Comparative Analysis Of Models Research Articles

Enhancing estuary salinity prediction: A Machine Learning and Deep Learning based approach

As critical transitional ecosystems, estuaries are facing the increasingly urgent threat of salt wedge intrusion, which impacts their ecological balance as well as human-dependent activities. Accurately predicting estuary salinity is essential for water resource management, ecosystem preservation, and for ensuring sustainable development along coastlines. In this study, we investigated the application of different machine learning and deep learning models to predict salinity levels within estuarine environments. Leveraging different techniques, including Random Forest, Least-Squares Boosting, Artificial Neural Network and Long Short-Term Memory networks, the aim was to enhance the predictive accuracy in order to better understand the complex interplay of factors influencing estuarine salinity dynamics. The Po River estuary (Po di Goro), which is one of the main hotspots of salt wedge intrusion, was selected as the study area. Comparative analyses of machine learning models with the state-of-the-art physics-based Estuary box model (EBM) and Hybrid-EBM models were conducted to assess model performances. The results highlighted an improvement in the machine learning performance, with a reduction in the RMSE (from 4.22 psu obtained by physics-based EBM to 2.80 psu obtained by LSBoost-Season) and an increase in the R2 score (from 0.67 obtained by physics-based EBM to 0.85 by LSBoost-Season), computed on the test set. We also explored the impact of different variables and their contributions to the predictive capabilities of the models. Overall, this study demonstrates the feasibility and effectiveness of ML-based approaches for estimating salinity levels due to salt wedge intrusion within estuaries. The insights obtained from this study could significantly support smart management strategies, not only in the Po River estuary, but also in other location.

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification.

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

Predicting Car Rental Prices: A Comparative Analysis of Machine Learning Models

In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers.

Comparative analysis of intelligent models for predicting compressive strength in recycled aggregate concrete

Comparative analysis of intelligent models for predicting compressive strength in recycled aggregate concrete

Deflagration Dynamics of Methane–Air Mixtures in Closed Vessels at Elevated Temperatures

In this paper, we explore the deflagration combustion of methane–air mixtures through both experimental and numerical analyses. The key parameters defining deflagration combustion dynamics include maximum explosion pressure (Pmax), maximum rate of explosion pressure rise (dP/dt)max, deflagration index (KG), and laminar burning velocity (SU). Understanding these parameters enhances the process of safety design across the energy sector, where light-emissive fuels play a crucial role in energy transformation. However, most knowledge on these parameters comes from experiments under standard conditions (P = 1 bar, T = 293.15 K), with limited data on light hydrocarbon fuels at elevated temperatures. Our study provides new insights into methane–air mixture deflagration dynamics at temperatures ranging from 293 to 348 K, addressing a gap in the current process industry knowledge, especially in gas and chemical engineering. We also conduct a comparative analysis of predictive models for the laminar burning velocity of methane mixtures in air, including the Manton, Lewis, and von Elbe, Bradley and Mitcheson, and Dahoe models, alongside various chemical kinetic mechanisms based on experimental findings. Notably, despite their simplicity, the Bradley and Dahoe models exhibit a satisfactory predictive accuracy when compared with numerical simulations from three chemical kinetic models using Cantera v. 3.0.0 code. The findings of this study enrich the fundamental combustion data for methane mixtures at elevated temperatures, vital for advancing research on natural gas as an efficient “bridge fuel” in energy transition.

A comparative analysis of consumer credit risk models in Peer-to-Peer Lending

PurposeThe purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.Design/methodology/approachThe author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.FindingsThe results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.Originality/valueThe main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

Comparative Analysis of Large Language Models in Emergency Plastic Surgery Decision-Making: The Role of Physical Exam Data.

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI's ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model's performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.

Predicting Credit Outlook of Banking and Nonbanking Finance Companies: A Comparative Analysis of Machine Learning Models

Predicting Credit Outlook of Banking and Nonbanking Finance Companies: A Comparative Analysis of Machine Learning Models

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.

МЕЖДУНАРОДНЫЙ ОПЫТ УПРАВЛЕНИЯ СИСТЕМОЙ ВЫСШЕГО ОБРАЗОВАНИЯ И ЕГО ЗНАЧИМОСТЬ ДЛЯ РОССИИ

This article is devoted to the analysis of modern trends in the management of the higher education system. It will examine the experience of foreign countries in managing the higher education system in the context of each individual country, as well as its significance for the Russian Federation. A comprehensive analysis of the international experience in the management of higher education systems was carried out. For the first time, a comparative analysis of management models in different countries has been carried out, which allows us to identify general trends and specific features of the management of the higher education system.

IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster

Water quality parameters such as dissolved oxygen, pH, and mineral content are important factors for aquaculture. Predictive analytics can predict water conditions in aquaculture and significantly reduce the mortality probability of aquaculture products. This paper applied stream predictive analytics to the freshwater lobster farming dataset where its real-time data supplied by End Node Unit (ENU) which integrated with dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The real-time data of ENU in Structured Query Language (SQL) is normally displayed for remote monitoring and the analytics will only be done after in different processing platform called batch analytics. Instead of batch, this paper demonstrates capability of stream analytics where the real-time data query from ENU streaming through Structured Query Language (SQL) right into R Studio and Autoregressive Integrated Moving Average (ARIMA) predictions executed on the query table simultaneously on the same processing platform. Previously, ARIMA, Neural Network Autoregressive (NNETAR), and Naïve Bayes, were run and evaluated in R Studio to identify the best algorithm for stream analytics. Prediction procedure in R studio start with importing real-time data stored in SQL database and stream into R Studio using command of “dbGetQuery(con,sql)”. These three models evaluated the performance of freshwater lobster water conditions, dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The data was collected for six months, and 70% was used as training data and 30% as test data. Compared to NNETAR and Naïve Bayes, ARIMA fits the entire data set well for 7 days; the ARIMA model exhibited lower absolute errors for pH and electrical conductivity, with errors ranging from 0.04 to 1.7 across days, while the NNETAR model had generally lower errors for TDS, with errors ranging from 0.3 to 0.7; however, the Naïve Bayes model's performance varied, with the lowest error for DO on day (5) 0.15 but higher errors for other parameters and days, including the highest error for electrical conductivity on day (6) 6.2. In conclusion, the average absolute errors for DO, pH, EC, and TDS are 0.163, 0.064, 0.705, and 0.498, respectively. Our findings underscore the efficacy of ARIMA for comprehensive water quality via stream prediction while highlighting the nuanced strengths and weaknesses of each model in forecasting specific parameters. This study contributes to the aquaculture literature by providing a nuanced comparative analysis of predictive models tailored to freshwater lobster farming, emphasizing the imperative role of stream predictive modelling. It enables real-time monitoring of water quality parameters, ensuring prompt interventions to maintain optimal conditions, thereby minimizing risks, enhancing aquaculture productivity, and ultimately contributing to sustainable and efficient freshwater lobster farming practices.

Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language

Despite the widespread use of Malay, under-resourced languages like Malay face challenges in Natural Language Processing (NLP), particularly in Part-of-Speech (POS) tagging. The scarcity of annotated corpora poses a primary obstacle to POS tagging in Malay. This study aims to enhance the effectiveness and reliability of POS tagging models explicitly tailored for under-resourced languages within the field of NLP, focusing on Malay. Existing models, which rely on Conditional Random Fields and Hidden Markov Models, exhibit limitations, underscoring the need for more robust approaches. The research conducts a comparative analysis of various deep-learning models with different encoders for POS tagging in Malay sentences. The experimental analysis demonstrates that the Bidirectional Long Short-Term Memory (Bi-LSTM) model, leveraging a pre-trained Bidirectional Encoder Representations from Transformers (BERT) embedding model, achieves exceptional accuracy, precision, recall, and F1 scores in predicting tags. Notably, the BERT + Bi-LSTM model, boasting an accuracy of 98.82%, outperforms other models, showcasing superior performance across all evaluated metrics. Additionally, this combined model effectively handles known and unknown words, yielding highly accurate POS tagging results for Malay sentences. Doi: 10.28991/HIJ-2024-05-02-04 Full Text: PDF

Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach

<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.</p>

Comparative Analysis of Algebraic Models of Laminar-Turbulent Transition

Comparative Analysis of Algebraic Models of Laminar-Turbulent Transition

Comparative Analysis of Univariate SARIMA and Multivariate VAR Models for Time Series Forecasting: A Case Study of Climate Variables in Ninahvah City, Iraq

This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature (T) in Ninahvah City, Iraq. Both univariate and multivariate time series approaches are employed to model these series. The paper also outlines the implementation of vector autoregressive, structural vector autoregressive, and structural vector error correction models using the 'vars' package. Additionally, it provides functions for diagnostic testing, estimation of constrained models, prediction, causality analysis, impulse response analysis, and forecast error variance decomposition. Furthermore, it introduces three fundamental functions, VAR, SVAR, and SVEC, for estimating these models. The comparison between the methods is based on evaluating the mean error produced by each approach. The findings of the study indicate that univariate linear stationary methods outperform multivariate models. The analysis of the data was carried out using the R software platform. The primary objective is to assess the performance of univariate and multivariate time series models in handling the given data. The research gap lies in the need for a comparative evaluation of SARIMA and VAR methods for time series analysis in the context of monthly environmental variables. These models were chosen due to their effectiveness in capturing temporal dependencies and interactions among multiple variables in time series data, providing a comprehensive analysis of climatic patterns in Ninahvah City, Iraq. The study aims to address the research gap by comparing these models and justifying their selection based on their capabilities to analyze the specified time series data.

Comparative analysis of HEC-HMS and SWAT hydrological models for simulating the streamflow in sub-humid tropical region in India.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash-Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions.

Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets

Lung cancer prediction is crucial for early detec-tion and treatment, and explainable AI models have gained attention for their interpretability. This study aims to compare various explainable AI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, prepro-cessed, and used to train models such as Logistic Regression, SVC-Linear, SVC-rbf, Decision Tree, Random Forest, AdaBoost Classifier, and XGBoost Classifier. Preliminary results indicate that Random Forest achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer.

Optimizing Concession Agreement Terms and Conditions: Stakeholder Interest Alignment in the Petrochemical Sector

This article is devoted to the examination of models and the selection of optimal parameters for concession agreements pertaining to construction and operation projects within the pipeline infrastructure of the petrochemical sector. Pipelines are underscored as capital-intensive assets crucial for the organization of complex petrochemical production processes. These processes play a vital role in generating added value, tax revenue, employment opportunities, and fostering territorial development while upholding environmental quality standards. This study aims to ascertain the economic parameters of concession agreements, with a focus on achieving a balance of economic interests between the government and businesses. Through a comparative analysis of fundamental economic and mathematical models of concession agreements, the authors model economic parameters to determine the government’s share in investments and concession fees concerning pipeline projects. Subsequently, an oil product pipeline project is discussed as a case study. The results gleaned from this analysis can be harnessed to optimize the parameters of concession agreements and enhance the economic efficiency of project implementation. Economically viable parameters not only facilitate the execution of concession agreements but also foster the generation of added value, social benefits, and environmental oversight, thus aligning with the principles of sustainable development.

A Comparative Analysis of ARX and ANFIS Models for Tumor Growth Prediction Under Single and Multi-agent Chemotherapy.

Despite the advances in oncology and cancer treatment over the past decades, cancer remains one of the deadliest diseases. This study focuses on further understanding the complex nature of cancer by using mathematical tumor modeling to understand, capture as best as possible, and describe its complex dynamics under chemotherapy treatment. Focusing on autoregressive with exogenous inputs, i.e., ARX, and adaptive neuro-fuzzy inference system, i.e., ANFIS, models, this work investigates tumor growth dynamics under both single and combination anticancer agent chemotherapy treatments using chemotherapy treatment data on xenografted mice. Four ARX and ANFIS models for tumor growth inhibition were developed, estimated, and evaluated, demonstrating a strong correlation with tumor weight data, with ANFIS models showing superior performance in handling the multi-agent tumor growth complexities. These findings suggest potential clinical applications of the ANFIS models through further testing. Both types of models were also tested for their prediction capabilities across different chemotherapy schedules, with accurate forecasting of tumor growth up to five days in advance. The use of adaptive prediction and sliding (moving) data window techniques allowed for continuous model updating, ensuring more robust predictive capabilities. However, long-term forecasting remains a challenge, with accuracy declining over longer prediction horizons. While ANFIS models showed greater reliability in predictions, the simplicity and rapid deployment of ARX models offer advantages in situations requiring immediate approximations. Future research with larger, more diverse datasets and by exploring varying model complexities is recommended to improve the models' reliability and applicability in clinical decision-making, thereby aiding the development of personalized chemotherapy regimens.

Comparative Analysis of VGG16, RESNET50, AND CNN Models for Lung Disease Prediction: A Deep Learning Approach

Abstract: Utilizing deep learning methods in medical image analysis has shown promise in enhancing disease detection and diagnosis. In this study, we conducted a detailed comparative analysis of threewidely recognized convolutional neural network (CNN) architectures: VGG16, ResNet50, and a custom CNN model tailored for pneumonia and COVID-19 detection from chest X-ray images. Leveraging transfer learning techniques and meticulously curated datasets, we evaluated the models' performance in accurately identifying respiratory diseases. Our investigation utilized two publicly available datasets, the ChestX-ray14 dataset and the NIH Chest X-ray Dataset, both annotated for pneumonia and COVID-19. Prior to model training, we conducted thorough preprocessing to ensure optimal data quality and consistency. Through rigorous experimentation, we assessed the models' accuracy, sensitivity, and specificity in disease detection. The results revealed nuanced differences in model performance across disease categories. While VGG16 demonstrated robust accuracy in pneumonia detection, ResNet50 exhibited enhanced sensitivity and specificity in identifying COVID-19 cases. Our custom CNN model, leveraging insights from both architectures, showcased competitive performance, emphasizing the importance of tailored model design for optimal diagnostic outcomes. Through comprehensive analysis and discussion, we elucidated the strengths and limitations of each model, considering factors such as computational efficiency, interpretability, and generalizability. Our findings underscore the potential of deep learningbased diagnostic tools in supporting healthcare professionals in timely and accurate disease diagnosis