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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是811-820 订阅

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Comparative Analysis of machine learning algorithms for Pulse Jammer Detection

Comparative Analysis of Machine Learning Algorithms for Puls...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Srishti Srivastava Prerna Chaudhary Manav R. Bhatnagar Department of Electronics & Communication Engineering Netaji Subhas University of Technology Delhi India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

This work compares various machine learning algorithms for predicting the presence of a random on/off jammer in wireless communication systems. We analyze four machine learning classification algorithms, Decision Trees, Support Vector machines, K-nearest neighbors, and Random Forest. The study applies data pre-processing techniques to improve detection performance and assesses the algorithms using metrics such as accuracy, precision, recall, and F1 score. Among the tested methods, Random Forest and Decision Trees achieved the highest performance with an accuracy of 97.5%, precision of 1.00, recall of 0.95, and an $F 1$ score of 0.97. SVM demonstrated strong precision at 1.00 but slightly lower accuracy at $\mathbf{9 2. 5 \%}$, while KNN exhibited the lowest accuracy at 85%. These results highlight the effectiveness of Random Forest and Decision Trees for robust and efficient jammer detection. The findings have significant implications for enhancing the resilience of wireless communication systems against jamming attacks. In particular, we show the use-case of our analytical work in the classification-based application of wireless communication systems, specifically jammer detection.

关键词： Wireless communication Support vector machines machine learning algorithms Accuracy Switches Nearest neighbor methods Classification algorithms Decision trees Jamming Random forests

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Optimized Breast Cancer Prediction Using machine learning algorithms

Optimized Breast Cancer Prediction Using Machine Learning Al...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Araoluwa Filani Shalom Arogundade Department Computer Science Joseph Ayo Babalola University Ikeji-Arakeji Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Breast cancer remains a leading cause of cancer-related mortality among women worldwide, underscoring the critical need for early detection and accurate prediction to improve treatment outcomes. This study optimizes breast cancer prediction using various machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector machine (SVM), Decision Trees, and ensemble methods such as Bagging and Boosting. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was employed, which was preprocessed to handle missing values and reduce dimensionality. Results indicate that the Decision Tree classifier achieved the highest accuracy at 98%, followed closely by KNN at 97.86%. KNN’s success can be attributed to its local approach, considering the five nearest neighbors, enabling it to adapt well to dataset variations. SVM, with an accuracy of 96.43%, excels in high-dimensional spaces by constructing optimal hyperplanes. Ensemble methods, particularly Bagging, reached 97.1%, demonstrating potential for stability through aggregation of multiple models. Despite ensemble techniques generally enhancing performance, they did not surpass the top individual classifiers in this analysis. These findings highlight the promise of machine learning in early breast cancer detection and the importance of selecting algorithms suited to specific dataset characteristics. Future research may focus on integrating advanced algorithms and larger datasets to refine predictive accuracy.

关键词： Support vector machines machine learning algorithms Accuracy Nearest neighbor methods Prediction algorithms Boosting Breast cancer Ensemble learning Decision trees Bagging

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Implementation of Artificial Neural Networks and machine learning algorithms to Evaluate and Predict Educational Student Dropout Metrics

Implementation of Artificial Neural Networks and Machine Lea...

引用

Artificial Intelligence and Education (WAIE), International Workshop on

作者： Sanjit Kakarla Daniel Mahany Independent Researcher New Jersey United States

ISBN: (数字)9798350378894

ISBN: (纸本)9798350378900

High school dropout rates are a severe and prevalent issue, deeply concerning the field of education due to their widespread impact and long-term implications. This work aims to employ artificial neural networks and machine learning (ML) algorithms to analyze past dropout rates among different demographics in the United States of America while making future predictions, intending to provide insights that can inform policy-making and resource allocation to proactively address concerns. Analyzing national historical dropout data from 2012 to 2022, various artificial neural network and machine learning techniques such as Multilayer Perceptrons (MLP), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), Neural Networks, and Support Vector Regression (SVR) are employed to determine model performance through various experiments and determine optimal models for future predictions. This work is focused on high school dropout calculations for the nation as a whole as well as specific groups of challenged students. Through this work, we find that the Long Short-Term Memory (LSTM) model has optimal performance and should be leveraged in data-driven aspects of educational decision-making. We also discern that there is still much to do with regard to educational policy on the fronts of assisting students facing disabilities, language barriers, and institutionalization.

关键词： Measurement Support vector machines Analytical models machine learning algorithms Education machine learning Predictive models Data models Vectors Long short term memory

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Solar Panel Soiling Classification Using machine learning algorithms

Solar Panel Soiling Classification Using Machine Learning Al...

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Information Technology (ACIT)

作者： Ala’ Khalifeh Rami Ghanem German Jordanian University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

With the widespread adoption of solar panels as a sustainable and cost-effective source of energy, it becomes paramount to ensure that these panels perform well, especially that they are prone to several factors that can affect their performance. One of these factors is soiling and dusting. This research agrees with other related studies confirming that machine learning methods are useful in PV systems monitoring. This paper aims at researching the application of machine learning algorithms to identify the soiled panels. In particular, the paper investigates the ability of the Long Short-Term Memory (LSTM) and Time Large Language Model (LLM) Transformer machine learning algorithms to detect and classify soiling on photovoltaic panels, thus notifying the responsible person to do the needed cleaning. Both models were trained on the time-series data that were collected by the researchers, including: the temperature, humidity, and irradiance. The models’ performances were ana-lyzed with the help of evaluation metrics. The highest accuracy of the LSTM model in the identifications of soiling patterns was up to 96.11%, whereas in the Time LLM Transformer model was 93.6%.

关键词： Measurement Temperature distribution machine learning algorithms Large language models machine learning Transformers Solar panels Information technology Long short term memory Monitoring

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Compression technique by Analyzing Peak Signal to Noise Ratio value using various machine learning algorithms

Compression technique by Analyzing Peak Signal to Noise Rati...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： M.S Saravanan Faiyaz Ahmad R.T. Thivya Lakshmi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Image compression method classifies the acquisition image and converts to grayscale image ($\text{P}=0.000$) using Support Vector machine (SVM) compared with Decision Tree method. Peak Signal to Noise Ratio (PSNR) are performed with the acquisition image. 1000 sample images are taken as input. The image compression of the acquisition image is carried out using the Support Vector machine algorithm where the number of samples $(\text{N}=40)$ and the sample size is divided into two ($\mathbf{N}=\mathbf{2 0}$) for SVM and $(\mathbf{N}=20)$ for Decision tree. The peak to signal noise ratio of SVM with 92.20 kbps is better than the Decision tree 84.80 kbps. There is a significant difference in PSNR is $\text{P}=0.293$ within the limit of study, the Support Vector machine algorithm performs better at finding the Peak to signal noise ratio when compared with the Decision Tree.

关键词： Support vector machines Graphics Analytical models Image coding PSNR machine learning algorithms Noise Gray-scale Data transfer Decision trees

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Leveraging machine learning algorithms for Enhanced Drug Bioactivity Prediction in Pharmaceutical Discovery

Leveraging Machine Learning Algorithms for Enhanced Drug Bio...

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Information Technology (OCIT), OITS International Conference on

作者： Charvi Suri Abhijeet Raipurkar Rashmi Kanharkar Komal Nichat Vrushali Dhondse Pragati Pawar School of Computer Science and Engineering Ramdeobaba University Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

In the realm of drug discovery, accurately predicting compound bioactivity is pivotal for developing effective therapeutic interventions. This research presents a comprehensive approach utilizing machine learning (ML) models to enhance bioactivity prediction. The primary objective is to develop models capable of predicting compound bioactivity based on pIC50 values, leveraging structural and activity data alongside textual chemical information. The proposed methodology involves understanding bioactivity mechanisms, bioactivity screening, SAR analysis, and model development using Python libraries such as TensorFlow, PyTorch, and Scikit-learn. Ensemble techniques are explored to combine the strengths of ML approaches. It therefore brings a team of both scientists and engineers together, working around the core values of scalability, interpretability, robustness, and usability of models in order to accelerate drug discovery, enabling an enhancement of the development of therapeutic interventions that are safer and more effective. Through rigorous validation and evaluation, the research aims to provide insights into compound bioactivity factors and facilitate their application in real-world drug discovery pipelines.

关键词： machine learning algorithms Biological system modeling Scalability Pipelines machine learning Predictive models Robustness Drug discovery Compounds Usability

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An Affective Computing Electroencephalogram-based System with machine learning algorithms

An Affective Computing Electroencephalogram-based System wit...

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International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Israa Laith Salim Osama Ali Awad Ali Sadeq Abdulhadi Department of Information and Communication Engineering College of IE Al-Nahrain University Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University Babylon Iraq

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

Affective Computing plays a crucial role in the Human-Computer Interaction (HCI) field by enabling computers to recognize, interpret, process, and simulate emotions evoked by positive/negative events, objects, or situations. It presents new avenues for applications ranging from personalized user interfaces to mental health monitoring. In this study, an affective computing system for human emotion recognition is developed by measuring the electrical activity of brain, Electroencephalogram (EEG) signals for 15 participants were recorded. Using machine learning (ML) algorithms to classify emotional states into three emotion dimensions: valence, arousal, and dominance. Two ML algorithms, Naïve Bayes (NB) and Artificial Neural Network (ANN) are exploited. The system also validated with EEG data from the public DREAMER dataset and compared with recorded data. Results demonstrate the feasibility and effectiveness of the proposed EEG-based affective computing system in accurately identifying and categorizing emotions, leveraging the designed ANN classifier; a maximum accuracy of 95.61% is achieved.

关键词： Computers Affective computing Emotion recognition machine learning algorithms Accuracy Anxiety disorders Artificial neural networks Electroencephalography Classification algorithms Reliability

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A Technical Analysis and Practical Implementation of machine learning algorithms for Predicting Survival in Breast Cancer Patients

A Technical Analysis and Practical Implementation of Machine...

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International Conference on Computer Science and Engineering (UBMK)

作者： Shamim Yousefi Samad Najjar-Ghabel Hamidreza Shafaei Department of Computer Engineering University of Mohaghegh Ardabili Ardabil Iran

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

Breast cancer is one of the most significant health challenges worldwide, with millions of women diagnosed annually and survival rates heavily dependent on early detection and effective treatment. The complexity of breast cancer, with its various subtypes and clinical characteristics, makes accurate survival prediction a critical yet challenging task. machine learning algorithms have emerged as potent tools in predicting survival outcomes by analyzing extensive patient data and identifying complex patterns that may be overlooked by traditional clinical methods. While numerous studies have explored machine learning approaches for survival prediction in breast cancer patients, many lack comprehensive comparisons of different algorithms on specific datasets and practical implementation insights. This paper aims to advance the field by comparing the performance of seven prominent machine learning classification algorithms: Naive Bayes (NB), k-Nearest Neighbors (kNN), Decision Tree (DT), Random Forest (RF), Support Vector machine (SVM), Logistic Regression (LR), and Artificial Neural Network (ANN). Utilizing the SEER (Surveillance, Epidemiology, and End Results) dataset from the National Cancer Institute, these classifiers are evaluated based on accuracy, precision, recall, and F-Score to determine their effectiveness in predicting patient survival. Among these algorithms, Random Forest shows superior performance, with an accuracy of 91.5%, precision of 92.80/0, recall of 55.7 % , and an F-Score of 69.6%, highlighting its robustness and efficacy in survival prediction tasks. While other algorithms also demonstrate potential, particularly under certain conditions, further research is needed to explore the benefits of combining multiple algorithms and incorporating additional features to enhance survival prediction models for breast cancer patients in future studies.

关键词： Support vector machines machine learning algorithms Accuracy Surveillance Artificial neural networks Prediction algorithms Breast cancer Robustness Classification algorithms Random forests

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Intelligent Framework to Detection of Fake Review Using machine learning algorithms

Intelligent Framework to Detection of Fake Review Using Mach...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Neha Janu Anjali Department of Computer Science & Engineering Manipal University Jaipur Jaipur Rajasthan Department of Computer Science & Engineering Poornima College of Engineering Jaipur Rajasthan

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

Online reviews are becoming a more important resource for consumers as they make purchasing decisions. Recent years have seen a rise in the importance of the study fields of social media, hotel reviews, and e-commerce websites. People's decisions are influenced by online reviews. The bogus review has an impact on both consumers and commercial organizations in day-to-day existence. Inflated ratings and fake reviews paint a false picture of a product or service and are intended to mislead consumers away from or towards that product or service. In this research paper, machine learning algorithms are used for detecting fake reviews. The prevalence of fake reviews on online platforms has become a major concern, making it difficult for customers to trust the reviews they read. In this study, we used a dataset of Yelp Chicago restaurants' online reviews and applied various techniques of machine learning to identify phony reviews. The algorithms used included Naive Bayes, Random Forest, Linear SVM, and NN. The performance was evaluated for the models based on F1-score, recall, precision, and accuracy. Our results show that fake reviews can be detected effectively with high accuracy with the help of machine learning algorithms. This study provides a useful framework for identifying fake reviews on online platforms and can help improve the credibility and trustworthiness of online reviews.

关键词： Support vector machines machine learning algorithms Accuracy Reviews Social networking (online) Organizations Bayes methods Microelectronics Fake news Random forests

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A Comparative Analysis of machine learning algorithms for Crop Recommendation

A Comparative Analysis of Machine Learning Algorithms for Cr...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Chandrika M Samitha Khaiyum Vinay Krishna Bhat Raksha Kodnad R Department of MCA Dayanada Sagar College of Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

This paper looks at how crop recommendation and yield prediction analysis, with the help of sophisticated machine learning, help enhance farming productivity. The choices of crops and the best time for applying fertilizers are determined by using LightGBM, decision trees, SVM, logistic regression, and random forest algorithms based on soil and climate data. Upon comparing these algorithms, LightGBM and Random Forest are identified as the best algorithms. Intelligent decision support systems are imperative to agriculture's importance in India's economy. These systems use machine learning to give the best results in terms of yields, resources used, and vulnerability to market and climatic changes. In addition, taking into account the forecasts of an increase in the world population to 9.7 billion by 2050, the pressure to generate more food in a sustainable manner is inevitable. This has underlined the need and importance of adopting such intelligent technologies in improving agriculture to feed the increasing population with regard to food and conservation of the environment.

关键词： Support vector machines Productivity Logistic regression machine learning algorithms Crops Soil Prediction algorithms Agriculture Random forests Meteorology

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