Comparative Analysis Of Models Research Articles

Predicting Online Gaming Behavior: A Comparative Analysis of Random Forest and Gaussian Naive Bayes Models

With the rising impact and economic benefits of the online gaming industry, online gaming companies need to understand players’ behavior to evaluate their products better and adjust their developing strategy. This paper uses machine learning models to predict players’ online gaming behaviors. The study utilizes the dataset from Kaggle, which contains 40,034 samples with 13 features related to player demographics, game preferences, and gameplay behaviors. The primary objective is to classify two target variables: ‘EngagementLevel’ and ‘InGamePurchases.’ Data preprocessing steps included handling missing values, encoding categorical features, normalizing numerical data, and addressing class imbalances using the Synthetic Minority Over-sampling Technique (SMOTE). The Random Forests (RF) and Gaussian Naïve Bayes (GNB) models were employed to predict the target variables. The RF model significantly outperformed the GNB model in both classification tasks, especially when predicting the in-game purchases. The RF model’s robustness is attributed to its ability to handle complex, non-linear relationships and interactions between features, while the GNB model’s performance was limited by its assumptions of feature independence and normally distributed data. The findings suggest that RF is a more effective tool for predicting online gaming behaviors, particularly in scenarios with complex feature interactions. Future research could incorporate additional machine learning models and more diverse datasets to further enhance predictive accuracy and offer a more comprehensive understanding of online gaming behaviors.

Predicting Soil Organic Carbon in Forest Soils of Telangana, India: A Comparative Analysis of Deterministic and Geostatistical Interpolation Models

Predicting Soil Organic Carbon in Forest Soils of Telangana, India: A Comparative Analysis of Deterministic and Geostatistical Interpolation Models

Characteristics of Economic Fairness in the Music Streaming Economy: A Comparative Analysis of Current Distribution Models

This article compares current distribution models in the music streaming economy. The models presented are the pro rata model, the user-centric model (UCM), a progressive extension of the UCM and an alternative model. It focuses on a critique of the UCM as an alternative remuneration model, arguing that the UCM has the potential for economic devaluation, which can be exposed through simple logical analysis. It also provides criteria for fair remuneration in a distribution model.

Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models

Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models

Deep Learning Approaches for Potato Price Forecasting: Comparative Analysis of LSTM, Bi-LSTM, and AM-LSTM Models

Deep Learning Approaches for Potato Price Forecasting: Comparative Analysis of LSTM, Bi-LSTM, and AM-LSTM Models

Comparative Analysis of the Portfolio-balance Models of Exchange Rate Determination

The current study conducts a comparative analysis of Portfolio-Balance Models (PBM) developed by Branson, Kouri and Dornbusch to assess the role of expectations and time horizons in determining and forecasting the India–US exchange rate over the period 1996:Q2–2019:Q3. Notably, it improves the original models by integrating microstructure theory into their framework. The Autoregressive Distributed Lag Error-Correction Model (ARDL-ECM) is used to investigate both short run and long run behaviour of the models. Additionally, the study assesses out-of-sample forecasting accuracy of the modified models against the Random Walk Model (RWM) using the root mean square error metric. The estimation results reveal that models based on rational expectations are better than the static expectations model. Notably, the microeconomic determinant is counterintuitively significant only in the long run across all models. Furthermore, these modified models demonstrate superior out-of-sample forecasting abilities compared to RWM for alternative forecasting horizons. However, forecasting results over a 6-month period is better with short run models. Over 1-year and 2-year horizons, rational expectations models outperform the static expectations model. This study challenges the Meese–Rogoff puzzle, ensuring that PBM, when modified to incorporate microstructure theory, is valid and yields superior forecasting results compared to RWM. JEL Codes: F31, F32, C22

Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention

With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conducts a comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edge-based, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies.

Comparative analysis and application of soft sensor models in domestic wastewater treatment for advancing sustainability

ABSTRACT This study focuses on the development and evaluation of soft sensor models for predicting NH3-N values in a wastewater treatment process. The study compares the performance of linear regression (LR), neural networks (NN) and random forest regression (RFR) models. The proposed methodology involves optimizing the sequencing batch reactor process using artificial intelligence and an automatic control system. Real-time NH3-N values are obtained by inputting data from electronic conductivity and temperature sensors into the prediction models. Once the predicted NH3-N value falls below the effluent standard, the cycle ends, improving energy efficiency and sustainability by cutting down the agitator and aerator. The research results demonstrate that the RNN-based NH3-N soft sensor built in this study exhibits the best performance, which is promising for wastewater treatment process optimization and evaluation. The results show that sensor model NNR[0.5Y]H exhibits exceptional performance, utilizing recurrent neural network with 5-step input delays. Sensor NNR[0.5Y]H exhibits an R2 of 0.921, an RMSE of 6.110, and an MAE of 4.558. Based on the findings, recurrent neural network (RNN) variants emerge as the most effective modeling technique due to their ability to capture temporal dependencies and handle variable-length sequences. This study provides satisfied performance results for the NNR[0.5Y]H soft sensor model in NH3-N monitoring and process optimization in wastewater treatment, highlighting the effectiveness of recurrent neural networks and their contribution to improving interpretability, accuracy, and adaptability of soft sensor models.

Comparative Study of Cox Proportional Hazard and Accelerated Failure Time Models on Survival of Diabetes Mellitus and Hypertension Patients

Comparative analysis of survival models for hypertension and diabetics carried out in this study. The study compared the performance of Cox Proportional Hazard (CPH) and Accelerated Failure Time (AFT) models with and without random effects in analyzing hypertension and diabetes survival data obtained from General Hospital Keffi and General Hospital Nasarawa for the period of five years (2018-2022). Models evaluated include CPH, AFT (Exponential, Weibull, Log-Logistic) and their corresponding random effect counterparts. The Weibull AFT random effect model outperformed all other models in terms of goodness-of-fit, predictive accuracy and interpretability. This model accounted for individual heterogeneity and clustering effects, providing a more nuanced understanding of risk factors’ impact on survival rates. The Weibull AFT random effects model is the most suitable choice for analyzing hypertension and diabetes survival data, offering improved predictive power and insight into the complex relationships between risk factors and time-to-event outcomes. These findings have significant implications for healthcare professionals, policymakers, and researchers seeking to optimize survival analysis methodologies.

Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting

Machine learning has become a powerful tool in forecasting, offering greater accuracy than traditional human predictions in today’s data-driven world. The capability of machine learning to predict future trends has significant implications for key sectors such as finance, healthcare, and supply chain management. In this study, ARIMA/SARIMA (AutoRegressive Integrated Moving Average/Seasonal AutoRegressive Integrated Moving Average), alongside Prophet, a scalable forecasting tool developed by Facebook based on a generalized additive model, are considered. These models are applied to predict the demand for antidiabetic drugs. The records were collected by the Australian Health Insurance Commission. This dataset was sourced from Medicare Australia. The study evaluates the performance of these models based on their Mean Absolute Error (MAE), a key metric for assessing forecast accuracy. Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are also considered. The outcome of the comparative analysis shows that the Prophet model outperformed both ARIMA and SARIMA models, achieving an MAE of 0.74, which is significantly lower than the MAE values of 2.18 and 3.02 obtained by SARIMA and ARIMA, respectively. Prophet's superior performance shows its effectiveness in handling complex, non-linear trends and seasonal patterns often observed in real-world time series data. This research contributes to the growing knowledge of machine learning-based forecasting and shows the importance of advanced models like Prophet in optimizing business operations and driving innovation. The findings from this research offer valuable guidance for data experts, analysts, and researchers in selecting the best forecasting methods for reliable predictions.

Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

Comparative thermal model analysis and experimental validation for predicting performance of a pyramidal solar still with integrated pulsating heat pipe

Developing nations face a dire situation as water scarcity and pollution significantly impact various aspects of life. It's crucial to take action now to address these pressing issues and ensure a sustainable future for all. Solar desalination processes emerge as a promising solution to these pressing problems in water purification technologies.This paper reports the thermal modeling and experimental results of two solar still designs, namely (i) Pyramidal solar still with pulsating heat pipe (PHP), Modified pyramidal solar still (MPSS), and (ii) conventional pyramidal solar still (CPSS). This study uses energy balance equations to focus on MPSS and CPSS thermal modeling. In the MPSS, a PHP, in conjunction with a solar collector, provides external heat to basin water. Several models, including Dunkel, Kumar and Tiwari, Chen, and Zheng Hongfei, are utilized to estimate the performance of both MPSS and CPSS. Extensive experiments have been conducted for validation purposes. Key parameters considered for prediction and comparison include evaporative heat transfer coefficient, convective heat transfer coefficient, total heat transfer coefficient, and hourly yield. It is noted that the Kumar and Tiwari model demonstrates a superior ability to predict cumulative yield, with a percentage error of 5.81 % and 5.9 % for MPSS and CPSS, respectively, compared to the experimental cumulative yield. The theoretical average basin water temperature observed in MPSS is 64.56 °C, and CPSS is 58.8 °C. The enhancement in temperature is attributed to the supplementary heat provided by the PHP.

Financial and Market Implications of the CSX-Conrail Acquisition

Conrail, Consolidated Rail Corporation, founded in 1976, was established due to the bankruptcy of multiple railway companies in the Northeast and Midwest regions of the United States. The federal government intervened to establish the company with the aim of restoring the services and profitability of bankrupt railways. As it developed, Conrail's railway network covered major cities and industrial centers in the Northeast and Midwest of the United States, possessing a strong market position and strategic value. Therefore, Conrail had become a target for mergers and acquisitions by large railway companies such as CSX and Norfolk Southern. Through mergers and acquisitions, the acquiring party could expand market coverage, enhance operational network connectivity and competitiveness. This is a complex and multi-layered issue, the CSX acquisition of Conrail involved financial and market implications, legal obstacles, competitive pressures, and strategic objectives. By analyzing these themes in depth, we could understand the key issues and response strategies that enterprises face in the M&A process, providing valuable experience for future M&A transactions. The study applied methods such as comparative analysis and case model analysis to analyze the main challenges, response strategies, and effects comprehensively and systematically in the M&A process, providing reference for similar cases. Overall, CSX's acquisition of Conrail demonstrates how companies can achieve acquisition goals and long-term development through flexible strategies, effective communication, and professional management in the face of complex legal, market, and operational challenges.

Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis.

Diagnosing atypical pigmented facial lesions (aPFLs) is a challenging topic for dermatologists. Accurate diagnosis of these lesions is crucial for effective patient management, especially in dermatology, where visual assessment plays a central role. Incorrect diagnoses can result in mismanagement, delays in appropriate interventions, and potential harm. AI, however, holds the potential to enhance diagnostic accuracy and provide reliable support to clinicians. This work aimed to evaluate and compare the effectiveness of machine learning (logistic regression of lesion features and patient metadata) and deep learning (CNN analysis of images) models in dermoscopy diagnosis and the management of aPFLs. This study involved the analysis of 1197 dermoscopic images of facial lesions excised due to suspicious and histologically confirmed malignancy, classified into seven classes (lentigo maligna-LM; lentigo maligna melanoma-LMM; atypical nevi-AN; pigmented actinic keratosis-PAK; solar lentigo-SL; seborrheic keratosis-SK; and seborrheic lichenoid keratosis-SLK). Image samples were collected through the Integrated Dermoscopy Score (iDScore) project. The statistical analysis of the dataset shows that the patients mean age was 65.5 ± 14.2, and the gender was equally distributed (580 males-48.5%; 617 females-51.5%). A total of 41.7% of the sample constituted malignant lesions (LM and LMM). Meanwhile, the benign lesions were mainly PAK (19.3%), followed by SL (22.2%), AN (10.4%), SK (4.0%), and SLK (2.3%). The lesions were mainly localised in the cheek and nose areas. A stratified analysis of the assessment provided by the enrolled dermatologists was also performed, resulting in 2445 evaluations of the 1197 images (2.1 evaluations per image on average). The physicians demonstrated higher accuracy in differentiating between malignant and benign lesions (71.2%) than in distinguishing between the seven specific diagnoses across all the images (42.9%). The logistic regression model obtained a precision of 39.1%, a sensitivity of 100%, a specificity of 33.9%, and an accuracy of 53.6% on the test set, while the CNN model showed lower sensitivity (58.2%) and higher precision (47.0%), specificity (90.8%), and accuracy (59.5%) for melanoma diagnosis. This research demonstrates how AI can enhance the diagnostic accuracy in complex dermatological cases like aPFLs by integrating AI models with clinical data and evaluating different diagnostic approaches, paving the way for more precise and scalable AI applications in dermatology, showing their critical role in improving patient management and the outcomes in dermatology.

Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables

Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables

A comparative analysis of an aerosol dry deposition microscale model for overhead powerlines insulators

Aerosol particles deposition on overhead power line insulators may significantly threaten electric systems, affecting dielectric properties and potentially causing surface discharges and service interruptions, leading to economic consequences. This study addresses this challenge by evaluating a dedicated microscale deposition model that integrated into an advanced air quality (AQ) modeling system. The microscale model integration into the AQ system aims to improve predictions of equivalent salt deposit density (ESDD), a key measure of insulator contamination. This enhancement is crucial for improving system resilience and reliability. The microscale model’s performance in reconstructing particulate matter (PM) deposition fluxes onto XP-70 porcelain-disk electrical insulator is assessed against measured ESDD data collected across various regions in Italy during several experimental campaigns. In order to better understand the specific phenomena impacting PM deposition, the microscale model is compared to the state-of-the-art Zhang deposition model used in CAMx. The analysis reveals that the microscale model tends to overestimate measured ESDD values and predicts higher deposition velocities than the Zhang model. These results suggest potential modelling differences between the two models, including variations in obstacle parameterizations, influence of particle aerodynamics, and dispersion modelling in the near-wall region.

Comparative Analysis of Deep Learning Models for Building Extraction from High-resolution Satellite Imagery

In this research, an approach to extract buildings from Google's satellite imagery was proposed. The performances of various deep learning models (U-Net, RIU-Net, U-Net++, Res-U-Net, and DeepLabV3+) on pre-processed datasets were compared. The models were trained using the similarity metrics of Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). The best-performing models among the segmentation techniques were Res-U-Net and DeepLabV3+. Res-U-Net, an enhanced version of the traditional U-Net model that incorporates residual connections for improved feature propagation, achieved an F1 score of 85.43% when using the RGB dataset. Similarly, DeepLabV3+ also achieved high performance on the Enhanced RGB dataset, obtaining an F1 score of 85.18% after applying pre-processing techniques. This research highlights the significance of color as a dominant feature for accurate building extraction from satellite images. The findings contribute to improved methodologies for building identification, benefiting urban planning, and disaster management applications.

Comparative analysis of fingerprint-based models for indoor visible light positioning: superior performance of ANN over KNN and DNN

Comparative analysis of fingerprint-based models for indoor visible light positioning: superior performance of ANN over KNN and DNN

A Comparative Analysis of Models for AAV-Mediated Gene Therapy for Inherited Retinal Diseases.

Inherited retinal diseases (IRDs) represent a diverse group of genetic disorders leading to progressive degeneration of the retina due to mutations in over 280 genes. This review focuses on the various methodologies for the preclinical characterization and evaluation of adeno-associated virus (AAV)-mediated gene therapy as a potential treatment option for IRDs, particularly focusing on gene therapies targeting mutations, such as those in the RPE65 and FAM161A genes. AAV vectors, such as AAV2 and AAV5, have been utilized to deliver therapeutic genes, showing promise in preserving vision and enhancing photoreceptor function in animal models. Despite their advantages-including high production efficiency, low pathogenicity, and minimal immunogenicity-AAV-mediated therapies face limitations such as immune responses beyond the retina, vector size constraints, and challenges in large-scale manufacturing. This review systematically compares different experimental models used to investigate AAV-mediated therapies, such as mouse models, human retinal explants (HREs), and induced pluripotent stem cell (iPSC)-derived retinal organoids. Mouse models are advantageous for genetic manipulation and detailed investigations of disease mechanisms; however, anatomical differences between mice and humans may limit the translational applicability of results. HREs offer valuable insights into human retinal pathophysiology but face challenges such as tissue degradation and lack of systemic physiological effects. Retinal organoids, on the other hand, provide a robust platform that closely mimics human retinal development, thereby enabling more comprehensive studies on disease mechanisms and therapeutic strategies, including AAV-based interventions. Specific outcomes targeted in these studies include vision preservation and functional improvements of retinas damaged by genetic mutations. This review highlights the strengths and weaknesses of each experimental model and advocates for their combined use in developing targeted gene therapies for IRDs. As research advances, optimizing AAV vector design and delivery methods will be critical for enhancing therapeutic efficacy and improving clinical outcomes for patients with IRDs.

Comparative Analysis of Machine Learning Models for Accurate Flight Price Prediction

Flight fare prediction is a vital component in helping consumers make informed decisions regarding travel expenses. Airline ticket prices fluctuate due to a variety of factors such as demand, time of purchase, and flight routes. In this research, we propose a machine learning-based solution for predicting flight fares using historical data. Models like Random Forest, Gradient Boosting, and Support Vector Machines (SVM) are employed to analyze flight data and produce reliable predictions. This study demonstrates how predictive models can benefit customers by offering insights into pricing trends, thus optimizing their flight booking process.