Machine Learning Approach For Analysis Research Articles

Advancing user classification models: A comparative analysis of machine learning approaches to enhance faculty password policies at the University of Buraimi

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

Improved modelling of low-pressure rotor speed in commercial turbofan engines: A comprehensive analysis of machine learning approaches

Accurate engine thrust modelling offers notable opportunities for reducing fuel consumption, mitigating emission effects, managing air traffic, and optimizing the preliminary design of aircraft. This study focuses on the development of machine learning models to predict the low-pressure rotor speed (N1) parameter, which is closely related to the thrust of turbofan engines, for commercial aircraft during the cruise phase. The analysis utilizes a Flight Data Recorder (FDR) dataset from 1086 actual flights involving twin-engine, narrow-body, short-to-medium haul commercial aircraft equipped with CFM56-7B turbofan engines. To achieve accurate N1 parameter predictions, the study employs machine learning models, including Deep Learning (DL), Random Forest (RF), Gradient Boosted Machines (GBM), and Artificial Neural Networks (ANN). These models are evaluated using statistically significant indicators, demonstrating that machine learning models yield highly accurate results. Among the models tested, the DL model provides the most precise estimates of the N1 parameter. This study not only advances the understanding of engine thrust modelling through machine learning but also provides practical insights for the aerospace industry. The findings underline the potential of machine learning techniques in delivering superior prediction accuracy, which can be integrated into real-time flight management systems, ultimately contributing to more sustainable and efficient aviation operations.

Comparative analysis of machine learning approaches in Kazakh banknote classification

Comparative analysis of machine learning approaches in Kazakh banknote classification

A Comparative Analysis of Machine Learning Approaches for Evaluating the Compressive Strength of Pozzolanic Concrete

This study leverages machine learning techniques to predict pozzolanic concrete's compressive strength accurately. Using artificial neural networks (ANN), random forest (RF), and gradient boosting regressor (GBR) models trained on a dataset of 482 samples, the study divides the data into 70% training and 30% testing subsets with seven input parameters. Model performance is assessed through metrics like coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The RF model excels, achieving R2 values of 0.976 in training and 0.964 in testing, along with the lowest RMSE (2.84 MPa) and MAE (2.05 MPa) during training and RMSE values of 7.81 MPa and MAE values of 5.89 MPa during testing, demonstrating superior predictive accuracy. Sensitivity analysis highlights the pivotal role of cement as an input parameter, contributing significantly to the model's accuracy. Employing K-fold cross-validation confirms the RF model's robustness with an average R2 value of 0.959. This research underscores the RF model's reliability and effectiveness in forecasting pozzolanic concrete compressive strength, with practical applications for concrete optimization and construction practices, establishing it as the preferred choice compared to other machine learning models. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 90-122

A Comparative Analysis of Machine Learning Approaches for State-of-Charge Forecasting for Enhancing Lithium-ion Battery Management in Electric Vehicles

A Comparative Analysis of Machine Learning Approaches for State-of-Charge Forecasting for Enhancing Lithium-ion Battery Management in Electric Vehicles

Analysis of Machine Learning Approaches for DNA Sequencing and Classification: An optimized Approach

Analysis of Machine Learning Approaches for DNA Sequencing and Classification: An optimized Approach

Analysis of machine learning approaches to determine online shopping ratings using naïve bayes and svm

This research aims to identify and compare the effectiveness of Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying ratings based on customer comments on the Lazada online shopping platform. The main issues identified include data uncertainty, model selection and optimization, as well as improving efficiency and scalability. Using a dataset of comments and reviews from Lazada, this study conducts an analysis using both algorithms to determine which is most effective in classifying comments into appropriate ratings. The research methodology includes data collection, text preprocessing, algorithm implementation, and evaluation using a confusion matrix to measure accuracy, precision, recall, and F-measure. This analysis is supported by data visualization using Python, allowing for in-depth interpretation and understanding of the results. The research findings show significant differences in the performance of both algorithms, with each having strengths in certain aspects of classification. The discussion in this study interprets these results to address the research questions formulated and demonstrates the practical application of machine learning theory in real-world data processing. This study concludes that both algorithms have significant potential in sentiment classification but require further adjustment and optimization to improve accuracy and efficiency. Recommendations for further research include the development of hybrid models or new approaches that can address the identified limitations, as well as exploration of more diverse datasets to test the scalability of the proposed solutions.

Optimizing Speech to Text Conversion in Turkish: An Analysis of Machine Learning Approaches

The Conversion of Speech to Text (CoST) is crucial for developing automated systems to understand and process voice commands. Studies have focused on developing this task, especially for Turkish-specific voice commands, a strategic language in the international arena. However, researchers face various challenges, such as Turkish's suffixed structure, phonological features and unique letters, dialect and accent differences, word stress, word-initial vowel effects, background noise, gender-based sound variations, and dialectal differences. To address the challenges above, this study aims to convert speech data consisting of Turkish-specific audio clips, which have been limitedly researched in the literature, into texts with high-performance accuracy using different Machine Learning (ML) models, especially models such as Convolutional Neural Networks (CNNs) and Convolutional Recurrent Neural Networks (CRNNs). For this purpose, experimental studies were conducted on a dataset of 26,485 Turkish audio clips, and performance evaluation was performed with various metrics. In addition, hyperparameters were optimized to improve the model's performance in experimental studies. A performance of over 97% has been achieved according to the F1-score metric. The highest performance results were obtained with the CRNN approach. In conclusion, this study provides valuable insights into the strengths and limitations of various ML models applied to CoST. In addition to potentially contributing to a wide range of applications, such as supporting hard-of-hearing individuals, facilitating notetaking, automatic captioning, and improving voice command recognition systems, this study is one of the first in the literature on CoST in Turkish.

Enhancing river flow predictions: Comparative analysis of machine learning approaches in modeling stage-discharge relationship

Streamflow, a pivotal variable in water resources management, holds profound significance in shaping the decision-making processes of hydrologic projects. This paper tries to delve into the exploration of the stage-discharge relationship using three machine learning methods (MLMs) namely multi-layer neural networks (MLNN), radial basis neural networks (RBNN), and neuro-fuzzy systems (ANFIS) to predict and simulate mean daily stage-discharge data derived from two monitoring stations, Bulakbasi and Karaozü, Kizilirmak River, Turkey. Root mean square error (RMSE), Mean absolute percentage error (MAPE), coefficient of determination (R2), and the Developed Discrepancy Ratio (DDR) metrics were utilized to MLMs' performance assessment. The performance evaluation indices (RMSE, MAEP, R2, DDR) for the preeminent MLNN model applied to Bulakhbashi and Karasu stations were determined as (0.29, 1.57, 0.9998, 17.62) and (1.71, 6.56, 0.9980, 6.65), respectively. The MLNN model contributed to a notable enhancement in the RMSE performance index for the aforementioned stations, exhibiting improvements of 87% and 56%, respectively. These results affirm the MLNN's proficiency in accurately capturing the stage-discharge at both monitoring stations.

Fraud Guard: A Comprehensive Comparative Analysis of Machine Learning Approaches to Enhance Credit Card Fraud Detection

Fraud Guard: A Comprehensive Comparative Analysis of Machine Learning Approaches to Enhance Credit Card Fraud Detection

Discrete mathematical models for enhancing cybersecurity : A mathematical and statistical analysis of machine learning approaches in phishing attack detection

This paper presents a discreate mathematical modelling of cybersecurity phishing attack detection methodologies, emphasizing the crucial role of continual advancements in detection methods amidst the pervasive threat of phishing attacks in the cybersecurity landscape. Leveraging mathematical modeling and machine learning algorithms, the study employs three distinct datasets—Mendeley, URL tokenized, and a merged dataset integrating both. Multiple machine learning algorithms, including Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Random Forest, Gradient Boosting Machines, Neural Networks, CatBoost, and XGBoost, are systematically applied to evaluate their efficacy. In the original Mendeley dataset, XGBoost achieves a top accuracy of 97.24%, along with CatBoost and Random Forest exceeding 97%. Post-preprocessing, CatBoost leads with an accuracy of 97.28%, showcasing superior precision, sensitivity, and F-score. Despite slight accuracy reductions post-preprocessing, models consistently achieve over 94% accuracy on the preprocessed Mendeley dataset, highlighting the substantial impact of preprocessing. Tokenized URLs exhibit comparatively lower performance, with the highest accuracy at 91.95%, emphasizing the challenges associated with this approach. The combined dataset proves optimal for most models, with XGBoost and SVM achieving the highest overall accuracy at 97.68%. SVM excels in sensitivity and specificity, while XGBoost excels in precision. The merged dataset significantly enhances accuracy, sensitivity, specificity, and precision, underscoring its pivotal role in refining predictive capabilities for identifying phishing websites. The results section provides a detailed overview of machine learning model performance on different datasets. CatBoost emerges as a standout performer on the preprocessed Mendeley dataset. The tokenized URLs offer valuable insights into associated challenges, and the combined dataset proves effective for various models. Confusion matrices, ROC curves, and Precision-Recall curves provide nuanced perspectives on model behavior, emphasizing the need for ongoing refinement and investigation into misclassification patterns to enhance model effectiveness in combating phishing threats.

Analysis of machine learning approaches to packing detection

Packing is a widely used obfuscation technique by which malware hides content and behavior. Much research explores how to detect a packed program via such varied approaches as entropy analysis, syntactic signatures, and, more recently, machine learning classifiers using various features. Yet no robust results indicate which algorithms perform best or which features are most significant. Reviews of these results highlight how accuracy, cost, generalization of capabilities, and other measures complicate evaluations. Our work addresses deficiencies by assessing nine different machine-learning approaches using 119 features to identify which features are most significant for packing detection, which algorithms offer the best performance, and which algorithms are most economical.

Predictive modelling of surface chloride concentration in marine concrete structures: a comparative analysis of machine learning approaches

Predictive modelling of surface chloride concentration in marine concrete structures: a comparative analysis of machine learning approaches

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity.

Respiratory diseases are among the major health problems causing a burden on hospitals. Diagnosis of infection and rapid prediction of severity without time-consuming clinical tests could be beneficial in preventing the spread and progression of the disease, especially in countries where health systems remain incapable. Personalized medicine studies involving statistics and computer technologies could help to address this need. In addition to individual studies, competitions are also held such as Dialogue for Reverse Engineering Assessment and Methods (DREAM) challenge which is a community-driven organization with a mission to research biology, bioinformatics, and biomedicine. One of these competitions was the Respiratory Viral DREAM Challenge, which aimed to develop early predictive biomarkers for respiratory virus infections. These efforts are promising, however, the prediction performance of the computational methods developed for detecting respiratory diseases still has room for improvement. In this study, we focused on improving the performance of predicting the infection and symptom severity of individuals infected with various respiratory viruses using gene expression data collected before and after exposure. The publicly available gene expression dataset in the Gene Expression Omnibus, named GSE73072, containing samples exposed to four respiratory viruses (H1N1, H3N2, human rhinovirus (HRV), and respiratory syncytial virus (RSV)) was used as input data. Various preprocessing methods and machine learning algorithms were implemented and compared to achieve the best prediction performance. The experimental results showed that the proposed approaches obtained a prediction performance of 0.9746 area under the precision-recall curve (AUPRC) for infection (i.e., shedding) prediction (SC-1), 0.9182 AUPRC for symptom class prediction (SC-2), and 0.6733 Pearson correlation for symptom score prediction (SC-3) by outperforming the best leaderboard scores of Respiratory Viral DREAM Challenge (a 4.48% improvement for SC-1, a 13.68% improvement for SC-2, and a 13.98% improvement for SC-3). Additionally, over-representation analysis (ORA), which is a statistical method for objectively determining whether certain genes are more prevalent in pre-defined sets such as pathways, was applied using the most significant genes selected by feature selection methods. The results show that pathways associated with the 'adaptive immune system' and 'immune disease' are strongly linked to pre-infection and symptom development. These findings contribute to our knowledge about predicting respiratory infections and are expected to facilitate the development of future studies that concentrate on predicting not only infections but also the associated symptoms.

P23 Machine Learning Approach for Analysis of Prodromal Phase for Early Risk Prediction in Multiple Sclerosis

P23 Machine Learning Approach for Analysis of Prodromal Phase for Early Risk Prediction in Multiple Sclerosis

A hybrid machine learning approach for analysis of stegomalware

PurposeGiven how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.Design/methodology/approachThis research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.FindingsThe tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results.Originality/valueCyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially expedient where the malware is stealthy, hiding IOC.

Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers

Impact energy, the main performance subject of hydraulic breakers, is required to evaluate value from consumers. This study proposes a neural network algorithm-based model to predict the impact energy of a hydraulic breaker without measuring it. The proposed model was developed using 1451 data points for various parameters as an input to predict the impact energy of hydraulic breakers in a small class to a large class. Different machine learning methods have been studied, including correlation analysis, linear regression, and neural networks. The results revealed that the working pressure, working flow rate, chisel diameter, nitrogen gas pressure, operating frequency, and power significantly influenced impact energy formation. The results obtained provide a reliable model for predicting the impact energy of hydraulic circuit breakers of various sizes.

Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language

Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language

Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing

Computational drug repurposing is an efficient method to utilize existing knowledge for understanding and predicting their effect on neurological diseases. The ability of a molecule to cross the blood-brain barrier is a primary criteria for effective therapy. Thus, accurate predictions by employing Machine learning models can effectively identify the drug candidates that could be repurposed for neurological conditions. This study comprehensively analyzes the performance of the well-known machine learning models on two different datasets to overcome dataset-related biases. We found that random forest and extratrees (i.e., tree-based ensembled models) have the highest accuracy with mol2vec fingerprint for BBB permeability prediction, attaining AUC_ROC of 0.9453 and 0.9601 on BBB and B3DB dataset, respectively. Additionally, we have analyzed the impact of the data balancing technique (i.e., SMOTE) to improve the specificity of the models. Finally, we have explored the impact of different fingerprint combinations on accuracy. By employing SMOTE and fingerprint combination, SVC attains the highest AUC_ROC of 0.9511 on BBB dataset. Finally, we used the best-performing models of the B3DB dataset to evaluate the BBB permeability for drugs intended to be used for repurposing. Model validation for repurposing predicted the non-passage for most antihypertensive drugs and passage for CYP17A1 cancer drugs.

Machine Learning Approach for Analysis of Ionosphere Parameters for Earthquake Precursors

Machine Learning Approach for Analysis of Ionosphere Parameters for Earthquake Precursors