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检索条件"主题词=Machine Learning Algorithms"

共 25882 条记录，以下是971-980 订阅

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Android App Malware Detection using Stacking of machine learning algorithms

Android App Malware Detection using Stacking of Machine Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jaikishan Mohanty Divyashikha Sethia Department of Software Engineering Delhi Technological University Delhi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The frequency of malware attacks on mobile devices has increased over time. Malicious apps that appear innocent are among the most popular ways to get malware into a phone system. Researchers have previously identified malware on phones using machine learning, and deep learning and supervised learning algorithms have made this possible. This paper proposes a novel GARB Model, which is an ensemble approach of combining different base classifier algorithms (Gradient Boosting, AdaBoost, Random Forest & Bagging Classifier with Decision Tree) using a weighted averaging method of stacking to determine whether the Android package is malware or Benign and the GARB model achieved the highest accuracy of 82.38% using Ensemble Prediction. Gradient Boosting Classifier followed with 82.01%, and Stacking with Logistic Regression with 81.87%, respectively. The weighted averaging classifier performed superiorly in ensemble prediction, even in most other criteria. Therefore, it is a reliable and effective approach for prediction tasks.

关键词： Time-frequency analysis machine learning algorithms Stacking Predictive models Boosting Malware Classification algorithms Reliability Smart phones Random forests

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Advanced Flood Prediction at Forest with Rainfall Data Using Various machine learning algorithms

Advanced Flood Prediction at Forest with Rainfall Data Using...

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Delhi Section Conference (DELCON), IEEE

作者： Saravanan M. S Sivashankar S A. Rajesh Masrullizam Mat Ibrahim Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India GLA University Mathura India Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka (UTeM) Melaka Malaysia

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The aim is to classify and predict floods in advance with rain data patterns of India using spatio-temporal logic. Two Classification algorithms are used to achieve the maximum accuracy namely K-Nearest Neighbour with a sample size=5 and Logistic Regression with a sample size=5 for continues iterations. The work focused towards comparison of K-Nearest Neighbour and logistic regression, which has confidential and forecast the standards from the rainfall statistics to produce estimated accuracy with K-nearest neighbour has higher accuracy by comparing with Logistic Regression accuracy. It has a high accuracy of 50.35%, in comparison with the Logistic Regression algorithm 45.96%. The significant values have been statistically defined with the value of (p< 0.001). Prediction in flood patterns, K-Nearest Neighbour consisting rainfall pattern expressively used to produce improved accuracy than the Logistic Regression.

关键词： Logistic regression Accuracy Rain machine learning algorithms machine learning Nearest neighbor methods Prediction algorithms Floods Logic Standards

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Review of machine learning algorithms for Predictive Maintenance in Industrial Applications

Review of Machine Learning Algorithms for Predictive Mainten...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Xiaohua Wu Qingfei He Zhaoyang Zuo School of Mechanical Engineering Xijing University Xi’an Shaanxi China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Predictive maintenance (PdM) has gained increasing importance in modern industries due to its ability to anticipate equipment failures and extend asset life. By leveraging machine learning (ML) techniques, PdM systems can analyze large volumes of operational data, identify early signs of wear, and predict remaining useful life (RUL) more accurately than traditional methods. This review delves into the application of various ML algorithms in PdM, including artificial neural networks, support vector machines, clustering algorithms, and random forests. We discuss how these algorithms enhance the ability to detect anomalies, optimize maintenance schedules, and reduce unnecessary maintenance costs. Additionally, we evaluate the challenges associated with data quality, feature selection, and model interpretability in real-world PdM implementations. The paper also provides insights into future trends, such as the integration of deep learning and IoT technologies to enable real-time monitoring and dynamic maintenance strategies that can adapt to complex industrial environments. Overall, this review underscores the transformative potential of ML-driven predictive maintenance in achieving more efficient, cost-effective, and reliable industrial operations.

关键词： Deep learning machine learning algorithms Costs Reviews Data integrity Clustering algorithms Prediction algorithms Data models Maintenance Predictive maintenance

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Wi-Fi Network Intrusion Detection: Enhanced with Feature Extraction and machine learning algorithms

Wi-Fi Network Intrusion Detection: Enhanced with Feature Ext...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Şevval Şolpan Hakan Gündüz Kerem Küçük Software Engineering Kocaeli University Kocaeli Turkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Wireless Fidelity (Wi-Fi) is one of the most popular technologies for Internet connection. It helps users to connect to other devices by simply typing the correct password. It contributes to a great extent to building environments powered by IoT in schools, offices, homes, and cities. However, it has vulnerabilities, causing others to suffer low quality service or personal information theft when misused. Misusing is possible in several ways, and it is often caused by a network attack. To maintain the security of a network, network intrusion detection systems are important. For this reason, this study focused on network intrusion detection and used publicly available Aegean Wi-Fi Intrusion Dataset 3 (AWID3). We have used K-Nearest Neighbors, Support Vector machine, and Gradient Boosting Trees algorithms for attack detection. In addition, we used time-series data and extracted features using two methods: Convolutional Neural Network (CNN) and statistical measures, to observe the effect on learning and facilitate the delivery of time-series data to machine learning algorithms. To the best of our knowledge, we are the first to conduct experiments on the AWID3 dataset using a combination of machine learning algorithms, feature extraction methods, and time-series data for network intrusion detection. We conducted six experiments in total, and the results of our best-performed experiments are as follows: accuracy: 0.978, precision: 0.978, recall: 0.978, F1 score: 0.978.

关键词： Deep learning Support vector machines machine learning algorithms Urban areas Time series analysis Network intrusion detection Telecommunication traffic Feature extraction Convolutional neural networks Wireless fidelity

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Comparative Analysis of machine learning algorithms for Predicting Student Academic Performance in Higher Education

Comparative Analysis of Machine Learning Algorithms for Pred...

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

作者： Michael Marvin P. Cruz Ramil G. Lumauag College of Computer Studies Central Philippines State University San Carlos Campus Negros Occidental Philippines Computer Department Iloilo Science and Technology University Iloilo City Philippines

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In recent years, educational institutions have increasingly sought to leverage data-driven approaches to enhance student success and retention. While numerous machine learning algorithms have been created to forecast academic performance, their efficacy differs among datasets, domains, and institutional contexts. The optimal algorithm for particular settings in higher education remains uncertain. This study aims to compare multiple algorithms such as Random Forest, Support Vector machine, Decision Tree, Logistic Regression, Naive Bayes, and K-Nearest Neighbors to identify the most effective model for predicting student academic performance in higher education institutions (HEIs). This paper also introduces the application of SMOTE, which focuses on addressing class imbalance. Evaluation of multiple models comparing their performance across metrics such as accuracy, precision, recall, and F1-Score was present in this paper. The results of the comparative analysis showed that Random Forest outperforms other algorithms, particularly when SMOTE is applied, with an accuracy of 97%, demonstrating its robustness and adaptability in handling imbalanced datasets. This paper underscores the capacity of machine learning to furnish educational stakeholders with novel instruments for predicting student outcomes, facilitating prompt interventions to enhance academic performance.

关键词： Support vector machines machine learning algorithms Accuracy Education Predictive models Prediction algorithms Ubiquitous computing Robustness Stakeholders Random forests

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Comparative Study of machine learning algorithms for Student Retention, early warning and intervention systems for Institutions of Higher learning

Comparative Study of Machine Learning Algorithms for Student...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Milka C.I Madahana John E.D. Ekoru School of Mining Engineering University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg South Africa School of Electrical & Information Engineering University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg South Africa

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

The main purpose of this research work is to conduct a comparative study of application of machine learning algorithms in the development of a student retention model for undergraduate students at an institution of higher learning. The Decision Tree, Random Forest, K-Nearest Neighbor, Gradient Boosting Trees, Logistic Regression, Stochastic Gradient Descent, Linear Support Vector Classification and Gaussian Naïve Bayes are used in the development of the Undergraduate student retention model. The implementation results show that The Decision Tree, Random Forest, K-Nearest Neighbor, Gradient Boosting Trees and Logistic Regression achieved an accuracy of ${9 8 . 5 9} \%, {9 4 . 1 1} \%, {8 9 . 5 5} \%$ and ${8 9 . 4 2} \%$ respectively. To improve this work in future it is recommended that the current machine learning algorithms could be amalgamated with Artificial Neural Networks or deep learning algorithms for improved performance. Good features identified in traditional models used for development of student retention models can also be integrated with machine learning models for improved performance.

关键词： Logistic regression machine learning algorithms Stochastic processes Forestry Nearest neighbor methods Boosting Mobile communication Vectors Decision trees Random forests

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Application of machine learning algorithms on Temporal Data for Vegetation Indices in Crop Classification

Application of Machine Learning Algorithms on Temporal Data ...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Vineet Saxena Rakesh Kumar Dwivedi Anil Kumar College of Computing Sciences and Information Technology Teerthanker Mahaveer University India Indian Institute of Remote Sensing Indian Space Research Organisation India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This paper therefore aims at examining the ability of machine learning models on the temporal satellite data for crop classification with the insert of the spectral indices; NDVI, EVI and SAVI. Accurate crop identification was achieved by assessing three models; namely Random Forest, Support Vector machine (SVM) and Long Short Term Memory (LSTM) networks developed using multi-temporal satellite images. The experiment reveals that the LSTM model, which captures temporal behavior of the input data, yielded the highest classification score, 91%, compared to both Random Forest and the SVM model. The results show that the temporal data are useful in differentiation between crops with similar spectra but different phenology. The results of this research confirm that timely data coming together with state-of-the-art machine learning algorithms can further enhance the accuracy of crop classification, thus helping to enhance the efficiency of precision agriculture. The work to be done in the future will be to include other sources of climate data such as meteor and soil to improve model stability and performance.

关键词： Support vector machines Accuracy machine learning algorithms Computational modeling Crops Vegetation mapping Soil Data models Long short term memory Random forests

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Chronic kidney disease prediction: Optimization of machine learning algorithms

Chronic kidney disease prediction: Optimization of machine l...

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Narinder Kumar Sanjay Singla Department of CSE Chandigarh University

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

A wide range of variables, such as more drinking, a growing worldwide pollution trouble caused on by rapid industrialization and warming temperatures, release of toxic gases from polluted water and food, misuse of drugs, and, most importantly, bad habits, are expected to make kidney failure worse. It is one of the deadliest illnesses affecting the world’s medical care systems. These factors ultimately lead to a constant rise in the number of kidney anomalies that patients are diagnosed with. Building classification and prediction models for early liver disease diagnosis involves examining the patient’s renal datasets. The disease is predicted using machine learning in an attempt to lighten the burden on physicians. This research investigates many past machine-learning algorithms for the diagnosis and categorization of renal disorders. A comparative examination of more than six models suggests the best course of action for the target dataset. For offline challenges, these are the greatest options for improving the accuracy of the model. While there is evidence to show that different ensemble procedures and hyper parameter tuning can lead to improved accuracy, these methods significantly raise computing productivity costs, rendering them impractical for real-world applications.

关键词： Temperature distribution machine learning algorithms Accuracy Computational modeling Medical services Rendering (computer graphics) Water pollution

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Improving Manufacturing Testing Steps Using Artificial Intelligence and machine learning algorithms Trained on Simulated Waveforms

Improving Manufacturing Testing Steps Using Artificial Intel...

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IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR

作者： Olariu Eliza Maria Hedesiu Horia Electrical Engineering Technical University of Cluj Napoca Cluj Napoca Romania

ISBN: (数字)9798350361933

ISBN: (纸本)9798350361940

In the present era, Artificial Intelligence and machine learning are helpful tools for solving any challenges we encounter in both our personal and professional life. Artificial Intelligence and machine learning have made it possible to find solutions for a wide range of global problems. In this study, a special technology is presented, which allows manufacturing to obtain more accurate product testing results. A simple simulation of waveform signal data production is used in order to illustrate how to enhance decisions performed in different testing stages of the manufacturing process. Once data generation started, a failure prediction model can be constructed through the implementation of Artificial Intelligence and machine learning techniques. Three models have been used: Random Forest Classifier, Decision Tree Classifier, and Support Vector Classification (SVC) and those respective results are compared. This approach is suitable for any industrial context and is readily tailored to a real-world use case.

关键词： machine learning algorithms Manufacturing processes Service robots Static VAr compensators Production Predictive models Vectors

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Deep Neural Networks based Handwritten Signature Verification using machine learning algorithms

Deep Neural Networks based Handwritten Signature Verificatio...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： G. Manoj Kumar P. Satyanarayana B. Sridhar K. Tejo Rohith G. V. S. Padma Rao V. Gokula Krishnan Department of ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada AP India Department of ECE S.R.K.R. Engineering College Bhimavaram A.P India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Signatures are unique forms of identity that are used especially in the authentication of documents. Although, there are ways to increase accuracy in verification including fingerprint scanning, retina scanning, face and voice recognition, it comes with the cost of extra hardware. One of the major difficulties in signature verification is the within class variability of offline signature where even the trained signers are unable to reproduce the same signatures repeatedly. Such fluctuations complicate distinguishing real signatures from fakes with the help of traditional methods of checking the authenticity of signatures. To overcome this problem, methods like machine learning algorithms and deep neural networks are used. Multilayer perceptrons and convolutional neural networks (CNNs) may provide effective solutions for the image classification problem inherent in signature verification. The goal of this research isto design a dependable technology to detect the authenticity of signatures. CNNs and binary classifiers were applied to preprocessed signature images which include genuine and fake signatures. All the proposed works have been implemented and tested on different datasets while varying the signature type and readability. Our implementation included the use of CNN to classify these datasets and generate useful results. The performance of the proposed method was evaluated using datasets from the GPDS Synthetic Signature Database and presented. The proposed research shows how deep learning networks can perform well in identifying forged signatures thereby playing a critical role in the fight against signature forgery.

关键词： Deep learning machine learning algorithms Accuracy Speech recognition Multilayer perceptrons Feature extraction Retina Robustness Convolutional neural networks Image classification

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