Comparative Modeling Study Research Articles

A comparative study of factor models for different periods of the electricity spot price market

Due to major shifts in the European energy supply, a structural change can be observed in Austrian electricity spot price data starting from the second quarter of the year 2021 onward. In this work, we study the performance of two different factor models for the electricity spot price in three different time periods. To this end, we consider three samples of EEX data for the Austrian base load electricity spot price, one from the pre-crisis from 2018 to 2021, the second from the time of the crisis from 2021 to 2023, and the whole data from 2018 to 2023. For each of these samples, we investigate the fit of a classical 3-factor model with a Gaussian base signal and one positive and one negative jump signal and compare it with a 4-factor model to assess the effect of adding a second Gaussian base signal to the model.For the calibration of the models, we develop a tailor-made Markov Chain Monte Carlo method based on Gibbs sampling. To evaluate the model adequacy, we provide simulations of the spot price as well as a posterior predictive check for the 3- and the 4-factor model. We find that the 4-factor model outperforms the 3-factor model in times of non-crises. In times of crisis, the second Gaussian base signal does not lead to a better fit of the model. To the best of our knowledge, this is the first study regarding stochastic electricity spot price models in this new market environment. Hence, it serves as a solid base for future research.

Comparative Analysis of Machine Learning Algorithms: KNN, SVM, Decision Tree and Logistic Regression for Efficiency and Performance

This paper presents a comparative study of machine learning models namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression on two datasets: 20Newsgroups and Wine datasets. Based on basic accuracy, and precision, and recall of the models, F1 measures were assessed. When compared with other classifiers such as KNN and Decisions Trees, SVM and Logistic regression gave better results especially in the case of the 20Newsgroups dataset dominated by textual high dimensional data. KNN had poor recall results and Decision Tree was moderate. In the Wine dataset, since the structure of data is comparatively less complex in our context then all the models yielded almost similar results with accuracy and precision factors very close to 1.0 which of course manifested that the choice of the model does not affect much on simple data. These results stress the importance of the choice of the appropriate model for tasks with a certain level of data complexity; detailed models show the greatest efficiency at the accomplishment of complicated tasks, though at the same time, they are not required for simple, structured data.

Modelling and Forecasting of Sugarcane at Aggregate and Province Level of Pakistan: A Comparative Study of Hybrid Model and Machine Learning Techniques

The study aimed to choose a more appropriate model, among others, for forecasting the annual production of sugarcane at the provincial and country levels of Pakistan. An ARIMA model with machine learning methods, i.e., and a Hybrid model (BRNN-ARIMA), have been used. The MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) have been used to compare the best forecasting method. The results conclude that the hybrid model (BRNN-ARIMA) is the best forecasting method compared to other models. Moreover, the study concludes that there was a long-term relationship between sugar production and inputs. Given the availability of cheaper labour in Pakistan, utilizing more labour in the agricultural sector could positively impact production and contribute to achieving the Sustainable Development Goals (SDGs). Policymakers must invest in agriculture machinery and increase labour employment to increase sugar production in Pakistan.

A Comparative Study of Swin Transformer and CNN Models for Crowd Counting

Abstract. Crowd counting, a critical component in the management and safety planning of large gatherings and public spaces, is essential for ensuring smooth event operations and preventing potential overcrowding issues. While the standard convolutional neural network (CNN) based model performs well in head counting tasks, it has certain drawbacks when applied to complex scenarios. With the rapid development of artificial intelligence, Transformer models that rely on self-attention mechanisms, as Swin Transformer, have demonstrated exceptional performance in visual tasks, such as image classification, and segmentation in recent times. This study examines the experimental findings of Swin Transformer's head counting tasks and contrasts them with the CNN-based model. Mean Absolute Error (MAE) and Mean Square Error (MSE) evaluation indicators show that the Transformer model outperforms the classic CNN model in terms of generalization abilities when dealing with complicated scenarios. Future research work will increase the diversity of data sets and focus on optimizing model structure and improving training efficiency.

Comparative Study of CALPUFF and CFD Modeling of Toxic Gas Dispersion in Mountainous Environments

Comparative Study of CALPUFF and CFD Modeling of Toxic Gas Dispersion in Mountainous Environments

A Comprehensive Comparative Study of Intuitive Physics Modeling in Machine Learning Trained with Cartoon and Realistic Data

Abstract. This study delves into the influence of training data typesspecifically cartoon versus realistic visual datasetson the development of intuitive physics modeling in machine learning. Intuitive physics, the inherent human ability to understand and predict the physical properties and dynamics of objects, presents a significant challenge for current AI systems to replicate accurately. Leveraging YOLOv5, a cutting-edge object detection model, this research systematically evaluates the cognitive understanding and performance of AI models trained on distinct types of visual data. The findings reveal that the visual complexity inherent in the training datasets plays a crucial role in shaping the model's ability to generalize and accurately perform intuitive physics tasks. Models trained on cartoon datasets exhibited different learning patterns and generalization capabilities compared to those trained on realistic data, providing valuable insights into the role of data representation in AI training. This research offers both theoretical advancements in understanding AI's cognitive limitations and practical guidance for designing AI systems that can interact with the physical world more effectively. Ultimately, the study contributes to bridging the gap between human cognition and machine learning, pushing the boundaries of what AI can achieve in modeling complex, real-world phenomena.

Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models

Abstract: Employee turnover is a significant issue for financial institutions, impacting productivity, increasing recruitment costs, and disrupting critical operations. In this project, this study aimed to predict employee turnover using a dataset containing attributes such as employee satisfaction, performance, and tenure. By framing the task as a binary classification problem, this study employed CatBoost and XGBoost, two advanced regression-based algorithms, to develop predictive models. This paper's analysis demonstrated that CatBoost outperformed XGBoost across all evaluation metrics, including MAE, MSE, RMSE, and R, making it the more effective model for predicting turnover in the financial sector. The study highlights key factors contributing to employee attrition, such as job satisfaction, tenure, and promotion opportunities, offering actionable insights for retention strategies. Additionally, by predicting probabilities rather than binary outcomes, this study aims to make more detailed decisions about employee retention. This research provides valuable tools for financial institutions to mitigate the risk of turnover, retain critical talent, and ensure operational continuity.

Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies

Article Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies Heng Zhao 1, Like Qian 1, Yaqi Zhu 1 and Dingcheng Tian 1,2,∗ 1 Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo 315211, China 2 College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110016, China ∗ Correspondence: 2310520@stu.neu.edu.cn Received: 8 August 2024; Revised: 10 October 2024; Accepted: 14 October 2024; Published: 5 November 2024 Abstract: Low-dose computed tomography (LDCT) denoising is an important topic in CT image research. Compared with normal-dose CT images, LDCT can reduce the radiation dose of X-rays, decreasing the radiation burden on the human body, which is beneficial to human health. However, quantum noise caused by low-dose rays will reduce the quality of CT images, thereby decreasing the accuracy of clinical diagnosis. In recent years, deep learning-based denoising methods have shown promising advantages in this field. Researchers have proposed some optimized models for low-dose CT image denoising. These methods have enhanced the application of low-dose CT image denoising from different aspects. From the perspective of experimental research, this paper investigates and evaluates some top deep learning models proposed in the field of low-dose image denoising in recent years, with the aim of determining the best models and training strategies for this task. We conducted experiments on seven deep learning models (REDCNN, EDCNN, QAE, OCTNet, UNet, WGAN, CTformer) on the AAPM dataset and the Piglet dataset. Our research shows that UNet has the best denoising effect among the models, obtaining PSNR = 33.06 (AAPM dataset) and PSNR = 31.21 (Piglet dataset), and good generalization capacity is also observed. However, UNet has a large number of parameters, and the time it takes to process an image is about 8 ms, while EDCNN takes about 4.8 ms to process an image, and its average PSNR is ranked second after UNet. EDCNN strikes a balance between denoising performance and processing efficiency, making it ideal for low-dose CT image denoising tasks.

Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

Heterogeneous continuous flow hydrogenation is important in the chemical industry, yet the simultaneous optimization of yield and productivity has historically been complex. This study introduces a heterogeneous continuous flow hydrogenation system specifically designed for preparing 2-amino-3-methylbenzoic acid (AMA), employing FTIR inline analysis coupled with an artificial neural network model for monitoring. We explored two distinct reaction optimization strategies: multi-objective Bayesian optimization (MOBO) and mechanism-based intrinsic kinetic modeling, executed in parallel to optimize reaction conditions. Remarkably, the MOBO approach achieved an optimal AMA yield (99%) and productivity (0.64 g/hour) within a limited number of iterations. In comparison, despite requiring extensive experimental data collection and equation fitting, the intrinsic kinetic modeling approach yielded a similar optimal result. Thus, while MOBO offers a more efficient route with fewer required experiments, kinetic modeling provides deeper insights into the optimization landscape but may be impacted by non-chemical kinetic phenomena and requires significant time and resources.

Exploring the sustainable future of social sports organizations: A comparative study of governance models in China, Japan, and South Korea

Exploring the sustainable future of social sports organizations: A comparative study of governance models in China, Japan, and South Korea

Electric Vehicle Sentiment Analysis Using Large Language Models

Sentiment analysis is a technique used to understand the public’s opinion towards an event, product, or organization. For example, sentiment analysis can be used to understand positive or negative opinions or attitudes towards electric vehicle (EV) brands. This provides companies with valuable insight into the public’s opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performance compared to traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming significant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimised BERT (RoBERTa), and a generalised autoregressive pre-training method (XLNet) using Lucid Motors and Tesla Motors YouTube datasets. Results evidenced that LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically when fine-tuned. Furthermore, our findings present the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with an F1 score of at least 92%.

Urban Building Energy Modeling: A Comparative Study of Process-Driven and Data-Driven Models

Urban Building Energy Modeling: A Comparative Study of Process-Driven and Data-Driven Models

A Comparative Study of Generative AI Models for Interior Design

Abstract: The integration of Generative AI in interior design has transformed traditional methods,allowing designers to explore new concepts with impressive efficiency. This paper presents a comparative study of leading generative models—StyleGAN, Variational Autoencoders (VAEs), Pix2Pix, and Reinforcement Learning (RL)—evaluating their effectiveness in turning sketches into photorealistic renderings, generating diverse room layouts, and optimizing spaces. By analyzing the results of these models, we show their ability to create unique design solutions that meet functional requirements while enhancing aesthetic appeal. The study highlights substantial enhancements in design precision, emphasizing the potential of generative AI models to elevate the design process and create more tailored interior solutions. This survey examines the methods and performance of each model and looksat future possibilities for using Generative AI to advance the field of interior design.

Potential Effects Of Propolis And Vitamin D3 Local Application During Orthodontic Movement: A Comparative Animal Model Study

Potential Effects Of Propolis And Vitamin D3 Local Application During Orthodontic Movement: A Comparative Animal Model Study

Dynamics of Carbon Export and Lower Trophic Ecosystems: A Comparative Model Study in the Yellow Sea and Dokdo Waters

Dynamics of Carbon Export and Lower Trophic Ecosystems: A Comparative Model Study in the Yellow Sea and Dokdo Waters

Comparative Study of XGBoost, Random Forest, and Logistic Regression Models for Predicting Customer Interest in Vehicle Insurance

In today’s competitive insurance market, accurately predicting customer interest in additional products, such as vehicle insurance, is crucial for optimizing marketing strategies and maximizing sales. This study presents a comparative analysis of three machine learning models such as XGBoost, RandomForest, and Logistic Regression to predict customer interest in vehicle insurance based on a dataset that includes demographic, vehicle, and policy-related features. The dataset was analyzed using five-fold cross-validation, and the performance of the models was evaluated using AUC-ROC, precision, recall, and F1-score. XGBoost demonstrated the highest recall (0.9525) and AUC-ROC (0.7854), making it the most effective model for identifying customers interested in vehicle insurance, though at the expense of lower precision (0.2585). RandomForest showed a more balanced trade-off between precision (0.3064) and recall (0.5341) but performed lower overall. Logistic Regression, while the most interpretable model, exhibited high variability in performance across different folds, with a lower average precision (0.2372). The findings of this research highlight that XGBoost is ideal for maximizing recall in high-volume campaigns, while RandomForest may be better suited for applications requiring fewer false positives. These results offer valuable insights into model selection based on business objectives and resource allocation.

Automating public policy: a comparative study of conversational artificial intelligence models and human expertise in crafting briefing notes

Automating public policy: a comparative study of conversational artificial intelligence models and human expertise in crafting briefing notes

Comparative study of CNN, LSTM and hybrid CNN-LSTM model in amazigh speech recognition using spectrogram feature extraction and different gender and age dataset

Comparative study of CNN, LSTM and hybrid CNN-LSTM model in amazigh speech recognition using spectrogram feature extraction and different gender and age dataset

Evaluating SAFE AI principles using Wasserstein distance: a comparative study of Machine Learning models

Modern data-driven Artificial Intelligence (AI), powered by advanced Machine Learning (ML) models, is transforming financial technologies by enhancing financial inclusion, transparency and reducing transaction costs. However, the opaque nature of some complex ML models requires new statistical approaches to manage risks and ensure trustworthiness. In this paper, we present a novel method to evaluate the key principles of trustworthy AI - Sustainability (Robustness), Accuracy, Fairness, and Explainability (SAFE). While Babaei et al. [A Rank Graduation Box for SAFE AI. Expert Syst Appl. 259;2025:125239, 2025] introduced the Rank Graduation Box as a streamlined approach for assessing the principles of trustworthy AI, we extend this work by employing the Wasserstein distance. Our method offers a more nuanced and geometrically oriented comparison of ML models, particularly in contexts where shifts in economic or environmental conditions alter the prediction distributions. We apply this method to compare popular ML models, including Support Vector Machines, Ensemble Trees, K-Nearest Neighbours Linear and Logistic Regression. The proposal is validated using both simulated data and real-world data in the context of financial risk assessment. Our findings demonstrate that the Wasserstein distance offers nuanced and interpretable insights into model behaviour across the SAFE dimensions, making it a valuable tool for model selection and regulatory compliance in AI applications.

Evaluating vision-capable chatbots in interpreting kinematics graphs: a comparative study of free and subscription-based models

This study investigates the performance of eight large multimodal model (LMM)-based chatbots on the Test of Understanding Graphs in Kinematics (TUG-K), a research-based concept inventory. Graphs are a widely used representation in STEM and medical fields, making them a relevant topic for exploring LMM-based chatbots’ visual interpretation abilities. We evaluated both freely available chatbots (Gemini 1.0 Pro, Claude 3 Sonnet, Microsoft Copilot, and ChatGPT-4o) and subscription-based ones (Gemini 1.0 Ultra, Gemini 1.5 Pro API, Claude 3 Opus, and ChatGPT-4). We found that OpenAI’s chatbots outperform all the others, with ChatGPT-4o showing the overall best performance. Contrary to expectations, we found no notable differences in the overall performance between freely available and subscription-based versions of Gemini and Claude 3 chatbots, with the exception of Gemini 1.5 Pro, available via API. In addition, we found that tasks relying more heavily on linguistic input were generally easier for chatbots than those requiring visual interpretation. The study provides a basis for considerations of LMM-based chatbot applications in STEM and medical education, and suggests directions for future research.