jinan Shandong China Cross-strait Tsinghua Research Institute Xiamen China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

In order to strengthen the application of artificial intelligence technology in the power grid, this paper proposes a new power system optimization calculation method based on artificial intelligence algorithms in the context of dual carbon. A machine learning based distribution network optimization method has been proposed. By proposing a complete calculation model and verifying it with actual data algorithms, the feasibility of the proposed method is demonstrated.

关键词： machine learning algorithms Costs Supply and demand Optimization methods machine learning Distribution networks Electricity supply industry Power grids Planning Carbon

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Advancements in machine learning for Predictive Modeling of Genetic Disorders

Advancements in Machine Learning for Predictive Modeling of ...

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Disruptive Technologies (ICDT), International Conference on

作者： Aadrita Nandy Parshotam School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Detecting and understanding gene patterns before the onset of genetic diseases holds immense promise for prevention. This paper discusses the current state of machine learning techniques available for the disease diagnosis problem, focusing on existing approaches, their analysis, and possible future work. machine learning algorithms can process large amounts of genetic information such as DNA sequences, gene expression data, and images to identify and recognize patterns that could be useful in predicting disease risk or its progression. In this regard, the paper also focuses on a sequence comparison method using protein-level similarity, leveraging codon-based translation from DNA to protein sequences to calculate similarity indices between a test sequence and multiple disease related sequences. This shows how machine learning and bioinformatics convergence can bring remarkable progress in predictive analytics.

关键词： Proteins Deep learning Support vector machines machine learning algorithms Accuracy Translation Predictive models Genetics Diseases Testing

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Efficient FPGA Implementation of Time-Domain Popcount for Low-Complexity machine learning

Efficient FPGA Implementation of Time-Domain Popcount for Lo...

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International Symposium on Advanced Research in Asynchronous Circuits and Systems

作者： Shengyu Duan Marcos L. L. Sartori Rishad Shafik Alex Yakovlev Emre Ozer Microsystems Research Group Newcastle University Newcastle upon Tyne UK Pragmatic Semiconductor Cambridge UK

ISBN: (数字)9798331503109

ISBN: (纸本)9798331503116

Population count (popcount) is a crucial operation for many low-complexity machine learning (ML) algorithms, including Tsetlin machine (TM)-a promising new ML method, particularly well-suited for solving classification tasks. The inference mechanism in TM consists of propositional logic-based structures within each class, followed by a majority voting scheme, which makes the classification decision. In TM, the voters are the outputs of Boolean clauses. The voting mechanism comprises two operations: popcount for each class and determining the class with the maximum vote by means of an argmax *** TMs offer a lightweight ML alternative, their performance is often limited by the high computational cost of popcount and comparison required to produce the argmax result. In this paper, we propose an innovative approach to accelerate and optimize these operations by performing them in the time domain. Our time-domain implementation uses programmable delay lines (PDLs) and arbiters to efficiently manage these tasks through delay-based mechanisms. We also present an FPGA design flow for practical implementation of the time-domain popcount, addressing delay skew and ensuring that the behavior matches that of the model’s intended functionality. By leveraging the natural compatibility of the proposed popcount with asynchronous architectures, we demonstrate significant improvements in an asynchronous TM, including up to 38% reduction in latency, 43.1% reduction in dynamic power, and 15% savings in resource utilization, compared to synchronous TMs using adder-based popcount.

关键词： machine learning algorithms Inference mechanisms machine learning Delay lines Dynamic scheduling Inference algorithms Delays Resource management Time-domain analysis Field programmable gate arrays

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Hybrid Deep learning for Heart Disease Prediction: Fourier Transformation meets CNN on MIT-BIH Arrhythmia Dataset

Hybrid Deep Learning for Heart Disease Prediction: Fourier T...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Tanishq Soni Deepali Gupta Mudita Uppal Ankita Kumari Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

heart disease continues to cause major morbidity and death globally. Heart disease prognosis and prevention remain difficult despite medical breakthroughs. Coronary artery disease, heart failure, arrhythmias, and congenital heart abnormalities are all types of heart disease. These illnesses strain people, healthcare systems, and economies worldwide. Age, gender, genetics, hypertension, diabetes, obesity, smoking, and inactivity are heart disease risk factors. machine learning algorithms may improve illness prediction and risk stratification using vast medical data. ML models may find complicated patterns and interactions in electronic health records, medical imaging, genetic data, and lifestyle variables. Hybrid ML models increase predicted accuracy and generalizability by combining decision trees, support vector machines, neural networks, and ensemble approaches. Innovative prediction and preventative methods are needed to combat heart disease. machine learning hybrid models may improve heart disease risk prediction by integrating varied data sources, using complicated algorithms, and boosting forecast accuracy.

关键词： Heart Accuracy machine learning algorithms Biological system modeling Arrhythmia Radar Predictive models Genetics Diseases Biomedical imaging

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Enhanced Water Quality Classification through Integrated machine learning and Ensemble Methods

Enhanced Water Quality Classification through Integrated Mac...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Preet Singh Taniya Hasija Kr Ramkumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

With the continuous rise in the pollution level of drinking resources, water quality is becoming a major global concern. Thus, techniques for regulating and categorizing water according to the presence of impurities must be developed. In this research article, machine learning (ML) Supervised Gradient Boosting (GB) Technique namely XGBoost, CatBoost and LGBoost is used for the classification analysis. To further increase the Efficiency and robustness of the classification model, weighted voting and stacking ensemble techniques are utilized. The comparison of the accuracy of the classification models used are combined using the ensemble techniques and the new classifier achieved an accuracy of 94.70% which is higher as compared to the other classifiers used. Moreover, the Jaccard Index Comparison of all the algorithms shows the similarity of the values predicted and tested is higher for the Stacking Ensemble Algorithm with a value of 66%. This classification is essential in making smart decisions about sustainable water resource management.

关键词： Accuracy machine learning algorithms Stacking Water quality Prediction algorithms Water pollution Robustness Classification algorithms Sustainable development Water resources

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machine learning based approach for Myocardial Infarction Prediction using real time ECG Signals

Machine Learning based approach for Myocardial Infarction Pr...

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Intelligent Systems and machine learning Conference (ISML)

作者： Shilpa Sondkar Sharv Apte Aneesh Deshmukh Paritosh Chavan Dept. Of Instrumentation and Control Engineering VIT Pune India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

This project focuses on utilizing machine learning techniques to identify and detect abnormal cardiac conditions through the analysis of Electrocardiogram (ECG) signals. With cardiovascular diseases being a significant global health concern, the need for precise diagnostic tools is paramount. The primary objectives of this study involve identifying specific cardiac conditions, including Myocardial Infarction, Arrhythmias and discerning normal cardiac conditions. Real time ECG signals were taken from Symbiosis hospital and research center pvt. Limited hospital in Satara. The methodology encompasses the acquisition of diverse ECG datasets, preprocessing to enhance signal quality, and feature extraction to characterize different cardiac conditions effectively. Support vector machines (SVMs), logistic regression, kNNs, and XGBoost these machine learning techniques are employed to develop robust classification models. The anticipated outcomes of this research aim to provide accurate models capable of early detection, enabling prompt interventions and, ultimately, enhancing patient outcomes within the domain of cardiovascular health.

关键词： Support vector machines machine learning algorithms Accuracy Signal processing algorithms machine learning Cardiac arrest Electrocardiography Myocardium Prediction algorithms Real-time systems

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Predictive Insights With machine learning and Consumer Behavior Models in E-commerce

Predictive Insights With Machine Learning and Consumer Behav...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Manita D Shah Naresh Sharma Khushboo Sharma School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Predictive insights with device learning and patron conduct fashions in e-commerce allow groups to find patterns and forecast destiny results primarily based on past facts. With the aid of leveraging information generated via analytics and device studying algorithms, corporations can gain precious insights into consumer choices, insights that might be used to enhance clients’ online reports and, on the flip, generate greater sales and higher customer loyalty within the lengthy period. Additionally, through the use of predictive fashions, organizations can benefit precise understanding of their customers’ behaviors, supplying a possibility to create more tailored services based on their purchaser’s options. Gadget mastering and purchaser conduct models in ecommerce could show vital for businesses to benefit from competitive blessings and to have an aggressive facet over competitors in the industry. Predictive insights with device learning and patron conduct fashions in e-commerce allow groups to find patterns and forecast destiny results primarily based on past facts. With the aid of leveraging information generated via analytics and device studying algorithms, corporations can gain precious insights into consumer choices, insights that might be used to enhance clients’ online reports and, on the flip, generate greater sales and higher customer loyalty within a lengthy period. Additionally, through the use of predictive fashions, organizations can benefit precise understanding of their customers’ behaviors, supplying a possibility to create more tailored services based on their purchaser’s options. Gadget mastering and purchaser conduct models in e-commerce could show vital for businesses to benefit from competitive blessings and to have an aggressive facet over competitors in the industry.

关键词： Industries Analytical models machine learning algorithms Target recognition Biological system modeling Organizations Predictive models Prediction algorithms Electronic commerce Predictive analytics

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