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检索条件"主题词=Machine Learning Algorithms"

共 25882 条记录，以下是921-930 订阅

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A Study on Drug Addiction Prediction in Bangladeshi Universities Using Advanced machine learning algorithms

A Study on Drug Addiction Prediction in Bangladeshi Universi...

引用

Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Binota Ray Umme Ayman Md Atik Asif Khan Akash Nusrat Khan Narayan Ranjan Chakraborty Md. Hasan Imam Bijoy Department of Computer Science and Engineering Daffodil International University Dhaka 1216

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Infection by alcohol and drugs has become one of the bigger dangers posed to the youth of Bangladesh, and responsible action on the part of society is called for in order to save these tender minds. In a bid to solve this problem, a study was conducted on reducing drug abuse using machine learning concepts. The data were gathered from 307 students, wherein there are drug users and non-users aged between 17-35, with the final dataset containing 21 features. Hence, the strategy for drug abusers can be predicted with the probability of an individual's addiction to drugs by designing a machine learning strategy. It is based on the consultations and opinions obtained from medical professionals and drug addicts, together with literature reviews, that key risk factors of addiction are suggested in the present study. The collected data was pre-processed and fed for four machine learning algorithms: logistic regression, SVM, naïve Bayes, and XGBoost. The performances of each classifier, as evaluated by some of the notable metrics, turned out to be: 95.16% for SVM, 93.55% for logistic regression, 96.77% for naïve Bayes, and 98.39% for XGBoost. The present research informs about the prospects of machine learning for risk assessment in drug addiction among the youth of Bangladesh and contributes to more effective prevention strategies. The main goal of this research is to develop predictive models to identify a person's risk of drug addiction based on behavioral, social and health factors. It can be an early intervention, prevention effort and help to improve its condition.

关键词： Drugs Support vector machines Logistic regression machine learning algorithms Addiction Prevention and mitigation machine learning Predictive models Prediction algorithms Bayes methods

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Promoter Prediction in DNA Classification Using machine learning algorithms

Promoter Prediction in DNA Classification Using Machine Lear...

引用

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

作者： Mohammad Moin Uddin Jasrin Shiddike Afsana Ahmed Tanveer Ahsan Department of Computer Science & Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

The genetic code distinguishing species is stored in DNA (Deoxyribonucleic acid), consisting of four bases: Adenine (A), Cytosine (C), Guanine (G), and Thymine (T). Gene promoters, critical for controlling transcription, are located near transcription start sites. Advances in sequencing technology have accelerated DNA data growth. ML (machine learning) has greatly aided DNA data analysis. This research aims to classify sequences as promoters or non-promoters accurately, achieving high true positive and true negative rates. We collected 106 human promoter and non-promoter data from the University of California, Irvine, ML repository., built a robust promoter classifier using various machine learning models, and utilized tokenization methods like label encoding for feature vectors with different k-mer sizes (3-mer, 6-mer). Combining 6-mer and PCA improved accuracy. Our system successfully distinguishes DNA sequences as promoters or non-promoters with high accuracy and F1-score, around 91% and 93%, automating the identification of complex biological features in sequence data.

关键词： Deep learning Sequential analysis Sentiment analysis Accuracy machine learning algorithms DNA Vectors

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Comparative Analysis of machine learning algorithms for Predicting Alzheimer's Disease

Comparative Analysis of Machine Learning Algorithms for Pred...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Chris Jose Preety Vidushi Christ(Deemed to be University)

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Alzheimer's disease (AD) is a progressive neurodegenerative disease marked by the loss of neurons in the brain leading to dementia. Early detection provides better patient outcomes and more time before the progression of the disease. In this study, a variety of machine learning models developed specifically- namely; a custom Random Forest model, Support Vector machine SVM, Logistic Regression LR model, Decision Tree DT, and a custom boosting model- will be developed and compared here that can be able to predict AD using both MRI and clinical data within the OASIS database. The result shows that a custom model of Random Forest achieved an accuracy of 86.21%. It highlights the significance of the adaptation of algorithms in machine learning to individual datasets. This study exemplifies how customized machine learning methods enhance the accuracy of neurodegenerative diseases, particularly Alzheimer's disease.

关键词： Support vector machines Adaptation models Accuracy machine learning algorithms Computational modeling Predictive models Brain modeling Data models Alzheimer's disease Random forests

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Integrating Artificial Intelligence and machine learning algorithms to Enhance Cybersecurity for E-Investment

Integrating Artificial Intelligence and Machine Learning Alg...

引用

IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Shubhi Saxena Garima Bhardwaj Amity University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

Cyber threats targeting these systems are increasingly being refused as the use of e-investment continues to grow. This study provides an insightful idea on how integrating AI and ML algorithms can help to boost the cybersecurity on Einvestment platforms. In order to identify irregularities, anticipate possible attacks, and enhance incident response, our AI- powered platform makes use of both supervised and unsupervised machine learning approaches. The methods for detecting fraud and emphasized the use of neural networks, decision trees, and support vector machines (SVM) in financial systems. Our system finds treads and weakness by analysis large amounts of data from multiple sources, allowing for proactive threat mitigation.

关键词： Support vector machines Radio frequency machine learning algorithms Accuracy Reinforcement learning Fraud Decision trees Reliability Computer security Resilience

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Malware Feature Extraction of the Alibaba Cloud Malware Dataset via machine learning algorithms

Malware Feature Extraction of the Alibaba Cloud Malware Data...

引用

Computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Qi Zhi Liu Mingxin Jia Yew The Burra Venkata Durga Kumar Iftekhar Salam Mohammad Arif Sobhan Bhuiyan School of Computing & Data Science Xiamen University Malaysia Sepang Malaysia School of EEE & AI Xiamen University Malaysia Sepang Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

This research explores a method for detecting and analyzing malware using machine learning algorithms, specifically variable-length N-grams and XGBoost, to extract significant behavioral features from API call sequences. By preprocessing the dataset and conducting statistical analysis to identify differences in API occurrences among malware families, the proposed method demonstrates high accuracy in classifying malware samples from the Alibaba Cloud dataset. The results show a 91.3% accuracy for multiclass classification and 99.0% for binary classification, highlighting the potential of variable-length N-grams and XGBoost in enhancing malware detection and analysis.

关键词： Technological innovation machine learning algorithms Accuracy Education Static analysis Production Feature extraction Malware Optimization Testing

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Deciphering Descriptors-Understanding Personality Beyond Resumes Using machine learning algorithms

Deciphering Descriptors-Understanding Personality Beyond Res...

引用

Trends in Engineering Systems and Technologies (ICTEST), International Conference on

作者： Nandini P Sony P Niranjana B Nair Sneha Devan Sona Martin Computer Science Department Managed by IHRD Govt. Model Engineering College Kochi India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

Recruiters face challenges in accurately evaluating candidates, with reliance on resumes alone hindering effectiveness. Predicting personality traits and assessing technical and soft skills pose additional hurdles, leading to inefficient selections and potential mismatches. To address this, a system integrating personality prediction via machine learning and resume analysis is proposed. Using a random forest model, candidate personality traits are predicted from standardized tests, encompassing dimensions like extraversion and conscientiousness. Simultaneously, the system conducts in-depth resume analyses, extracting qualifications, work experience, and interpersonal skills. By considering personality-job compatibility, this system offers comprehensive insights for informed decision-making, enhancing recruitment efficiency and reducing mismatches.

关键词： Industries machine learning algorithms Resumes Predictive models Prediction algorithms Classification algorithms Reliability

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Leveraging machine learning algorithms for Depression Prediction in College Students Using PHQ-9 Scores

Leveraging Machine Learning Algorithms for Depression Predic...

引用

PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS)

作者： S Vidya Nagalambika Swamy H Lalitha Bangalore Institute of Technology Bangalore Karnataka India M S Ramaiah College of Arts Science and Commerce Bangalore Karnataka India

ISBN: (数字)9798331541965

ISBN: (纸本)9798331541972

Depression among college students significantly affects their well-being and academic performance. Using PHQ-9 responses and Questionnaire, this study uses models of machine learning - including Logistic Regression, XGBoost, Support Vector machine and Random Forest in detecting depression. Key indicators were extracted to evaluate the models using recall, accuracy, precision, and F1-score showing that the Random Forest model attained the highest performance with an accuracy of 98.00% and an F1-score of 98.68%. XGBoost closely follows with 97.00% accuracy and 97.00% F1-score, while SVM also performed well at 96.59% accuracy. In contrast, Logistic Regression lagged significantly at 80.68% accuracy. These findings illuminate the effectiveness of advanced machine learning models in the early detection of depression among college students, suggesting their potential integration into campus mental health service.

关键词： Support vector machines Logistic regression Technological innovation Accuracy machine learning algorithms Mental health Forestry Depression Random forests

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SIGMAML: SNR-Guided 5G Mobility Management using machine learning algorithms

SIGMAML: SNR-Guided 5G Mobility Management using Machine Lea...

引用

Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Adnan Farooq Bhat Shahid Mehraj Shah Dept. of ECE Communication Control & Learning Lab (C2L2) National Institute of Technology Srinagar J&K India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

In the realm of 5G technology, seamless mobility relies heavily on efficient mobility management, particularly handovers (HOs). Our study investigates the parameters of A3 event and their impact on the mean edge signal-to-noise ratio (SNR), a vital but often overlooked metric. Using Deep Neural Networks (DNNs), we establish a detailed connection between A3 parameters and key performance indicators (KPIs). Our analysis highlights the dominance of A3-offset over A3-time to trigger (TTT) in influencing mean edge SNR. While DNNs offer complexity, decision tree provide a balanced trade-off between RMSE and complexity, aiding in optimizing mobility management for enhanced 5G network performance.

关键词： machine learning algorithms 5G mobile communication Key performance indicator Artificial neural networks Handover Aerospace testing User experience

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Optimizing machine learning algorithms for Heart Disease Prediction in Healthcare: A Comparative Study

Optimizing Machine Learning Algorithms for Heart Disease Pre...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Subha C. Balakrishnan T.P. Anish Gokila. E Nalini. M Siva Subramanian R Department of Computer Science and Business Systems Rajalakshmi Institute of Technology Chennai Dept of CSE S.A Engineering College Dept of CSE R.M.K. College of Engineering and Technology Tiruvallur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The healthcare business faces significant challenges in successfully maintaining and evaluating large amounts of patient data. Data analytics has emerged as a viable tool for issue resolution and decision-making as a result of technological advancements in healthcare. This automated procedure improves the resilience of healthcare systems by collecting and analyzing data to provide more effective and cost-effective treatments. One critical use of technology, primarily machine learning, involves the identification and diagnosis of diseases, notably heart disease, which contributes significantly to global mortality. This research study analyzes how machine learning methods (NB, RF, SVM, and LR) can predict the existence of heart disease. Given that nearly 90% of cardiac diseases are avoidable, using machine learning for prediction is critical. This research study assesses the performance of different algorithms using performance metrics such as accuracy, precision, area under the curve (AUC), and F1-Score. The experimental results reveal Random Forest (RF) as the most reliable predictor of heart disease with an accuracy of 83.52% compared to other supervised machine learning algorithms. The Random Forest classifier achieves F1-Score, AUC, and accuracy values of 84.21 %, 88.24%, and 88.89%, respectively. This study highlights the potential of ML, notably the Random Forest algorithm, in improving the predictive capacity of healthcare systems for more effective heart disease prevention and treatment.

关键词： Heart Measurement Accuracy machine learning algorithms Medical services Prediction algorithms Reliability Random forests Diseases Classification tree analysis

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Precision Agriculture Revolution: Enhancing Crop Recommendations with machine learning algorithms for Optimal Yield and Environmental Sustainability

Precision Agriculture Revolution: Enhancing Crop Recommendat...

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International Conference on Advances in Information Technology (ICAIT)

作者： Shifana Begum Akhilraj V. Gadagkar Rameesa K Department of CSE Srinivas University Institute of Engineering and Technology Mangaluru India Department of CSE Nitte (Deemed to be University) NMAM Institute of Technology Nitte India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Crop recommendation systems based on machine learning are becoming more and more widespread in precision agriculture. Using a variety of machine learning algorithms, these systems select crops depending on soil type, weather patterns, and other environmental factors. This system's primary objective is to raise agricultural yields while reducing the damaging impacts of farming on the environment. machine learning algorithms are used to examine large datasets containing information on crop traits, climate, and soil types. These algorithms may identify patterns in the data that people would find difficult or impossible to identify. Once the patterns are identified, the computers can predict which crops will grow most well in a given field. Statistical models that have been validated against real observations and trained on historical data are frequently the basis for recommendations generated by these systems. Two algorithms, such as KNN and Random Forest, were employed in our research. Based on user inputs such as location, soil type, season, and acreage, both models suggest two crops. It not only forecasts crops but also their price and yield. A farmer can use this information to choose which crop to produce. A model's performance is assessed using measures including recall, accuracy, precision, and F1 score. A more precise and trustworthy crop recommendation algorithm is suggested based on these metrics.

关键词： Temperature sensors Precision agriculture machine learning algorithms Accuracy Crops Nearest neighbor methods Prediction algorithms Real-time systems Recommender systems Random forests

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