Analysis Of Machine Learning Algorithms Research Articles

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (Top) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting Top and Eh, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in Eh prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

Comparative analysis of machine learning algorithms for phishing website detection

As phishing assaults continue to pose a serious hazard in the digital world, trustworthy detection techniques are required. The effectiveness of machine learning techniques in detecting phishing websites is investigated in this study. The best-performing models were XGBoost and Multilayer Perceptrons (MLPs), which obtained test data accuracy of 90.4% and 90.3%, respectively. On the test data, the Random Forest and Decision Tree models showed competitive accuracies of 86.5% and 87.3%, respectively. SVMs, or support vector machines, performed admirably as well, obtaining an accuracy of 86.4% on the test set. Notably, with accuracy of 74.0% on the test data, the Autoencoder Neural Network demonstrated a restricted level of efficacy. These results highlight the effectiveness of XGBoost and MLPs in precisely detecting phishing websites, offering academics and practitioners in cybersecurity useful information.

Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms

Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data.

Performance Analysis of Machine Learning Algorithms for MRI Using Wavelet Feature Extraction

Performance Analysis of Machine Learning Algorithms for MRI Using Wavelet Feature Extraction

Analysis of Machine Learning Algorithms for prediction and classification of Breast Cancer

Analysis of Machine Learning Algorithms for prediction and classification of Breast Cancer

Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.

Comparative Analysis of Machine Learning Algorithms for Diabetic Disease Identification

This article introduces a comparative analysis of different types of machine learning algorithms (MLAs) used for diabetic disease identification. Today machine learning algorithms are a major role in solving and identifying the different type of diseases in the medical sector. In the early prediction of diabetic to easily treat physicians and protect from other diseases in patients seven types of MLAs such as support vector machine (SVM), decision tree (DT), logistic regression (LGR), Gradient boost method (GDBM), k-nearest neighbour (KNN), XG boost (XGBM) and random forest (RF) are used for diabetic identification. PIMA Indian diabetic dataset (PIMAIDD) is used to train and test the MLAs. Confusion matrix, accuracy (ACR), precision (PCN), recall (RCL), f1-score (FSC), receiver operating curve (ROC) and K-fold cross-validation are the metrics used for performance evaluation of MLAs and experiments are implemented by Jupyter notebook and python sci-kit libraries. Six types of test cases were conducted whereas test case 4 (70%-30%) was well performed in which RF reported better results in diabetic identification that differentiates from other machine learning metric scores.

Enhancing Loan Prediction Accuracy: A Comparative Analysis of Machine Learning Algorithms with XAI Integration

The contemporary financial landscape necessitates loan recommendation systems that offer both accuracy and transparency. Conventional assessment methodologies often suffer from limitations in efficiency and transparency, leading to potential risks for both lenders and borrowers. This research proposes the development of a novel loan recommendation system that leverages the power of machine learning (ML) and Explainable Artificial Intelligence (XAI). The paper delves into the processes of data collection, preprocessing, model training, evaluation, and subsequent integration into a web application using the Flask framework. The employed datasets encompass a variety of loan types, with the study aiming to identify the most effective ML algorithms from a selection that includes XGBoost, CatBoost, Random Forest, Gradient Boosting, and Logistic Regression. To enhance the system's transparency, Explainable AI methods, such as LIME, are incorporated. The culmination of this research is a web application that facilitates personalized predictions regarding loan eligibility, accompanied by clear explanations. Index terms - Loan Recommendation System, Machine Learning, Explainable AI, XGBoost, CatBoost, Random Forest, Gradient Boost, Logistic Regression,LIME.

Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree

This study compares Support Vector Machine (SVM), Logistic Regression, and Decision Tree algorithms for liver disease prediction using a dataset sourced from Kaggle, comprising 20,000 training records and approximately 1,000 test records. The research evaluates the algorithms based on performance metrics, including accuracy, precision, recall, and F1-score. SVM emerged as the most effective model with an accuracy of 85%, followed by Logistic Regression with 82% and Decision Tree with 79%. The findings underscore the significance of algorithm selection in healthcare applications and highlight SVM's potential for early detection and intervention in liver disease cases, paving the way for improved patient outcomes and healthcare management. Future work will focus on refining the algorithms and validating the results with larger and more diverse datasets to enhance predictive accuracy and robustness further.

تحليل مقارن لخوارزميات التعلم الآلي لتصنيف مرض السكري باستخدام تحليل مصفوفة الارتباك

يستخدم العاملين في الرعاية الصحية التعلم الآلي أكثر فأكثر في السنوات الأخيرة لتعزيز نتائج المرضى وخفض التكاليف عليهم. بالإضافة إلى ذلك، تم تنفيذ التعلم الآلي في مجالات مختلفة، بما في ذلك تشخيص الأمراض، وتصنيف مخاطر المرضى، واقتراحات العلاج المخصصة، وتطوير الأدوية. يمكن لخوارزميات التعلم الآلي أن تفحص كميات هائلة من البيانات من السجلات الصحية الإلكترونية، والصور الطبية، ومصادر أخرى لتحديد الأنماط والتنبؤات، والتي يمكن أن تدعم المتخصصين في الرعاية الصحية والخبراء في اتخاذ قرارات مستنيرة، وتعزيز رعاية المرضى، وتحديد حالة المريض الصحية. في هذا الصدد، اختار المؤلف مقارنة أداء ثلاث خوارزميات (الانحدار اللوجستي ، Adaboost ، بايزالساذجة) من خلال معدل التصنيف الصحيح للتنبؤ بمرض السكري من أجل ضمان فعالية التشخيص الدقيق. تم الحصول على مجموعة البيانات المطبقة في هذا العمل من مستودع مؤسسي بجامعة فاندربيلت وهي بيانات متاحة للحميع. استنجتت الدراسة أن ثلاث خوارزميات فعالة للغاية في التنبؤ. بشكل أساسي، كان معدل تصنيف الانحدار اللوجستي و Adaboost أعلى من 92٪ ، وحققت خوارزمية بايز الساذجة معدل تصنيف أعلى من 90٪.

Comparative Analysis of Machine Learning Algorithms for Text-based Phishing Detection

Comparative Analysis of Machine Learning Algorithms for Text-based Phishing Detection

ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR SEISMIC ANOMALY DETECTION IN INDONESIA: UNVEILING PATTERNS IN THE PACIFIC RING OF FIRE

This study presents an integrated analysis of machine learning algorithms for the detection of seismic anomalies in Indonesia, a region within the volatile Pacific Ring of Fire. Employing Local Outlier Factor, Isolation Forest, and Elliptic Envelope algorithms, we processed a comprehensive dataset of seismic events characterized by latitude, longitude, depth, and magnitude. Our methodology involved standardizing these features and aggregating model predictions to establish a consensus mechanism for outlier detection. The results indicated that the vast majority of seismic events are consistent with the expected geological patterns, with a negligible percentage exhibiting anomalous behavior across the models. Through statistical analysis and visual mapping, we discerned that while anomalies are varied, they may correlate with specific seismic event features such as higher magnitudes or unique geographic locations. The consensus approach revealed a high-confidence subset of outliers, offering a focused direction for further seismological scrutiny. The study's implications extend to enhancing seismic risk assessment and early warning systems, providing a methodological framework for identifying seismic events that deviate from normative patterns. By outlining a scalable approach for anomaly detection, this research contributes to the predictive analytics tools available for disaster risk management and emergency preparedness, aiming to mitigate the impact of seismic hazards in seismically active regions

A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

For countries with large populations, such as Indonesia, food security is a very important issue. The majority of Indonesia's population depends on rice as their main food, and paddy is one of the most widely cultivated food commodities. The very good and accurate national paddy production prediction results really support decisions regarding national paddy production targets for the coming period. Therefore, to ensure supply and price stability, paddy availability must be predicted. Many studies have used machine learning to predict crop yields. By learning important patterns and relationships from input data, machine learning can combine the advantages of other methods to make better predictions of paddy yields. The aim of this research is to conduct a comparative analysis between three machine learning algorithms, namely, random forest, decision tree, and k-nearest neighbors, in predicting paddy production. To determine which algorithm is the best, a model evaluation is carried out using the coefficient of determination (R2-score), mean absolute error (MAE), and mean squared error (MSE). This research goes through methodological stages, starting from collecting datasets, data preprocessing, training and testing split datasets, applying algorithms, and evaluating the model. From this research, results were obtained for the random forest algorithm with an R2-score of 82.38%, MAE of 261726.20, and MSE of 2.19495E+11. For the decision tree, the R2-score was 79.62%, MAE was 323257.99, and MSE was 2.49304E+11. Meanwhile, k-nearest neighbors obtained an R2-score of 76.25%, MAE of 318433.42, and MSE of 2.90577E+11. The results of this research show that the random forest algorithm is the best for predicting paddy production because it obtains a larger R2-score as well as smaller MAE and MSE results.

Delamination and crack detection and localization of a laminated composite plate structure using regression and regression-based neural network methods

Despite significant progress, a comprehensive analysis of Machine learning algorithms for detecting delamination and crack damage in composite laminate plates is noticeably absent in the literature. This study aims to bridge this gap by meticulously help spectrum of six regression algorithms. By utilizing simulated displacement-time responses from a 16-layer laminated composite plate structure subjected to a random force, a robust dataset is constructed to evaluate the efficacy of these machine learning paradigms. Performance assessments rely on critical metrics: coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE).

Adaptive Analysis of Machine Learning Algorithms on Medical Datasets for Disease Prediction

This paper aims to analyze and evaluate the performance of different classification algorithms on medical datasets and gives some tips and guidelines for choosing the right algorithm for the dataset. For this purpose, twelve medical datasets were selected from the UCI repository, including breast cancer data, chronic kidney disease, cryotherapy, hepatitis, immunotherapy, Indian Liver Patient Dataset (ILPD), liver disorders, pima diabetes, risk factors cervical cancer, statlog (heart) dataset, dermatology and lung cancer. The selected classifiers for performance evaluation are Bagging, IBK, J48, JRip, Multilayer Perceptron (MP), Naïve Bayes (NB), Support Vector Machine (SVM), ZeroR and Voting Frequency Intervals (VFI). The algorithms were implemented on datasets using the WEKA tool and 10-fold cross-validation. Experimental results and analysis for all measures were introduced to guideline researchers who seek to achieve and how to balance different measures. Additionally, challenges and future directions were presented at the end of this paper.

Performance analysis of Machine Learning Algorithms to classify Software Requirements

Building a software system requires a clear understanding of its purpose and its operational characteristics. Functional requirements establish the system's purpose, while non-functional requirements define its operational performance aspects. It is essential to identify and classify requirements accurately to develop reliable software. In this research paper, we aim to classify functional and non-functional software requirements using different Machine Learning algorithms and techniques. We utilized four popular classification models, including Logistic Regression, Support Vector Machines, Decision Tree, and Random Forest Multi-layer Perceptron Neural Network to classify the requirements. To enhance the accuracy of the classification, we also applied a technique based on cosine similarity to verify if the custom string provided as input is related to software requirements. The addition of cosine similarity improved the accuracy of classification and reduced the misclassification of non-requirements.

A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images

Machine intelligence has the potential to play a significant role in diagnosing, managing, and guiding the treatment of disease, which supports the rising demands on healthcare to provide rapid and accurate interpretation of clinical data. The global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus (SARSCoV-2) exposed a need for rapid clinical data interpretation in response to an unprecedented burden on the healthcare system. A new healthcare challenge has arisen – post-COVID syndrome or ‘long COVID’. Symptoms of the post-COVID syndrome can persist for months following infection with SARS-CoV-2, often characterised by fatigue, breathlessness, dizziness, and pain. Despite this additional healthcare burden, no tests can diagnose, monitor, or determine the efficacy of treatments/interventions to support recovery. In this paper, an array of machine-learning algorithms is trained to evaluate and detect COVID-19-associated changes to lung tissue from X-ray images. X-ray images are classified from open sources into three categories: COVID-19 patients, patients with pneumonia, and unaffected otherwise healthy individuals using existing Machine Learning (ML) and pre-trained deep learning models. Prioritising models with the fewest false positives and false negatives assessed the performance of different models in detecting COVID-19-associated lung tissue. In addition, image pre-processing, data augmentation, and hyperparameter tuning are used to achieve the best accuracy in the models. Different ML models, including K Nearest Neighbour (KNN), and decision trees (DT), as well as transfer learning models such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG-16, VGG-19), ResNet50, DenseNet201, Xception, and InceptionV3, were tested to evaluate the performance of these models for X-ray images classification. The comparative analysis indicates that VGG-19 with augmentation performed best among the ten algorithms with a training accuracy of 99%, testing accuracy of 98%, and precision of 90% for COVID-19, 90% for normal, and 100% for pneumonia. This higher accuracy for detecting COVID-19-associated lung changes on X-ray may be further developed to stratify patients suffering from post-COVID syndrome. This may enable future intervention studies to determine the efficacy of treatments or better track patients’ prognoses to be optimised.

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Machine-learning Algorithm-based Risk Prediction and Screening-detected Prostate Cancer in A Benign Prostate Hyperplasia Cohort.

Prostate cancer (PCa) is lethal. Our aim in this retrospective cohort study was to use machine learning-based methodology to predict PCa risk in patients with benign prostate hyperplasia (BPH), identify potential risk factors, and optimize predictive performance. The dataset was extracted from a clinical information database of patients at a single institute from January 2000 to December 2020. Patients newly diagnosed with BPH and prescribed alpha blockers/5-alpha-reductase inhibitors were enrolled. Patients were excluded if they had a previous diagnosis of any cancer or were diagnosed with PCa within 1 month of enrolment. The study endpoint was PCa diagnosis. The study utilized the extreme gradient boosting (XGB), support vector machine (SVM) and K-nearest neighbors (KNN) machine-learning algorithms for analysis. The dataset used in this study included 5,122 medical records of patients with and without PCa, with 19 patient characteristics. The SVM and XGB models performed better than the KNN model in terms of accuracy and area under curve. Local interpretable model-agnostic explanation and Shapley additive explanations analysis showed that body mass index (BMI) and late prostate-specific antigen (PSA) were important features for the SVM model, while PSA velocity, late PSA, and BMI were important features for the XGB model. Use of 5-alpha-reductase inhibitor was associated with a higher incidence of PCa, with similar survival outcomes compared to non-users. Machine learning can enhance personalized PCa risk assessments for patients with BPH but more research is necessary to refine these models and address data biases. Clinicians should use them as supplementary tools alongside traditional screening methods.

Comparative analysis of machine learning algorithms for Diabetes Mellitus prediction

Type II Diabetes Mellitus (T2DM) has become an increasingly prevalent disease due to the rising number of the obese population. Even though this disorder is the leading cause of more severe health complications, studies have shown that T2DM is largely preventable if detected in an early phase. To improve current diagnosis and prognosis procedures, this study aims to examine three machine learning algorithmsK-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Classification (SVC)and their potential in making accurate predictions on the outcomes of the Pima Indians Diabetes Dataset (PIDD). After training and 5-fold cross validation, the results show that the RF algorithm has the highest accuracy at 75.25%, followed by SVC at 74.91% and KNN at 71.01%. In addition, feature importances were evaluated for all three models, yet we observed a drastic difference in the top-ranked features across different models, which implies that more training and larger datasets are necessary before realizing these computational approaches into practice. Nevertheless, the potential of these approaches highlighted in this study demonstrated that machine learning is a burgeoning strategy in clinical use and in solving real-world problems.