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检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是51-60 订阅

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A Review of machine learning algorithms in Diabetes Management

A Review of Machine Learning Algorithms in Diabetes Manageme...

引用

Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Unnati Gayaki Subodh Daronde Abhay Tale Aditya Barhate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University)) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare.

关键词： Deep learning Support vector machines Ethics machine learning algorithms Reviews Neural networks Medical services Diabetes Convolutional neural networks Long short term memory

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Leveraging machine learning algorithms for Comprehensive Marketing Analysis

Leveraging Machine Learning Algorithms for Comprehensive Mar...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： Ramesh Dadi Kudurupaka Harshavardhan Therala Abhinay Kumar Enumula Siddardha Reddy School of Computer Science and Artificaial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

This study evaluates the use of advanced marketing analytics techniques on consumer data set to inform and optimize decision-making in issues of customer segmentation, targeting, and campaign effectiveness. Using customer data with mixed variables that represent product preferences, purchase behaviors, demographic characteristics, and more, it applies the dataset of customers to the campaign. The study makes use of Artificial Neural Network models, Support Vector machines, Random Forest, and XGBoost or Ensemble Boosting machine learning models that are non-linear for complex consumer behavior recognition. The previous part mentions the combination of Artificial Intelligence with human judgment as a way to solve the problems related to feelings and data management. The findings reveal that XGBoost improves the model accuracy by ensuring non-linear relationships among data variables which is facilitated by the ANN technique. Results revealed in this section that adaptive, data-driven strategies are now coming to digital consumer trends so the accuracy of marketing on dynamic market conditions will be increased.

关键词： Support vector machines Adaptation models Accuracy machine learning algorithms Artificial neural networks Predictive models Market research Data models Informatics Random forests

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Intrusion Detection System Based On machine learning algorithms In the DOUNG Ad Hoc Network

Intrusion Detection System Based On Machine Learning Algorit...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Boubacar Tawayé Abdoul Aziz Mahamadou Issoufou Tiado Boukar Abatchia Nicolas Boureima Djibo Abasse Department of Mathematics and Computer Science Research team on Network and Telecommunication University of Abdou Moumouni Niamey Niger

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The New Generation of Open Digital Universities (DOUNG) [1] is a model designed to cover as few human resources (professors) and material resources (lecture halls and classrooms) as possible. Its architecture offers global ubiquity and tailor-made offers to learners by relying on the IT and telecom networks already available. Thus, it inherits the vulnerabilities and threats inherent in these channels, in particular those of wireless networks and without pre-existing infrastructure. This lengthens the perimeter to be protected and exponentially increases the risk of intrusions into the synchronous course offer. To overcome these challenges, we proposed and tested an intelligent intrusion detection model based on machine learning (ML). Based on the approaches used (Fl score and confusion matrix), it appears from our experiment that logistic regression (LR) provides the best detection of MiTM attacks [1]–[3]. in the ad hoc network of the DOUNG.

关键词： Support vector machines Radio frequency Logistic regression machine learning algorithms Wireless networks Intrusion detection Ad hoc networks Telecommunications Network intrusion Random forests

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Credit Card Fraud Detection Using KNC, SVC, and Decision Tree machine learning algorithms

Credit Card Fraud Detection Using KNC, SVC, and Decision Tre...

引用

AI in Cybersecurity (ICAIC), International Conference on

作者： Pushpanjali Pandey K K Garg Department of Management Lingaya’s Vidyapeeth Faridabad India

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Credit cards are replacing cash in shopping. With the increase in popularity of plastic money like credit cards, hackers are misusing this as an opportunity to commit fraud. In this research work, we have used three different machine-learning techniques to detect credit card fraud. We have taken a credit card fraud dataset from Kaggle and applied K Neighbor Classifiers (KNC), Support Vector Classifiers (SVC), and decision tree machine-learning methods to this dataset. We have achieved $\mathbf{9 9. 9 4 \%}$ accuracy using both KNN and Decision Tree machine learning algorithms. We have got $\mathbf{9 9. 8 4 \%}$ accuracy using the Support vector classifier (SVC) machine learning Algorithm.

关键词： machine learning algorithms Accuracy Static VAr compensators machine learning Nearest neighbor methods Credit cards Vectors Fraud Decision trees Plastics

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Predicting Port Congestion at Busan Port Using machine learning algorithms and Temporal Variables

Predicting Port Congestion at Busan Port Using Machine Learn...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Sang-Hyun Ha Ki-Hwan Kim Young-Jin Kang Ji Yeon Kim Seok-Chan Jeong AI Grand ICT Research Center Dong-Eui University Busan Republic of Korea Department of Artificial Intelligence Dong-Eui University Busan Republic of Korea Dept. of e-Business AI Grand ICT Research Center Dong-Eui University Busan Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Ports are vital infrastructures for global logistics and trade, with South Korea handling approximately 99.7 % of its import and export cargo via maritime routes. Busan Port, the largest in the country and the seventh-largest container port worldwide, plays a crucial role in both national logistics and regional economic growth. However, congestion in port operations leads to delays, increased costs, and reduced efficiency. This study employs actual data from Busan Port (February to September 2024) to analyze the impact of temporal variables-such as day of the week, time of day, and monthly factors-on port congestion. machine learning algorithms, including Random Forest, XGBoost, and LightGBM, were utilized to predict congestion levels using a refined dataset of approximately 1.4 million samples. Results indicate that Extra Trees and CatBoost classifiers achieved high accuracy (0.9654) and AUC (0.9952), while Extreme Gradient Boosting reached an AUC of 0.9989, demonstrating exceptional performance. These findings highlight the effectiveness of machine learning in capturing the nonlinear dynamics of port operations. Future research will aim to enhance model performance by expanding data collection and integrating external factors such as weather conditions and cargo characteristics. This study underscores the potential of machine learning tools in optimizing port management and improving logistical efficiency.

关键词： machine learning algorithms Costs Seaports Data collection Data models Nonlinear dynamical systems Delays Random forests Logistics Meteorology

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A Comparative Analysis of Feature Selection and machine learning algorithms for Enhanced Anomaly Detection in 5G Core Networks

A Comparative Analysis of Feature Selection and Machine Lear...

引用

International Global Information Infrastructure Symposium (GIIS)

作者： Júnia Maísa Oliveira César Morais Daniel Macedo José Marcos Nogueira Computer Science Department Universidade Federal de Minas Gerais Department of Electrical Electronic and Information Engineering (DEI) University of Bologna Italy

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

A critical challenge lies in choosing the appropriate processing techniques to achieve optimal evaluation performance in machine learning algorithms for the 5G core data analysis in anomaly detection use case. In this work, we address this challenge by focusing on the comparative analysis of machine learning (ML) algorithms combined with data processing techniques for anomaly detection in 5G core networks. ML algorithms are promising for detecting such anomalies, but their performance heavily depends on the quality of the features used. This work highlights the importance of feature selection, demonstrating that by applying techniques such as Pearson’s correlation and dimensionality reduction (PCA and LDA), we can enhance the detection accuracy of various ML models. We evaluate eight ML algorithms using the 5GAD-2022 cybersecurity dataset and show that Random Forest achieves the best performance in terms of F1-score, AUC, and accuracy, regardless of the preprocessing techniques applied. The results suggest that meticulous feature selection can significantly enhance the algorithms’ effectiveness in detecting anomalies.

关键词： machine learning algorithms Data analysis Accuracy 5G mobile communication Focusing Feature extraction Computer security Anomaly detection Random forests Principal component analysis

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Soft Alert Generation for student Dropout Mitigation and Proactive Management by machine learning algorithms

Soft Alert Generation for student Dropout Mitigation and Pro...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Geetha Spandana A P Vijay D Vishruth M V Dept. of CSE B.N.M Institute of Technology Bangalore Karnataka India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Proactive dropout mitigation is crucial for enhancing student success and retention in schools. Dropout rates pose significant challenges in education systems globally, impacting students, communities, and societies. This paper emphasizes the necessity of proactive measures to address dropout rates and support student achievement by building an educational network. Through proactive approaches, schools can identify and address risk factors early, preventing student disengagement and dropout. Various strategies such as early interventions, personalized support systems, academic enrichment programs, and community partnerships are examined for their efficacy in promoting student engagement and retention. Additionally, the role of data-driven decision-making and predictive analytics in identifying at-risk students and tailoring interventions is explored. Implementing proactive dropout mitigation requires collaboration among educators, administrators, families, and community stakeholders. Prioritizing proactive measures can foster a supportive learning environment, empowering students to excel academically. This paper highlights the significance of proactive dropout mitigation that has been enabled using a seemingly enhanced automated full-stack student management system integrated with ML algorithms used in enhancing student success and promoting educational equity. By investing in proactive interventions, educational institutions can reduce dropout rates and foster a culture of academic excellence and lifelong learning.

关键词： machine learning algorithms Prevention and mitigation Education Decision making Buildings Collaboration machine learning Stakeholders Predictive analytics

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Comparative Analysis of machine learning algorithms for Enhancing Stroke Prediction in Healthcare Database

Comparative Analysis of Machine Learning Algorithms for Enha...

引用

Devices for Integrated Circuit (DevIC)

作者： Samik Basu Shubhangini Dey Prapti Ganguly Debangana Basak Pritha Banerjee Amlan Chakrabarti Dept. of Computer Science & Engg. University of Calcutta Kolkata India Dept. of Information Technology Jadavpur University Kolkata India A.K. Choudhury School of I.T. University of Calcutta Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

Stroke is a major global cause of disability and death, often resulting from sudden blockages in the brain or heart. Recognizing early warning signs can help reduce stroke risk and is crucial for effective stroke prevention and management. This study enhances stroke prediction using machine learning and neural networks, analyzing risk factors like hypertension, heart disease, ever-married, work type, residence type, glucose levels, BMI, smoking, gender, and age. Ten machine learning algorithms are tested on a well-curated clinical dataset. The results showed high accuracy for Random Forest (98.17%), Logistic Regression (98.32%), CatBoost (98.52%), and an Ensemble Technique (98.63%). However, the Artificial Neural Network (ANN) outperformed all with 99.37% accuracy, the highest AUC score, and the lowest false positive rate. These findings highlight ANN’s effectiveness in stroke risk assessment, offering valuable insights for improved prevention and timely medical interventions.

关键词： Heart Integrated circuits Logistic regression machine learning algorithms Accuracy Prevention and mitigation Artificial neural networks Medical services Stroke (medical condition) Random forests

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Impact Analysis of Nasdaq-100 Using machine learning algorithms

Impact Analysis of Nasdaq-100 Using Machine Learning Algorit...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Mohd. Toushif Akhtar Shreshth Goyanka Alka Chaudhary Amity Institute of Information Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

This study focuses on leveraging machine learning algorithms to predict and analyse the performance of the NASDAQ-100 index in financial markets. Our objectives encompass developing accurate predictive models, identifying the growth of the index, and determining the trends. Through extensive data preprocessing and model evaluation, our findings indicate that the index possesses great potential for a long-term investment. This research holds significance for people involved in related sectors and also provides guidance for further exploration in this field.

关键词： Analytical models machine learning algorithms Accuracy Computational modeling Biological system modeling Time series analysis Predictive models Data models Indexes Long short term memory

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Application of machine learning algorithms in Artificial Intelligence Image Recognition Technology

Application of Machine Learning Algorithms in Artificial Int...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Qi Ouyang Siyang Dai Kexin Xie Zeyu Sun Yao Wang Sustainable Development of Architecture and Landscape Polytechnic of Milan Milan Italy Faculty of Engineering and Information Technology The University of Melbourne Victoria Australia College of Business and Public Management Wenzhou-Kean University Wenzhou China School of Environment and Science Griffith University Brisbane Australia Nanjing Stomatological Hospital Affiliated Hospital of Medical School Institute of Stomatology Nanjing University Nanjing China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse targets, affecting accuracy and robustness. This study explores machine learning algorithms to enhance image recognition performance by addressing noise interference, intricate backgrounds, and feature extraction difficulties. It combines Convolutional Neural Networks (CNN) with transfer learning, starting with data preprocessing to reduce noise, using pre-trained CNN models to extract high-level features, and fine-tuning with a ResNet transfer learning strategy for specific tasks. Additionally, ensemble learning methods are employed to further improve model robustness and accuracy. Experimental results show that the ensemble model maintains around 85% accuracy even with high background complexity, and transfer learning achieves 90% accuracy when the sample size reaches 1000. These findings demonstrate that transfer and ensemble learning effectively enhance image recognition accuracy and resilience in complex environments.

关键词： Image recognition Accuracy machine learning algorithms Transfer learning Noise Interference Feature extraction Robustness Ensemble learning Convolutional neural networks

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