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检索条件"主题词=Machine Learning Algorithms"

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Information and Communication Technology (ICICT), International Conference on

作者： Tayaba Khondoker Tinny Abdul Halim Bhuiyan Mohammed Forhad Uddin Department of Mathematics Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

machine learning(ML)-based feature selection algorithms use specific techniques to assess feature relevance. In medical datasets, many attributes can be irrelevant for classification. Many features in these datasets are often irrelevant for classification tasks, and removing them can improve efficiency without sacrificing accuracy. The study uses techniques like Random Forest and Permutation Feature Importance to determine significant features from datasets provided by the University of California, Irvine (UCI). To assess feature relevance, statistical tests such as the Kolmogorov-Smirnov statistic and t-test are employed, along with the Mahalanobis Distance Measure to filter important features. The research highlights the value of feature selection in improving model performance. The dataset used demonstrated a 96% accuracy, 97% F1 score, and 98% AUC in 5-fold cross-validation with full features. When reduced to 50% of the most relevant features, the performance improved with an AUC of 99%, indicating better results with fewer features. The findings suggest that statistical similarity metrics like the Mahalanobis Distance Measure can be effective for feature selection, improving model efficiency and accuracy.

关键词： Deep learning Measurement Radio frequency Accuracy machine learning algorithms Data analysis Feature extraction Classification algorithms Information and communication technology Random forests

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A machine learning Based Approach to Classify Tense from English Text

A Machine Learning Based Approach to Classify Tense from Eng...

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

作者： Umme Ayman Md. Shafiqul Islam Md. Azmain Mahtab Rahat Dewan Mamun Raza Narayan Ranjan Chakraborty Md. Hasan Imam Bijoy Department of Computer Science and Engineering Daffodil International University Dhaka

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

This paper investigates the classification of tense in English text using machine learning algorithms. Support Vector machine (SVM), Random Forest (RF), Multinomial Naive Bayes (MNB), Decision Tree (DT), XGBoost, and K-Nearest Neighbors (KNN) are the six classifiers used in the study. The dataset was collected from diverse sources including novels, books, blogs, articles, social media platforms, newspapers, websites and some of them self-made. The data underwent preprocessing steps such as cleaning, normalization, and feature extraction using TfidfVectorizer. Among the other algorithms, SVM achieved the highest accuracy at 97.17%. Classifier performance was assessed with metrics such as F1-score, recall, accuracy, and precision. To evaluate performance, ROC curves, and confusion matrices were also examined. The study underlines the necessity for focused approaches and draws attention to the significant gaps in the field of natural language processing (NLP) regarding tense classification studies. By leveraging machine learning, this research aims to enhance the accuracy and contextual appropriateness of tense classification, thereby improving cross-cultural communication and understanding in machine translation systems. This research contributes to NLP by offering a robust approach to tense classification and demonstrates the potential of SVM in achieving high accuracy for this task. Future work will focus on addressing limitations such as short training data, overfitting and tense conversion.

关键词： Support vector machines Measurement Accuracy machine learning algorithms Social networking (online) Training data Nearest neighbor methods machine translation Random forests Overfitting

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Device Threat Identification and Risk Assessment using Feature Analysis in machine learning

Device Threat Identification and Risk Assessment using Featu...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Krish Panchal Mihir Desai Tejas Itankar Ramchandra Mangrulkar Information Technology Department Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In response to the growing threat of malware in our digitally linked society, this research paper introduces a ground-breaking Malware Prediction System (MPS). Our system seeks to foresee and proactively detect malware in real-time using cutting-edge machine learning algorithms, serving as a critical defence mechanism against shifting cyber threats. The procedure comprises creating a user-friendly website that is accessible to both individuals and businesses. Users can rapidly upload files from their Windows computers, kicking off the feature analysis process. Based on the unique attributes acquired from the given files, the study estimates the likelihood of a malware attack using machine learning methods. Through a simple and intuitive interface, the system offers a smooth experience, allowing users to execute scans and obtain quick, actionable findings. Furthermore, the aim of the paper is to bring users with a tool that not only improves security but also contributes to cybersecurity awareness. By making the prediction process accessible and user-friendly, we aim to foster a proactive approach to malware prevention. The integration of Microsoft’s extensive dataset ensures the model’s robustness and adaptability to diverse cyber threats, making the MPS a valuable asset in the ongoing battle against malware.

关键词： machine learning algorithms Prevention and mitigation Refining machine learning Feature extraction Malware Robustness Real-time systems Object recognition Computer security

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machine learning in Wireless Link Estimation: A Comparative Study of Supervised, Unsupervised, and Deep learning Approaches

Machine Learning in Wireless Link Estimation: A Comparative ...

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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Hoang-Long Nguyen Ngoc-Nhat Huynh Huy-Tan Thai Khanh-Hoi Le-Minh University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

The proliferation of Internet of Things devices demands reliable wireless connections against negative environmental factors and signal interference. In this paper, we present a comprehensive comparative analysis of about 17 machine learning algorithms with different settings, including unsupervised, supervised, and deep learning models, for wireless link estimation. We evaluate the accuracy and adaptability of these algorithms under various connection conditions in two datasets: the publicly available dataset from Colorado and a custom dataset collected using Raspberry Pi 4 devices. The key finding reveals that while deep learning models achieve superior performance, they are more prone to overfitting than traditional machine learning approaches. Notably, unsupervised models struggle to find meaningful cluster structures in complex datasets but achieve high accuracy on simpler ones. Based on this finding, researchers in this field can have insights into the strengths and limitations of machine learning approaches, offering a practical foundation for developing more effective and adaptable algorithms for wireless link estimation in diverse IoT environments.

关键词： Wireless communication Deep learning Performance evaluation Adaptation models machine learning algorithms Accuracy Clustering algorithms Estimation Approximation algorithms Classification algorithms

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Using machine learning to Support Prostate Cancer Detection Without a Dedicated Biomarker

Using Machine Learning to Support Prostate Cancer Detection ...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hadrien Salem Sarah Ben Othman Marc Broucqsault Slim Hammadi CRIStAL CNRS UMR 9189 Nord France Altao Lille Nord France

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Early disease detection through the analysis of laboratory tests is critical for the successful treatment of certain diseases. However, current medical knowledge relies on the use of certain biomarkers in order to diagnose associated diseases. This means that some diseases cannot be detected unless the corresponding biomarker is actually *** this paper, we will explore the possibility of using machine learning to detect abnormal values in biomarkers, even when they are not directly measured. This stems from the fact that, as an ensemble of features, even a set of common biomarkers contains sufficient information to suspect anomalies in a patient’s *** present a complete pipeline for processing medical laboratory data, taking into account the many challenges offered by this use case, such as data cleanliness, the abundance of missing values, and strong class imbalance. Using this framework, we perform a case study on prostate cancer and its associated biomarker, Prostate-Specific Antigen (PSA). Our implementation, based on an imbalance-aware ensemble prediction model, is tested on a real laboratory dataset of more than 8 million records. We find that basing the ensemble on Random Forest models provides the best results, with a ROC-AUC of 75.86%, indicating that it is indeed possible to detect abnormal PSA values without measuring it directly. We also show that a simple baseline of 24 commonly-measured biomarkers provides near-equivalent results to a full set of features. Our work offers new perspectives for routine disease screening.

关键词： Antigens machine learning algorithms Systematics Sensitivity Biological system modeling Pipelines Biomarkers Prostate cancer Medical diagnostic imaging Diseases

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Fraud Detection System for Financial System Using machine learning Techniques: A Review

Fraud Detection System for Financial System Using Machine Le...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Priya Kumari Sonu Mittal Amity institute of information and technology Noida Uttar Pradesh

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

Using machine learning approaches, this study presents a comparative investigation of fraud detection in financial systems. The study examines a number of databases, machine learning algorithms, and pre-processing methods that are employed in fraud detection. A variety of preprocessing techniques, feature extraction, and ML and DL approaches—including logistic regression, decision trees, random forests, K-NN, CatBoost, SVM, isolation forests, CNN, and RNN—as well as exploratory data analysis techniques are included in the literature review. The difficulties with feature engineering, class disparity, and machine learning technologies are all covered in the article. The UCI machine learning Repository, GitHub, Kaggle, and other publications and datasets are among the sources of information that the study uses to presentand analyse its results. The data sets include a variety of fraud situations such as application fraud, data phishing, online and offline fraud, credit card theft, and data theft. The study comes to the conclusion that machine learning algorithms offer a viable way to identify fraud, and that machine learning models improve over time and become more skilled at identifying fraud as new patterns appear.

关键词： Support vector machines machine learning algorithms Reviews Phishing Market research Fraud Reliability

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Application of machine learning Techniques for Heavy Rainfall Prediction using Satellite Data

Application of Machine Learning Techniques for Heavy Rainfal...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Gujjari Balram N Poornachandrarao D. Ganesh Banavath Nagesh Reddy.A Basi M.Sunil Kumar Anurag University Venkatapur Ghatkesar Rd Hyderabad Telangana Geethanjali College of Engineering and Technology cheeryal Hyderabad Telangana India (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Mohan Babu University Tirupati Andhra Pradesh India Department of Cyber Security Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This study presents a large-scale lunar dataset with the goal of utilizing machine learning techniques to forecast periods of intense rain. The dataset includes features from the lunar surface such as slope, height and depth of craters, types of boulders, patterns of distribution, and the presence of shadows. The study uses well-known machine learning techniques Random Forest, AdaBoost, Support Vector machine (SVM), and k-Nearest Neighbors (KNN), with an emphasis on the Heavy Rain Prediction target variable, which indicates the chance of heavy rain events ($\mathrm{Yes} / \mathrm{No}$). In order to provide insights into efficient heavy rain event prediction on the moon’s surface, the algorithms are trained and assessed to identify patterns and linkages within the lunar dataset. The results further lunar exploration plans and provide useful recommendations for planning lunar missions and safety precautions against potentially unfavorable weather circumstances.

关键词： Support vector machines Rain machine learning algorithms Moon Nearest neighbor methods Prediction algorithms Safety Planning Random forests Meteorology

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Using machine learning to Predict the Language Proficiency of Thai Students Studying in Taiwan

Using Machine Learning to Predict the Language Proficiency o...

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International Conference on Applied System Innovation (ICASI)

作者： Charoenwat Moocharoen Wen-Chih Chang International Master Program in Information Technology and Applications National Pingtung University Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

Currently, there are students from Thailand who wish to study abroad. The researchers have chosen Taiwan as the focus. Among the students who come for further studies, there are those proficient in the English language and those who are not. Factors such as language readiness, financial preparedness, scholarship availability, and the university's reputation are crucial in the decision-making process for choosing an educational institution. Simultaneously, some students had the opportunity to study in Taiwan but, due to language unpreparedness, had to withdraw from their studies and return to their home country. The researchers emphasize the importance of language readiness among students. Data has been collected from 316 data including undergraduate, master's, doctoral, and language students of Thai students throughout Taiwan, and a model has been developed to predict the English language proficiency of Thai students. The goal is to create a model with the highest level of accuracy to produce reliable results. This model can be utilized by educational institutions to tailor their courses to match the language abilities of Thai students, thereby enhancing the overall learning experience.

关键词： Logistic regression Technological innovation machine learning algorithms Scholarships Neural networks Forestry Medical services

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Poster: Android Malware Detection using Hybrid Features and machine learning

Poster: Android Malware Detection using Hybrid Features and ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Abdul Kadir Sateesh K. Peddoju Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

The proliferation of the Android platform has in-creased malware development. New or zero-day attacks easily bypass traditional anti-malware techniques relying on signature-based detection. Recent machine learning (ML) models for An-droid malware detection struggle with obfuscated malware and high false-positive rates. This paper proposes a new technique for detecting Android malware using static hybrid features and ML algorithms. The proposed model enhances training efficiency and generalization by removing weak features during the feature selection. We use various ML algorithms like Logistic Regression (LR) and Random Forest (RF) to train the detection model on the OmniDroid dataset, which includes 22,000 malware and benign samples. Experimental results show that our model achieved 90% accuracy and 91% precision.

关键词： Training Radio frequency Logistic regression machine learning algorithms Accuracy Operating systems Feature extraction Smart systems Malware Random forests

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machine learning Based Electrical Load Prediction Using Regression Learners: A Performance Evaluation

Machine Learning Based Electrical Load Prediction Using Regr...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Sushama D. Wankhade Babasaheb R. Patil Department of Electrical Engineering Sardar Patel College of Engineering Andheri India Vishwaniketan's Institute of Management Entrepreneurship and Engineering Technology Kumbhivali Maharshtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The primary intent of the present research was to design and execute an electrical load forecasting system using machine learning techniques. Implementing an advanced predictive method, specifically a machine learning algorithm, helped in accurate load forecasting which is crucial for efficient power grid management, avoiding blackouts, and optimizing resource allocation. Electricity load fluctuates due to complex factors like weather, holidays, and human behavior, making traditional forecasting methods struggle. This is where machine learning (ML) shines. ML algorithms can learn from historical data, identifying complex patterns and relationships that influence electricity demand. This allows them to make more accurate predictions than static models. In this work, regression learning models in machine learning are used with the MATLAB platform. Three years of real-time data from the Wavi substation in India is used in the work. Considering day, date, hour of day max & min temperature of the day, and input voltage and current are taken as input parameters to test fourteen different models of assorted regression algorithms, namely, linear regression, SVM, decision tree model, and neural networks. The performance of these models is evaluated using commonly used metrics, RMSE, MSE & MAE along with a few other parameters. An optimized trained model is then tested with real data to obtain a forecasted load for one week. The correlation between the Actual load and forecasted load is found to be 0.999962 showing strong relation between actual and forecasted values.

关键词： Support vector machines Adaptation models machine learning algorithms Load forecasting Biological system modeling Neural networks Weather forecasting machine learning Predictive models Load modeling

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