Analysis Of Machine Learning Techniques Research Articles

A comparative analysis of machine learning techniques for detecting probing attack with SHAP algorithm

A comparative analysis of machine learning techniques for detecting probing attack with SHAP algorithm

Risk integrated effort estimation of software projects: a comparative analysis of machine learning techniques

Risk integrated effort estimation of software projects: a comparative analysis of machine learning techniques

Outsmarting Phishers: A Comparative Analysis of Machine Learning Techniques

Phishing attacks threaten the security of the internet by stealing confidential data and money as well. As a way to prevent phishing, an extensive comparative study of the top most machine learning methods for phishing site detection was carried out. This research analyses the performance of ANN, RNN, XGBoost and Random Forest algorithms in the identification of phishing websites using the Kaggle dataset. These algorithms were selected due to their ability to uncover intricate associations and patterns from website information. The review examines the advantages and disadvantages each algorithm presents and compares them to each other based on accuracy efficient, precision, recall, F1 score, and computing efficiency. Through the comparison of these algorithms, the most effective algorithm for phishing detection is revealed, which can be useful to scholars and experts who focus on the improvement of on-line security. The research helps deposit the foundations for attacks prevention and facilitates the protection of online sensitive information. This study shows the effectiveness of using machine learning in the field of cybersecurity, especially with focus on the algorithms and how they can be optimized.

Analysis of Machine Learning Techniques to Identify and Mitigate Phishing Websites

Abstract: This paper focuses on understanding and solving the problem of phishing websites. Phishing is a type of cyber attack that creates fake websites and URLs that look like real people and tricks people into sharing sensitive information, such as passwords or financial details. These attacks target unsuspecting users and can have serious consequences. In this study, we discuss how AI can help identify and prevent phishing websites. We also explore different AI models that could create smarter, more effective anti-phishing systems in the future. While phishing is quite a common problem, no single method can address all the vulnerabilities. Instead, an amalgamation of techniques is often needed to combat different types of attacks. Machine learning, in particular, is a powerful tool that can mitigate phishing attempts and ensure online safety for everyone. As technology advances, phishing approaches are becoming more sophisticated and often outpacing the potency of present antiphishing tools, which often have limitations. This paper explores how to use machine learning controls to combat phishing. The goal is to create an efficient, accurate, and effective system. To achieve this, we use four machine learning models: K- Nearest Neighbors (KNN), Kernel-SVM, Random Forest Classifier, and Decision Tree. These models rely on labeled datasets to learn and improve their ability to detect phishing attempts. Each of these algorithms brings unique strengths to the classification process, helping to identify and prevent malicious activity. Machine learning allows cybersecurity systems to analyze patterns, adapt to new threats, and take proactive steps to stop future attacks. By integrating machine learning into security, we can take important steps to protect users from these attacks

Comparative Analysis of Machine Learning Techniques for Automated Classification of Pet Skin Diseases

Comparative Analysis of Machine Learning Techniques for Automated Classification of Pet Skin Diseases

A Comparative Analysis of Machine Learning Techniques to Explore Factors Affecting Mathematics Success in Developing Countries: Turkey, Mexico, Thailand, And Bulgaria Case Studies

This study explores factors influencing mathematics achievement in Turkey, Bulgaria, Mexico, and Thailand using PISA 2018 data and machine learning models, comparing their performance. Both classification and regression models were utilized: linear regression, support vector machine, decision tree, and random forest for regression; logistic regression, support vector, decision tree, and random forest for classification. Additionally, XGBoost identified key predictors of math achievement, and K-Means filled missing data. According to results, key contributing factors across all countries included students' economic, social, and cultural status, study materials at home, sense of ownership, and family welfare. Regarding model success, random forests outperformed other models in both regression and classification, with Random Forest Regression achieving the highest R-square values (71%-84%) while linear regression has the lowest (22%-43%). In addition, the classification algorithms were analyzed in terms of binary and ternary classification, binary classification proved more successful than ternary, with RF accuracy scores ranging from 73% to 83% across countries. The study's findings offer valuable insights for selecting optimal algorithms for predicting math achievement, aiding decision-makers in enhancing educational outcomes.

Comparative analysis of different supervised methods for satellite-based land-use classification: A case study of Reyhanlı

Satellite-based land-use classification plays a crucial role in various Earth observation applications, ranging from environmental monitoring to disaster management. This study presents a comparative analysis of machine learning techniques applied to land cover classification using Landsat-9 and Sentinel-2 satellite imagery in the Reyhanlı district in southern Türkiye. Three different classification algorithms, Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classification (MLC), were evaluated for their ability to distinguish different land cover classes. High resolution multispectral satellite imagery processed under the same conditions using Geographic Information System (GIS) software was utilized in this study. Visual inspection and statistical evaluation, including overall accuracy and kappa coefficient, were employed to assess classification performance. The classification of Sentinel-2 and Landsat-9 satellite imagery using different machine learning algorithms resulted in the highest overall accuracy (OA = 0.911, Kappa = 0.879) for Sentinel 2 imagery with the RF algorithm. These findings highlight the importance of satellite image selection and algorithm optimization for accurate land cover mapping. This study provides valuable insights for local planners and authorities and underscores the potential of Sentinel-2 imagery combined with machine learning techniques for effective land-use classification and monitoring.

Comparative Analysis of Machine Learning Models to Predict Common Vulnerabilities and Exposure

Predicting Common Vulnerabilities and Exposures (CVE) is a challenging task due to the increasing complexity of cyberattacks and the vast amount of threat data available. Effective prediction models are crucial for enabling cybersecurity teams to respond quickly and prevent potential exploits. This study aims to provide a comparative analysis of machine learning techniques for CVE prediction to enhance proactive vulnerability management and strengthening cybersecurity practices. The supervised machine learning model which is Gaussian Naive Bayes and unsupervised machine learning models that utilize clustering algorithms which are K-means and DBSCAN were employed for the predictive modelling. The performance of these models was compared using performance metrics such as accuracy, precision, recall, and F1-score. Among these models, the Gaussian Naive Bayes achieved an accuracy rate of 99.79%, and outperformed the clustering-based machine learning models in effectively determining the class labels or results of the data it was trained on or tested against. The outcome of this study will provide a proof of concept to Cybersecurity Malaysia, offering insights into the CVE model.

IoT-enabled monitoring and comparative analysis of machine learning techniques for detection of plant diseases in marigolds

IoT-enabled monitoring and comparative analysis of machine learning techniques for detection of plant diseases in marigolds

Comparative Analysis of Machine Learning Techniques for Fault Detection in Solar Panel Systems

Comparative Analysis of Machine Learning Techniques for Fault Detection in Solar Panel Systems

User Interface Bug Classification Model Using ML and NLP Techniques: A Comparative Performance Analysis of ML Models

Analyzing user interface (UI) bugs is an important step taken by testers and developers to assess the usability of the software product. UI bug classification helps in understanding the nature and cause of software failures. Manually classifying thousands of bugs is an inefficient and tedious job for both testers and developers. Objective of this research is to develop a classification model for the User Interface (UI) related bugs using supervised Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques. Also, to assess the effect of different sampling and feature vectorization techniques on the performance of ML algorithms. Classification is based upon ‘Summary’ feature of the bug report and utilizes six classifiers i.e., Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF) and Gradient Boosting (GB). Dataset obtained is vectored using two vectorization techniques of NLP i.e., Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). ML models are trained after vectorization and data balancing. The models ' hyperparameter tuning (HT) has also been done using the grid search approach to improve their efficacy. This work provides a comparative performance analysis of ML techniques using Accuracy, Precision, Recall and F1 Score. Performance results showed that a UI bug classification model can be built by training a tuned SVM classifier using TF-IDF and SMOTE (Synthetic Minority Oversampling Techniques). SVM classifier provided the highest performance measure with Accuracy: 0.88, Precision: 0.86, Recall: 0.85 and F1: 0.85. Result also inferred that the performance of ML algorithms with TF-IDF is better than BoW in most cases. This work provides classification of bugs that are related to only the user interface. Also, the effect of two different feature extraction techniques and sampling techniques on algorithms were analyzed, adding novelty to the research work.

MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS

This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key performance metrics. Results indicate that Random Forest outperformed other models in classification tasks with an accuracy of 92%, making it highly effective for real-time security assessment. SVM also demonstrated strong classification capabilities, particularly in high-dimensional spaces, with an accuracy of 88%. K-Means and DBSCAN clustering algorithms excelled in anomaly detection, identifying unusual patterns that could signal market irregularities. LSTM models, designed for time-series forecasting, achieved a root mean square error (RMSE) of 1.78, proving their utility in predicting future stock trends but requiring more computational resources.Our findings suggest that a hybrid approach, combining the strengths of supervised and deep learning models, can provide a robust solution for stock market security measurement. By leveraging explainable AI techniques such as SHAP and LIME, we also improved model interpretability, making these predictions more actionable for stakeholders. This research highlights the potential of machine learning in financial security monitoring and supports the growing integration of AI in the finance industry.

Comparative analysis of machine learning techniques in predicting dielectric behavior of ternary chalcogenide thin films

Abstract The current research introduces a comparative analysis of the dielectric behavior exhibited by ternary chalcogenide thin films, including both experimental data and machine learning techniques. The study of temperature and frequency dependencies of dielectric parameters is crucial for assessing material losses, particularly focusing on the low-frequency range where dielectric dispersion occurs. The experimental results on the frequency (100–1000000 Hz)and the temperature(303–393 K) influences on the dielectric (constant ( ε 1 ) and loss ( ε 2 )) of A s 4 G e 24 T e 72 composition in thin film form have been discussed. Results demonstrate that ε 1 exhibits an increasing trend with temperature, attributed to heightened orientation polarization as dipoles gain mobility with elevated thermal energy. Conversely, ε 1 declines with rising frequency, reflecting diverse different polarization mechanisms. Understanding these behaviors is important for material characterization and applications in fields like electronics and solar cells. A comparative analysis of the dielectric behavior exhibited by ternary chalcogenide thin films, including both experimental data and machine learning techniques. Through the experimental approach, the dielectric properties of the films are investigated. Additionally, the study employs a range of machine learning algorithms, including Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Genetic Programming (GP), hybrid techniques ANFIS-GA, and ANFIS-PSO, to model and predict the dielectric behavior with remarkable accuracy. The paper presents a comparison between the performance of the proposed machine learning models, highlighting their strengths and limitations in capturing the complex dielectric phenomena observed in the ternary chalcogenide thin films. This comparative study not only provides valuable insights into the dielectric properties of these materials but also demonstrates the efficacy of machine learning in understanding and predicting their behavior, paving the way for further advancements in materials science and device development.

A Multivariate Phenotypical Approach of Sepsis and Septic Shock-A Comprehensive Narrative Literature Review.

Despite medical advances, sepsis and septic shock remain some of the leading causes of mortality worldwide, with a high inter-individual variability in prognosis, clinical manifestations and response to treatment. Evidence suggests that pulmonary sepsis is one of the most severe forms of sepsis, while liver dysfunction, left ventricular dysfunction, and coagulopathy impact the prognostic. Sepsis-related hypothermia and a hypoinflammatory state are related to a poor outcome. Given the heterogeneity of sepsis and recent technological progress amongst machine learning analysis techniques, a new, personalized approach to sepsis is being intensively studied. Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. This has led to the devise of a phenotypical approach in sepsis, further dividing patients based on host and trigger-related factors, clinical manifestations and progression towards organ deficiencies, dynamic prognosis algorithms, and patient trajectory in the Intensive Care Unit (ICU). Host and trigger-related factors refer to patients' comorbidities, body mass index, age, temperature, immune response, type of bacteria and infection site. The progression to organ deficiencies refers to the individual particularities of sepsis-related multi-organ failure. Finally, the patient's trajectory in the ICU points out the need for a better understanding of interindividual responses to various supportive therapies. This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes as a useful clinical tool for a precision-based approach in sepsis and septic shock.

Comparative Analysis of Machine Learning Techniques for Identifying Multiple Force Systems from Accelerometer Measurements.

The knowledge of the forces acting on a structure enables, among many other factors, assessments of whether the component's useful life is compromised by the current machine condition. In many cases, a direct measurement of those forces becomes unfeasible, and an inverse problem must be solved. Among the solutions developed, machine learning techniques have stood out as powerful predictive tools increasingly applied to engineering problem-solving. This study evaluates the ability of different machine learning models to identify parameters of multi-force systems from accelerometer measurements. The models were assessed according to their prediction potential based on correlation coefficient (R2), mean relative error (MRE), and processing time. A computational numerical model using the finite element method was generated and validated by vibration measurements performed using accelerometers in the laboratory. A robust database created by the response surface methodology in conjunction with Design of Experiment (DOE) was used for the evaluation of the ability of machine learning models to predict the position, frequency, magnitude, and number of forces acting on a structure. Among the six machine learning models evaluated, k-NN was able to predict with a 0.013% error, and Random Forests showed a maximum error of 0.2%. The innovation of this study lies in the application of the proposed method for identifying parameters of multi-force systems.

Comparative Analysis of Machine Learning Techniques for Sentiment Classification

Abstract—Sentiment analysis is an important part of natural language processing (NLP) and has many applications in social media, e-commerce, and other fields. This study aims to provide a clear distinction between these methodologies by offering a structured overview of sentiment analysis and the variety of techniques used in its execution. The article examines different machine learning algorithms for sentiment analysis and high- lights their advantages and disadvantages by drawing on credible prior research on the subject. Additionally, the study provides a tabular comparison of different machine learning methods by selecting suitable parameters. Keywords: Decision Tree, Support Vector Machine (SVM), Random Forest, Convolution neural network (CNN), Neural network, Long Short-Term Memory Networks (LSTM), BERT

A Comparative Analysis of Machine Learning Techniques for Predicting the Performance of Microchannel Gas Coolers in CO2 Automotive Air-Conditioning Systems

The automotive industry is increasingly focused on developing more energy-efficient and eco-friendly air-conditioning systems. In this context, CO2 microchannel gas coolers (MCGCs) have emerged as promising alternatives due to their low global warming potential (GWP) and environmental benefits. This paper explores the application of machine learning (ML) algorithms to predict the thermohydraulic performance of MCGCs in automotive air-conditioning systems. Using data generated from an experimentally validated numerical model, this study compares various ML techniques, including both linear and nonlinear regression models, to forecast key performance metrics such as refrigerant outlet temperature, pressure drop, and heat transfer rate. Spearman’s correlation was employed to develop performance maps, whereas the R2 and MSE metrics were used to evaluate the models’ predictive accuracy. The linear models gave around 70% forecasting accuracy for pressure drop across the gas cooler and 97% accuracy for refrigerant outlet temperature, whereas the nonlinear models achieved more accurate predictions, with an accuracy ranging from 71% to 99%. This implies that nonlinear regression generally performs better than linear regression models in assessing the overall thermohydraulic performance of microchannel gas coolers. This research brings forth new ideas on how ML methods can be applied to enhance efficiency and effectiveness in gas coolers, contributing to the development of more eco-friendly automotive air-conditioning systems.

Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques

Flotation deinking is one of the most widely used techniques for the separation of ink particles from cellulose fibers during the process of paper recycling. It is a complex process influenced by a variety of factors, and is difficult to represent and usually results in models that are inconvenient to implement and/or interpret. In this paper, a comprehensive study of several machine learning methods for the prediction of flotation deinking performance is carried out, including support vector regression, regression tree ensembles (random forests and boosting) and Gaussian process regression. The prediction relies on the development of a limited dataset that assumes representative data samples obtained under a variety of laboratory conditions, including different reagents, pH values and flotation residence times. The results obtained in this paper confirm that the machine learning methods enable the accurate prediction of flotation deinking performance even when the dataset used for training the model is limited, thus enabling the determination of optimal conditions for the paper recycling process, with only minimal costs and effort. Considering the low complexity of the Gaussian process regression compared to the aforementioned ensemble models, it should be emphasized that the Gaussian process regression gave the best performance in estimating fiber recovery (R2 = 97.77%) and a reasonable performance in estimating the toner recovery (R2 = 86.31%).

اكتشاف سرطان الجلد الميلانيني بناءً على الذكاء الاصطناعي - دراسة استقصائية

In recent decades, melanoma, a lethal form of skin cancer, has become more common internationally. Due to the recovery strategy used in the medical field, dermoscopic image-based automatic skin lesion detection remains a challenging and complicated task. This difficulty in diagnosing lesions can be attributed to various factors, such as the lesions revealing diverse characteristics, including uncertain borders, inadequate color assessment, shape variations, positional dependence, and complex structures. To prevent the growing public health burden from spreading to other body organs and potentially save many lives, early detection and appropriate treatment are essential among medical professionals and researchers. An individual may develop melanoma if there is an abnormal change in the appearance of the skin. To achieve more effective cancer detection, dermatology expertise should be integrated with computer vision strategies. Consequently, it is crucial to create a variety of detection strategies to aid medical experts in diagnosing early-stage cancer. This paper provides a thorough, methodical analysis of machine learning techniques for early skin cancer detection, examining published studies. It offers an overview of an artificial intelligence-assisted evaluation approach for diagnosing skin cancer. To address existing challenges in skin cancer detection, this paper proposes a model integrating improved techniques across preprocessing, feature extraction, and classification. The proposed model utilizes adaptive histogram equalization to enhance image clarity, combined with feature extraction methods such as the ABCD rule, GLCM, and HOG, alongside deep learning-based autoencoders. For classification, it employs an ensemble approach that integrates CNNs and SVMs, leveraging transfer learning from pre-trained models and implementing a multi-class framework. This model aims to enhance detection accuracy, reduce costs, and improve usability. The review aims to provide researchers with the latest advancements in machine learning for cancer diagnostics, offering a comprehensive understanding of current and emerging techniques. Keywords: Skin Cancer, Melanoma Detection, Artificial Intelligence, Machine Learning, Deep Learning ،Neural Network Melanin.

Comparative analysis of machine learning techniques for detecting potability of water

This research aims to evaluate the effectiveness of machine learning algorithms in determining the potability of water. In the study, a total of 3276 water samples were analyzed for 10 different features that determine the potability of water. Besides that, the study's consideration is to evaluate the impact of trimming, IQR, and percentile methods on the performance of machine learning algorithms. The models were built using nine different classification algorithms (Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, K-Nearest Neighbors, Support Vector Machine, AdaBoost, and Bagging Classifier). According to the results, filling the missing data with the population mean and handling outliers with Trimming and IQR methods improved the performance of the models. Random Forest and Decision Tree algorithms were the most accurate in determining the potability of water. The findings of this research are of high importance to sustainable water resource management and serve as a crucial input for the decision-making process on the quality of water. The study also offers an example for researchers working on datasets that contain missing values and outliers.