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共 563 条记录，以下是131-140 订阅

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PhishingWebsite URL's Detection Using machine learning

PhishingWebsite URL's Detection Using Machine Learning

引用

computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Bayan Aljunidi Shahad Albshayer Afnan Almutairi Nawal Alenezi College of Computer Science and Engineering University of Hail. University of Ha'il Ha'il KSA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Phishing attacks pose a significant threat to online security, tricking users into divulging sensitive information by mimicking legitimate websites. this paper addresses the critical problem of detecting phishing URLs, which are often the frontline of phishing campaigns. Traditional security measures struggle to keep pace with the sophistication and volume of phishing attacks, necessitating more proactive and dynamic solutions. To combat this issue, we propose a machine learning-based approach that leverages Logistic Regression (LR), Naive Bayes (NB), Decision Trees, KNN, Random Forest and Linear Support Vector Classifier (LSVC) models to enhance the accuracy of phishing URL detection. the results of our research indicate enhanced accuracy of the models in identifying phishing URLs.

关键词： Uniform resource locators Accuracy Phishing Volume measurement Nearest neighbor methods Market research Vectors Numerical models Random forests Resilience

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Early Detection: Deep learning-Driven ECG Image Analysis Forcardiovascular Screening

Early Detection: Deep Learning-Driven ECG Image Analysis For...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： J. R. V. Jeny K. Nikhil Goud Divya Sai Leela Amrutha Kishore Azmira Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

the heart is a very important organ in the circulatory system. Heart disease is extremely severe and can even lead to death, therefore it is essential to make an accurate and timely diagnosis. However, health data analysis may be complicated because there may not be any evident patterns. Cardiovascular diseases (CVDs) are the number one cause of death throughout the world. Early detection of them is vital for successful management. For this purpose, electrocardiograms (ECGs) are used which indicate electrical activities in our hearts; however, computers and machine learning systems are ways by which we could analyze patients’ data more swiftly. It concentrates on using deep as well as machine learning techniques in assisting diagnosing diseases. the purpose was to evaluate how well deep learning could identify four significant heart conditions: arrhythmia, myocardial infarction or heart attacks, history of myocardial infractions (past heart attacks), and normal patterns. We utilized a real-world ECG dataset reflecting actual scenarios and complexities. the Proposed model performed even better than the existing ones with an accuracy of 98.23% implying that it could correctly identify heart issues in ECG images 98.23% times out of 100%. Also recall, precision and F1 score were very high which are other measures about how good a model is performing. By using machine learning & Deep learning Algorithms like Support Vector machine, Efficient Net, Residual Neural Network, and *** Algorithms created their own deep learning model specifically for predicting heart problems.

关键词： Heart Deep learning Support vector machines machine learning algorithms Accuracy Computational modeling Cardiac arrest Electrocardiography Myocardium Prediction algorithms

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Real Time Posture Detector using MediaPipe and OpenCV

Real Time Posture Detector using MediaPipe and OpenCV

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： Sukhdeep Singh Trilok Singh Tarun Singh Diana Nagpal Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

this work presents the development of a smart gym system utilizing the pre-trained posture estimation model, MediaPipe and torsion angle. While exercising offers numerous health benefits, improper form can pose significant risks. Existing resources like trainers and instructional videos are not always readily accessible, leading individuals to adopt self-learning methods. However, self-learning carries the risk of adopting incorrect postures, potentially causing both short-term and long-term health problems. this system addresses this challenge by analyzing and tracking user movements and postures in real-time using OpenCV. the proposed system then provides immediate feedback on any identified postures through a display screen, allowing users to rectify their mistakes and ensure safe and effective exercise routines with user-friendly interface. this novel approach empowers individuals to prioritize safe exercise techniques and achieve the fitness goals.

关键词： Visualization machine learning algorithms Tracking Pose estimation Real-time systems Mobile applications Security Smart phones Monitoring Videos

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Impact of Data Preprocessing on Wind Forecasting Using machine learning Techniques 4

Impact of Data Preprocessing on Wind Forecasting Using Machi...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Gaikwad, Nidhi Haribhau Mitra, Arghya Payal, Jangilwar Das, Soumyabrata Keshri, Ritesh kumar Department of Electrical Engineering VNIT Nagpur India

ISBN: (纸本)9798331530402

there is a high demand for renewable energy sources as non renewable energy sources are harmful to the environment due to its effects such as global warming. Among non renewable energy source, the wind energy is unpredictable in nature, making Its integration with the grid a concern. By accurately forecasting wind energy, the stability of the grid can be ensured. the aforementioned can be achieved with the help of machine learning (ML) techniques. this paper investigate four ML techniques Multiple Linear Regression, Support Vector Regression, Random Forest Regression, and Decision Trees which are utilized and compared based on the metrics such as root mean square error, mean square error, mean absolute error, and R2 for three different cases. Initially errors are calculated without data preprocessing and later with data preprocessing. It is observed that preprocessing reduces error. this paper also investigates the effect of different seasons on the errors generated by these ML techniques. For this research, data is taken from National Solar Radiance database (NSRD) over two year period from January 2018 to December 2019. © 2025 IEEE.

关键词： Multiple linear regression

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Sentiment Analysis of Hotel Customers' Feedback in Bangla Using machine learning

Sentiment Analysis of Hotel Customers' Feedback in Bangla Us...

引用

computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Jairul Islam Md. Shohidul Islam Husnul Ajra Department of CSE Khulna Khan Bahadur Ahsanullah University Khulna Bangladesh Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In the rapidly expanding internet era, customers or tourists can easily find information about hotels. the growth of the hospitality industry in Bangladesh has led to a rise in the number of online hotel reviews. In this respect, it becomes a concern to understand the customer experience and review the quality level of hotels relying on their services. Sentiment analysis is one of the intimate extents of text mining, where digital text-based consumer responses or emotions are reviewed using machine learning methods. the purpose of this study is to construct a benchmark dataset of hotel customers' feedback in Bangla and preprocess the data for sentiment analysis using natural language processing techniques. In accordance with this, it has proposed a machine learning method to accomplish sentiment analysis on Bangla text-based hotel reviews. It has been employed a set of machine learning algorithms like Decision Tree (DT), Random Forest (RF), Support Vector machines (SVM), and Naive Bayes (NB) to explore the accuracy of Bangla text data regarding customer feedback. these algorithms classify the reviews as positive, negative, or neutral. this study performs these algorithms and finds the consequences of machine learning models in terms of accuracy. However, the proposed analysis from these algorithms shows that RF provides the highest accuracy of 83% for sentiment analysis tasks. these findings suggest that combining machine learning techniques can be an effective way to analyze Bangla text-based hotel customers' feedback and provide valuable insights for the hospitality industry in Bangladesh.

关键词： Radio frequency Support vector machines Industries Sentiment analysis Analytical models machine learning algorithms Accuracy Reviews machine learning Classification algorithms

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A New Database Integrity Protection Approach Against SQL Injection Attacks (SQLIAs)

A New Database Integrity Protection Approach Against SQL Inj...

引用

computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Asma Hasan Alhowiti Awad M. Awadelkarim Mohamed Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

SQL Injection Attacks (SQLIAs) are among the most significant and serious threats to web applications, empowering assailants to employ countless techniques in order to steal and/or tamper data stored in databases, in addition to exploit vulnerabilities and gain unauthorized access. the unauthorized modification of sensitive data could compromise the integrity of the entire database. therefore, this paper proposes an approach to protect database integrity against SQL injection attacks. the proposed approach uses a structured methodology to identifying the associated vulnerabilities, namely threat modeling and vulnerability analysis, likewise for development and effectiveness evaluation. Moreover, the methodology incorporates Support Vector machine (SVM), a machine learning algorithm, to enhance the detection and prevention of SQL Injection Attacks (SQLIAs) while ensuring database integrity. Consequently, the proposed method enhances security measures by preventing unauthorized modifications to safeguard data integrity.

关键词： Support vector machines threat modeling machine learning algorithms Databases Prevention and mitigation Data integrity SQL injection Security Protection Information technology

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Android Malware Detection Using Ensemble learning and Feature Selection with Insights from SHAP Explainable AI

Android Malware Detection Using Ensemble Learning and Featur...

引用

international conference on soft computing and machine Intelligence (ISCMI)

作者： Rian Adriansyah Parman Sukarno Aulia Arif Wardana School of Computing Telkom University Bandung Indonesia Faculty of ICT Wroław University of Science and Technology Wroław Poland

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

the increasing proliferation of Android mobile devices, along with a surge in malware targeting these platforms, underscores the need for advanced malware detection methods. this research introduces a robust approach to Android malware detection by employing ensemble learning techniques. the integration of Random Forest, XGBoost, Extremely Randomized Trees, and Histogram-based Gradient Boosting models forms the core of the proposed method. Enhancing this approach, feature selection is performed using Random Forest Feature Importance, focusing on the most relevant features to achieve high accuracy and efficiency. Additionally, SHAP (SHapley Additive exPlanations) Explainable AI is utilized to provide transparency and a comprehensive understanding of the model’s decisions, thereby fostering user trust and comprehension. the proposed method was evaluated on the CICMalDroid2020 dataset, demonstrating superior performance, with XGBoost achieving the highest accuracy at $\mathbf{9 4. 8 8 \%}$. this research not only advances the field of malware detection on Android devices but also contributes significantly to the explanation and interpretation of machine learning models within the context of cybersecurity.

关键词： Performance evaluation Accuracy Explainable AI Feature extraction Boosting Malware Ensemble learning Surges Random forests Smart phones

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the Effective Detection of Alzheimer in the System via ML Algorithm

The Effective Detection of Alzheimer in the System via ML Al...

引用

Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： R. Kishore Kanna Kapil Joshi Gunjan Chhabra Ambali Pacholi X.S. Blessing NimaSajai Flosy R. Menezes Department of Biomedical Engineering Jerusalem College of Engineering (Autonomous) Chennai India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand School of Nursing Science ITM University Gwalior (MP) India College of Nursing Khamis Mushayt King Khalid University Kingdom of Saudi Arabia Institute of Nursing Education Bambolim(Goa) India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Effective therapy for Alzheimer’s disease depends on early diagnosis. Key results from four research that used machine learning to detect Alzheimer’s disease during its initial level. Early research indicates that combining cognitive and neuropsychological data may enhance diagnosis. the second research interprets neuroimaging data using machine learning methods. Finding the first signs of illness is the objective here. In the third research, risk variables for Alzheimer’s defect conditions are detected using machine learning algorithms. they stress the need of comprehensive data for precise prediction. A novel algorithm is presented in the fourth research. the soft voting classifier method increases recognition accuracy by operating on an averaging mechanism. these papers demonstrate how machine learning may enhance approaches for diagnosing Alzheimer’s disease. To enhance therapeutic results, combine everything from cognitive evaluations to multimodal data.

关键词： Neuroimaging machine learning algorithms Accuracy Sensitivity machine learning Classification algorithms Alzheimer's disease

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Beyond Asymptotics: Real-Time Performance of Basic Operations (Preliminary Results)

Beyond Asymptotics: Real-Time Performance of Basic Operation...

引用

computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Rania Alhalaseh Information Technology Faculty Mutah University Karak Jordan American University of Madaba (AUM) Jordan

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

this paper presents preliminary findings on the real-time performance of fundamental mathematical operations, moving beyond traditional asymptotic analysis. While asymptotic analysis offers valuable insights into algorithms' theoretical behavior, it is equally important to consider real-world applications' practical performance and resource constraints. Our work empirically measures and analyzes the execution time of basic operations (addition, subtraction, multiplication, division, etc.) across various data types and sizes, along with a basic Artificial Neural Network (ANN). the results emphasize the effect of size on the algorithm's overall performance, keeping in mind the complication of the operation itself. Our results focus on studying real-time performance beyond the theoretical analysis offered by asymptotic analysis are considered the building block for further research. Moreover, it lays the groundwork for a deeper understanding of real-time performance for scalable algorithms for real-time big data analytics where efficient execution of basic operations is essential. Also, applying adaptive optimization based on machine learning techniques for optimal performance. Using machine learning to predict the real-time cost of basic operations is an innovative approach.

关键词： Costs machine learning algorithms machine learning Artificial neural networks Prediction algorithms Real-time systems Time measurement Mathematical models Optimization Testing

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An Intelligent Approach for Classification In-Band SQLi Attacks Using machine learning Techniques

An Intelligent Approach for Classification In-Band SQLi Atta...

引用

computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Imdad Ali Shah NZ Jhanjhi School of Computing Science Taylor's University Malaysia School of Computer Science SCS Taylor's University Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In-band SQL injection is the simplest type of SQL injection, and attackers use this type to modify the original query and get direct results from the modified query. Users get information from the database through the SQL injection code technique. the software market is increasing daily, and hackers' activities are also growing from 2019 to 2023, using the SQL injection approach to receive important user information. the researchers presented many models for the classification of SQL injection in the software market, but our proposed model will use updated dataset features in pre-processing and provide the required results. Further, our proposal will work on the machine learning algorithm bases. this research paper presents identification problems on websites and works on the top 10 vulnerabilities A07:2021. Our proposed model works on the classification of phishing emails using the ML Approach. Our proposed algorithms are KNN and Random Forest. It provides higher accuracy due to its feature extraction and classification process. the KNN algorithm accuracy was 95.9% and the logistic regression technique Random Forest was used to improve the 99.6% proposed model.

关键词： Logistic regression machine learning algorithms Accuracy Phishing SQL injection Nearest neighbor methods software Classification algorithms Proposals Random forests

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