Comparative Modeling Study Research Articles

Artificial Intelligence in Academic Translation: A Comparative Study of Large Language Models and Google Translate

Purpose. The advent of Large Language Model (LLM), a generative artificial intelligence (AI) model, in November 2022 has had a profound impact on various domains, including the field of translation studies. This motivated this study to conduct a rigorous evaluation of the effectiveness and precision of machine translation, represented by Google Translate (GT), in comparison to Large Language Models (LLMs), specifically ChatGPT 3.5 and 4, when translating academic abstracts bidirectionally between English and Arabic. 
 Methods. Employing a mixed-design approach, this study utilizes a corpus comprising 20 abstracts sourced from peer-reviewed journals indexed in the Clarivate Web of Science, specifically the Journal of Arabic Literature and Al-Istihlal Journal. The abstracts are equally divided to represent both English-Arabic and Arabic-English translation directionality. The study’s design is rooted in a comprehensive evaluation rubric adapted from Hurtado Albir and Taylor (2015), focusing on semantic integrity, syntactic coherence, and technical adequacy. Three independent raters carried out assessments of the translation outputs generated by both GT and LLM models. 
 Results. Results from quantitative and qualitative analyses indicated that LLM tools significantly outperformed MT outputs in both Arabic and English translation directions. Additionally, ChatGPT 4 demonstrated a significant advantage over ChatGPT 3.5 in Arabic-English translation, while no statistically significant difference was observed in the English-Arabic translation directionality. Qualitative analysis findings indicated that AI tools exhibited the capacity to comprehend contextual nuances, recognize city names, and adapt to the target language's style. Conversely, GT displayed limitations in handling specific contextual aspects and often provided literal translations for certain terms.

Enhancing Economic Development through Inventory Management Optimization in Agro-based Industries in Bihar: A Comparative Study of EOQ and EPQ Models

Inventory management is crucial for optimizing operational efficiency and cost-effectiveness in agro-based industries, particularly in regions like Bihar,known for their agricultural resources. This study compares the effectiveness of two inventory management models, the Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ), in addressing the unique challenges faced by Bihar's agro-based industries. Data from Taruwar Agro Industries Private Limited in Ashiyana, Patna, use to calculate EOQ and EPQ values for three product categories: Tomato Chili, Peri Peri, and Punjabi Tadka. The study employs relevant formulas to compute optimal order quantities and total inventory costs, assuming constant demand, quality, price, holding costs, and ordering costs. Both the EOQ and EPQ models offered viable approaches to inventory optimization. EOQ focused on minimizing holding and ordering costs, while EPQ integrated production-related costs to optimize production flow. The study underscored the importance of aligning inventory management strategies with operational capabilities, market demand, and financial objectives. This study contributes to advancing theoretical understanding and practical applications of inventory management in agro-based economies like Bihar. Recommendations for empirical validation, contextual adaptation, technology integration, and long-term impact assessment are provided to enhance inventory management practices. Integration of the BCG matrix into decision-making processes offers a comprehensive approach to prioritizing inventory management strategies and guiding strategic implications.

A Comparative Study of Transformer-based Models for Text Summarization of News Articles

Transformer-based models such as GPT, T5, BART, and PEGASUS have made substantial progress in text summarization, a sub-domain of natural language processing that entails extracting important information from lengthy texts. The main objective of this research was to conduct a comparative analysis of these four transformer-based models based on their performance in text summarization of news articles. In achieving this objective, the transformer models pre-trained on extensive datasets were fine-tuned on the CNN/DailyMail dataset using a low learning rate to preserve the learned representations. The T5 transformer records the highest scores of 35.12, 22.75, 32.82, and 28.59 in ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum respectively, surpassing GPT, BART, and PEGASUS across all ROUGE metrics. The findings deduced from this study establish the proficiency of encoder-decoder models such as T5 in summary generation. Furthermore, the findings also demonstrated that the fine-tuning process's effectiveness in pre-trained models is improved when the pre-training objective closely aligns with the downstream task.

Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction

This research presents a comprehensive investigation into the accurate estimation of shear strength in rectangular reinforced concrete columns through advanced machine learning (ML) models. The study addresses the intricate challenge posed by shear strength complexity, which is crucial for evaluating column stability and ensuring structural integrity. Building upon a substantial dataset comprising 545 experimental observations sourced from diverse literature, this research establishes a robust foundation for predictive modeling. Four distinct ML regression models, Random Forest, Decision Tree, XGBoost, and LightGBM, are meticulously evaluated for their performance. The evaluation employs established metrics, including R2, RMSE, MAE, and MAPE to quantify their predictive capabilities. The outcomes highlight the models' robustness in capturing nuanced variations in shear strength, with impressive R2 values ranging from 93.6% to 93.9%, showcasing their exceptional ability to elucidate intricate shear behaviors. Furthermore, comparative analysis indicates the slightly superior performance of the Random Forest over the Decision Tree, highlighting the efficacy of ensemble methods in this context. Extending the exploration to include XGBoost and LightGBM, the study showcases their potential as accurate shear strength predictors. The performance of the models is validated through scatter plots and error distribution plots, confirming accurate shear strength predictions across various scenarios. This research contributes significantly to the advancement of structural engineering methodologies by highlighting the potential of ML to improve the accuracy of shear strength estimation. The findings not only underscore the exceptional performance of ML models but also provide valuable insights into their comparative effectiveness, paving the way for enhanced structural assessments in columns.

A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis

A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis

Comparative study of robust optimization model of multimaterial emergency logistics in an uncertain environment

AbstractGiven the significance of objective functions in alleviating human suffering within the humanitarian relief supply chain, it is imperative to consider both logistics costs and the cost of deprivation as ideal targets. This study investigates a multimaterial single‐cycle stochastic mixed integer model, integrating uncertainties about post‐disaster demand, supply survival, and road networks. An innovative heuristic approach is used to solve the stochastic facility location problem, which minimizes unfulfilled requests and response times in lexicographic order. This research also compares the cost‐minimization model of the pre‐positioning problem to an alternative model that maximizes demand satisfaction under resource constraints. Empirical validation through numerous case studies and solutions confirms the effectiveness and feasibility of the alternative model. The results of our experiments consistently demonstrate that the best solution provided by the alternative model not only satisfies the minimum cost model but also surpasses the conventional problem, without compromising performance. Consequently, it is crucial to avoid the cost‐minimization approach at the expense of human suffering.

A Comparative Study of Large Language Models, Human Experts, and Expert-Edited Large Language Models to Neuro-Ophthalmology Questions.

While large language models (LLMs) are increasingly used in medicine, their effectiveness compared with human experts remains unclear. This study evaluates the quality and empathy of Expert + AI, human experts, and LLM responses in neuro-ophthalmology. This randomized, masked, multicenter cross-sectional study was conducted from June to July 2023. We randomly assigned 21 neuro-ophthalmology questions to 13 experts. Each expert provided an answer and then edited a ChatGPT-4-generated response, timing both tasks. In addition, 5 LLMs (ChatGPT-3.5, ChatGPT-4, Claude 2, Bing, Bard) generated responses. Anonymized and randomized responses from Expert + AI, human experts, and LLMs were evaluated by the remaining 12 experts. The main outcome was the mean score for quality and empathy, rated on a 1-5 scale. Significant differences existed between response types for both quality and empathy (P < 0.0001, P < 0.0001). For quality, Expert + AI (4.16 ± 0.81) performed the best, followed by GPT-4 (4.04 ± 0.92), GPT-3.5 (3.99 ± 0.87), Claude (3.6 ± 1.09), Expert (3.56 ± 1.01), Bard (3.5 ± 1.15), and Bing (3.04 ± 1.12). For empathy, Expert + AI (3.63 ± 0.87) had the highest score, followed by GPT-4 (3.6 ± 0.88), Bard (3.54 ± 0.89), GPT-3.5 (3.5 ± 0.83), Bing (3.27 ± 1.03), Expert (3.26 ± 1.08), and Claude (3.11 ± 0.78). For quality (P < 0.0001) and empathy (P = 0.002), Expert + AI performed better than Expert. Time taken for expert-created and expert-edited LLM responses was similar (P = 0.75). Expert-edited LLM responses had the highest expert-determined ratings of quality and empathy warranting further exploration of their potential benefits in clinical settings.

Comparative study of physics-based model and machine learning model for epidemic forecasting and countermeasure

Comparative study of physics-based model and machine learning model for epidemic forecasting and countermeasure

Comparative study of machine learning models for predicting the compressive strength of concrete using Non-Destructive Testing methods

This study proposes and compares the conventional machine learning (CML) models as artificial neural networks (ANN), support vector machines (SVM) and ensemble machine learning (EML) models namely, random forest (RF), and gradient boosting (GB). These four-machine learning (ML) models were used to predict the compressive strength (CS) of concrete based on two non-destructive testing (NDT) methods, namely ultrasonic pulse velocity (UPV) and rebound hammer number (RHN). The article uses a large and reliable database of experimental data, collected from the literature to train and test the models, and evaluates their performance using various metrics, scatter plots and a Taylor diagram. It was found that gradient boosting (GB) is the most accurate and robust model for this study, followed by RF model, while ANN and SVM have lower performance. For this study, it suggests that EML models are more suitable and reliable than CML models for predicting the CS of concrete based on NDT methods.

Real-time RSET prediction across three types of geometries and simulation training dataset: A comparative study of machine learning models

Agent-based evacuation models provide useful data of the evacuation process, but they are not primarily designed for use during an emergency. The paper aims to test predicting RSET using a surrogate ML model trained on a simulation dataset with 60 samples. A total of 9 machine learning algorithms were tested on 3 simple geometries: bottleneck, stairway and walkway. A set of 7 spatial features was used to train the surrogate models. The results showed a relatively good ability of Artificial Neural Network to learn in scenarios involving bottlenecks and stairways, with an R2: 0.99 on the testing dataset. In the walkway scenario, all models experienced a significant drop in performance, with Gradient Boost performing the best (R2: 0.92). The paper demonstrated ability to generalize effectively in bottleneck-type tasks with training on a relatively small dataset containing spatial parameters obtainable in real-time from camera systems.

The role of theoretical models in IoT-based irrigation systems: A Comparative Study of African and U.S. Agricultural Strategies for Water Scarcity Management

This review paper presents a comparative study of the theoretical models underpinning the Internet of Things (IoT)-based irrigation systems, focusing on their application in managing water scarcity within agricultural sectors in Africa and the United States. By examining the adaptation and implementation of these models, the paper sheds light on the diverse strategies employed to optimize water use and enhance crop yields in response to the pressing challenge of water scarcity. In Africa, the emphasis is on low-cost, innovative solutions tailored to local conditions and resource constraints, whereas in the U.S., the focus shifts to leveraging advanced technology, scalability, and economic viability. The study underscores the significance of IoT-based irrigation systems in promoting sustainable agriculture and water management practices across different environmental and socio-economic contexts. Through a detailed discussion, the paper reflects on the theoretical and practical implications of these findings for policymakers, farmers, and technology developers, acknowledging the limitations of the current analysis while suggesting directions for future research. The comparative insights highlight the potential of IoT technologies to revolutionize water scarcity management strategies globally, advocating for increased collaboration and innovation in the development of sustainable irrigation solutions.

A comparative study of machine learning regression models for production systems condition monitoring

A comparative study of machine learning regression models for production systems condition monitoring

A comparative study of machine learning-based regression models for supply chain management

The rise of machine learning technology has opened up unprecedented opportunities for the retail industry. Machine learning, as an essential branch of artificial intelligence, enables computers to improve their performance through continuous learning and experience. It has demonstrated its ability to handle large-scale data and complex problems effectively. In retail, machine learning predictions and methods can also lead to significant breakthroughs in supply chain management, helping businesses identify better ways to maintain economic stability and growth, which are crucial for improving people's living standards, eliminating poverty, promoting social stability, driving technological progress, and reducing inequality. This is achieved through different algorithmic regression methods, which can predict future trends and consumer behavior with high accuracy. Machine learning algorithms can analyze vast amounts of data to identify patterns and trends and make accurate predictions about future demand, product inventory levels, and other important factors that drive business success in the retail industry.

Comparative Study of Neural Network Models for Prediction of Chaotic System

Comparative Study of Neural Network Models for Prediction of Chaotic System

Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models.

Phishing is one of the most dangerous attacks targeting individuals, organizations, and nations. Although many traditional methods for email phishing detection exist, there is a need to improve accuracy and reduce false-positive rates. Our work investigates one-dimensional CNN-based models (1D-CNNPD) to detect phishing emails in order to address these challenges. Additionally, further improvement is achieved with the augmentation of the base 1D-CNNPD model with recurrent layers, namely, LSTM, Bi-LSTM, GRU, and Bi-GRU, and experimented with the four resulting models. Two benchmark datasets were used to evaluate the performance of our models: Phishing Corpus and Spam Assassin. Our results indicate that, in general, the augmentations improve the performance of the 1D-CNNPD base model. Specifically, the 1D-CNNPD with Bi-GRU yields the best results. Overall, the performance of our models is comparable to the state of the art of CNN-based phishing email detection. The Advanced 1D-CNNPD with Leaky ReLU and Bi-GRU achieved 100% precision, 99.68% accuracy, an F1 score of 99.66%, and a recall of 99.32%. We observe that increasing model depth typically leads to an initial performance improvement, succeeded by a decline. In conclusion, this study highlights the effectiveness of augmented 1D-CNNPD models in detecting phishing emails with improved accuracy. The reported performance measure values indicate the potential of these models in advancing the implementation of cybersecurity solutions to combat email phishing attacks.

Sentiment analysis of COP9-related tweets: a comparative study of pre-trained models and traditional techniques.

Sentiment analysis has become a crucial area of research in natural language processing in recent years. The study aims to compare the performance of various sentiment analysis techniques, including lexicon-based, machine learning, Bi-LSTM, BERT, and GPT-3 approaches, using two commonly used datasets, IMDB reviews and Sentiment140. The objective is to identify the best-performing technique for an exemplar dataset, tweets associated with the WHO Framework Convention on Tobacco Control Ninth Conference of the Parties in 2021 (COP9). A two-stage evaluation was conducted. In the first stage, various techniques were compared on standard sentiment analysis datasets using standard evaluation metrics such as accuracy, F1-score, and precision. In the second stage, the best-performing techniques from the first stage were applied to partially annotated COP9 conference-related tweets. In the first stage, BERT achieved the highest F1-scores (0.9380 for IMDB and 0.8114 for Sentiment 140), followed by GPT-3 (0.9119 and 0.7913) and Bi-LSTM (0.8971 and 0.7778). In the second stage, GPT-3 performed the best for sentiment analysis on partially annotated COP9 conference-related tweets, with an F1-score of 0.8812. The study demonstrates the effectiveness of pre-trained models like BERT and GPT-3 for sentiment analysis tasks, outperforming traditional techniques on standard datasets. Moreover, the better performance of GPT-3 on the partially annotated COP9 tweets highlights its ability to generalize well to domain-specific data with limited annotations. This provides researchers and practitioners with a viable option of using pre-trained models for sentiment analysis in scenarios with limited or no annotated data across different domains.

Analysis and comparative study of a deterministic mathematical model of SARS-COV-2 with fractal-fractional operators: a case study

In this paper, we investigate a fractal-fractional-order mathematical model with the influence of hospitalized patients and the impact of vaccination with fractal-fractional operators. The respective derivatives are considered in the Caputo, Caputo Fabrizio, and Atangana–Baleanu senses of fractional order α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\alpha $$\\end{document} and fractal dimension τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ au $$\\end{document}. For the proposed problem, some results regarding basic reproduction number and stability are given. Using the next-generation matrix approach, we have investigated the global and local stability of several types of equilibrium points. We provide a detailed analysis of the existence and uniqueness of the solution. Moreover, we fit the model with the real data of Pakistan from June 01, 2020, till March 24, 2021. Then, we use the fractal-fractional derivative to find a numerical solution for the model. MATLAB software is used for numerical illustration. Graphical presentations corresponding to different parameteric values are given as well.

Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets

Dispersion modeling is a useful tool for reproducing the spatial–temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requires a large number of input variables, including meteorological and orographical variables. The reliability of model results depends on the accuracy of these input variables. The present research aims to discuss a comparative study of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic data, and a blend of prognostic and measured data. Two distinct sources (a point and an area source) and two different simulation domains in Cuba and Italy are considered. The analysis of results is based on odor impact criteria enforced in some Italian regions by computing the 98th percentile of odor peak concentrations on an annual basis. For the area source, simulation results reveal that the ‘OBS’ and ‘HYBRID’ modes are largely comparable, whereas prognostic data tend to underestimate the odor concentrations, likely due to a reduced percentage of wind calms. For point sources, different input meteorological settings provide comparable results, with no significant differences.

This study addresses the challenge of intelligent diagnosis in Alzheimer's Disease by employing machine learning to classify MRI images depicting various disease stages. The author conducted a comparative analysis to assess the efficacy of diverse models

This study addresses the challenge of intelligent diagnosis in Alzheimer's Disease by employing machine learning to classify MRI images depicting various disease stages. The author conducted a comparative analysis to assess the efficacy of diverse models for this purpose, yielding several key findings. The primary model utilized in the research is founded on ResNet152V2, encompassing 152 pre-trained layers, 1 GlobalAveragePooling2D layer, 3 dropout layers, 3 dense layers, and 1 output layer. The research aims to achieve precise classification of MRI images corresponding to distinct Alzheimer's Disease stages, a pivotal step for early detection and intervention. To achieve this, this research adjusted the structure of the original model and changed some of the parameters and compared their results. Based on the results obtained, the author drew several conclusions. The author found that setting a larger value for the "patience" parameter in the Early Stopping callback helps avoid stopping the training process too early. Additionally, the author observed that the original model with only 2 fully connected layers was not stable enough in terms of gradient descent, indicating the need for additional fully connected layers. In conclusion, this research paper addressed the problem of classifying MRI images of different stages of Alzheimer's Disease using machine learning techniques. The comparative study of different models provided insights into the performance and potential improvements of the classification taskt.

Predicting Academic Success: A Comparative Study of Machine Learning and Clustering-Based Subject Recommendation Models

The study of students' academic performance is a significant endeavor for higher education schools and universities since it is essential to the design and management of instructional strategies. The efficacy of the current educational system must be monitored by evaluating student achievement. For this research, we used multiple Machine Learning algorithms and Neural Networks to analyze the learning quality. This study investigates the real results of university examinations for B.Tech (Bachelor in Technology) students, a four-year undergraduate programme in Computer Science and Technology. The K-means clustering approach is used to recommend courses, highlighting those that would challenge students and those that will improve their GPA. The Linear Regression method is used to make a prediction of a student’s rank among their batchmates. Academic planners might base operational choices and future planning on the findings of this study.