Journal Description
Computer Sciences & Mathematics Forum
Computer Sciences & Mathematics Forum
is an open access journal dedicated to publishing findings resulting from academic conferences, workshops, and similar events in the area of computer science and mathematics. Each conference proceeding can be individually indexed, is citable via a digital object identifier (DOI), and is freely available under an open access license. The conference organizers and proceedings editors are responsible for managing the peer-review process and selecting papers for conference proceedings.
Latest Articles
Fundamentals of Time Series Analysis in Electricity Price Forecasting
Comput. Sci. Math. Forum 2025, 11(1), 16; https://doi.org/10.3390/cmsf2025011016 - 11 Aug 2025
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Time series forecasting is a cornerstone of decision-making in energy and finance, yet many studies fail to rigorously analyse the underlying dataset characteristics, leading to suboptimal model selection and unreliable outcomes. This paper addresses these shortcomings by presenting a comprehensive framework that integrates
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Time series forecasting is a cornerstone of decision-making in energy and finance, yet many studies fail to rigorously analyse the underlying dataset characteristics, leading to suboptimal model selection and unreliable outcomes. This paper addresses these shortcomings by presenting a comprehensive framework that integrates fundamental time series diagnostics—stationarity tests, autocorrelation analysis, heteroscedasticity, multicollinearity, and correlation analysis—into forecasting workflows. Unlike existing studies that prioritise pre-packaged machine learning and deep learning methods, often at the expense of interpretable statistical benchmarks, our approach advocates for the combined use of statistical models alongside advanced machine learning methods. Using the Day-Ahead Market dataset from the Irish electricity market as a case study, we demonstrate how rigorous statistical diagnostics can guide model selection, improve interpretability, and improve forecasting accuracy. This work offers a novel, integrative methodology that bridges the gap between statistical rigour and modern computational techniques, improving reliability in time series forecasting.
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Open AccessProceeding Paper
Beyond the Hodrick Prescott Filter: Wavelets and the Dynamics of U.S.–Mexico Trade
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José Gerardo Covarrubias and Xuedong Liu
Comput. Sci. Math. Forum 2025, 11(1), 14; https://doi.org/10.3390/cmsf2025011014 - 1 Aug 2025
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This study analyzes the evolution of the Mexico–U.S. trade balance as a seasonally adjusted time series, comparing the Hodrick–Prescott (HP) filter and wavelet analysis. The HP filter allowed the trend and cycle to be extracted from the series, while wavelets decomposed the information
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This study analyzes the evolution of the Mexico–U.S. trade balance as a seasonally adjusted time series, comparing the Hodrick–Prescott (HP) filter and wavelet analysis. The HP filter allowed the trend and cycle to be extracted from the series, while wavelets decomposed the information into different time scales, revealing short-, medium-, and long-term fluctuations. The results show that HP provides a simplified view of the trend, while wavelets more accurately capture key events and cyclical dynamics. It is concluded that wavelets offer a more robust tool for studying the volatility and persistence of economic shocks in bilateral trade.
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Open AccessProceeding Paper
Inclusive Turnout for Equitable Policies: Using Time Series Forecasting to Combat Policy Polarization
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Natasya Liew, Sreeya R. K. Haninatha, Sarthak Pattnaik, Kathleen Park and Eugene Pinsky
Comput. Sci. Math. Forum 2025, 11(1), 11; https://doi.org/10.3390/cmsf2025011011 - 1 Aug 2025
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Selective voter mobilization dominates U.S. elections, with campaigns prioritizing swing voters to win critical states. While effective for a short-term period, this strategy deepens policy polarization, marginalizes minorities, and undermines representative democracy. This paper investigates voter turnout disparities and policy manipulation using advanced
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Selective voter mobilization dominates U.S. elections, with campaigns prioritizing swing voters to win critical states. While effective for a short-term period, this strategy deepens policy polarization, marginalizes minorities, and undermines representative democracy. This paper investigates voter turnout disparities and policy manipulation using advanced time series forecasting models (ARIMA, LSTM, and seasonal decomposition). Analyzing demographic and geographic data, we uncover significant turnout inequities, particularly for marginalized groups, and propose actionable reforms to enhance equitable voter participation. By integrating data-driven insights with theoretical perspectives, this study offers practical recommendations for campaigns and policymakers to counter polarization and foster inclusive democratic representation.
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Open AccessProceeding Paper
The Forecasting of Aluminum Prices: A True Challenge for Econometric Models
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Krzysztof Drachal and Joanna Jędrzejewska
Comput. Sci. Math. Forum 2025, 11(1), 13; https://doi.org/10.3390/cmsf2025011013 - 31 Jul 2025
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This paper explores the forecasting of aluminum prices using various predictive models dealing with variable uncertainty. A diverse set of economic and market indicators is considered as potential price predictors. The performance of models including LASSO, RIDGE regression, time-varying parameter regressions, LARS, ARIMA,
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This paper explores the forecasting of aluminum prices using various predictive models dealing with variable uncertainty. A diverse set of economic and market indicators is considered as potential price predictors. The performance of models including LASSO, RIDGE regression, time-varying parameter regressions, LARS, ARIMA, Dynamic Model Averaging, Bayesian Model Averaging, etc., is compared according to forecast accuracy. Despite the initial expectations that Bayesian dynamic mixture models would provide the best forecast accuracy, the results indicate that forecasting by futures prices and with Dynamic Model Averaging outperformed all other methods when monthly average prices are considered. Contrary, when monthly closing spot prices are considered, Bayesian dynamic mixture models happen to be very accurate compared to other methods, although beating the no-change method is still a hard challenge. Additionally, both revised and originally published macroeconomic time-series data are analyzed, ensuring consistency with the information available during real-time forecasting by mimicking the perspective of market players in the past.
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Open AccessProceeding Paper
Monitoring Multidimensional Risk in the Economy
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Alexander Tyrsin, Michail Gerasimov and Michael Beer
Comput. Sci. Math. Forum 2025, 11(1), 10; https://doi.org/10.3390/cmsf2025011010 - 31 Jul 2025
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In economics, risk analysis is often associated with certain difficulties. These include the presence of several correlated risk factors, non-stationarity of economic processes, and small data samples. A mathematical model of multidimensional risk is described which satisfies the main features of processes in
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In economics, risk analysis is often associated with certain difficulties. These include the presence of several correlated risk factors, non-stationarity of economic processes, and small data samples. A mathematical model of multidimensional risk is described which satisfies the main features of processes in the economy. In the task of risk monitoring, we represent the analyzed factors as a set of correlated non-stationary time series. The method allows us to assess the risk at each moment using small data samples. For this purpose, risk factors are locally described in the form of parabolic or linear trends. An example of monitoring the risk of reducing the level of socio-economic development of Russia in 2000–2023 is considered. The monitoring results showed that the proposed multivariate risk model was generally sensitive to all the most significant economic shocks and adequately responded to them.
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Open AccessProceeding Paper
Analyzing and Classifying Time-Series Trends in Medals
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Minfei Liang, Yu Gao and Eugene Pinsky
Comput. Sci. Math. Forum 2025, 11(1), 9; https://doi.org/10.3390/cmsf2025011009 - 31 Jul 2025
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Since the 19th century, the development of metallurgical technology has been influenced by various factors, such as materials, casting technology, political policies, and the economic development of different countries. This paper aims to analyze the time-series evolution trend in medal issues in different
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Since the 19th century, the development of metallurgical technology has been influenced by various factors, such as materials, casting technology, political policies, and the economic development of different countries. This paper aims to analyze the time-series evolution trend in medal issues in different countries and explore their historical and commemorative significance. Taking the characteristics of medal production places, types, compositions, diameters, weights, shapes, compositions, and thicknesses between 1850 and 2025 as indicators, data analysis methods such as time series, hierarchical cluster analysis (HCA), logistic regression, and random forests are used to study the process of medal development and influencing factors in the past 175 years. The results show that compared with the pre-World War II period, the weight and diameter of all medals of major countries changed significantly in different periods. Moreover, before and after World War II, there was a shift from traditional materials to cost-effective and convenient alternatives.
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Open AccessProceeding Paper
An Autoregressive Moving Average Model for Time Series with Irregular Time Intervals
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Diana Alejandra Godoy Pulecio and César Andrés Ojeda Echeverri
Comput. Sci. Math. Forum 2025, 11(1), 8; https://doi.org/10.3390/cmsf2025011008 - 31 Jul 2025
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This research focuses on the study of stochastic processes with irregularly spaced time intervals, which is present in a wide range of fields such as climatology, astronomy, medicine, and economics. Some studies have proposed irregular autoregressive (iAR) and moving average (iMA) models separately,
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This research focuses on the study of stochastic processes with irregularly spaced time intervals, which is present in a wide range of fields such as climatology, astronomy, medicine, and economics. Some studies have proposed irregular autoregressive (iAR) and moving average (iMA) models separately, and moving average autoregressive processes (iARMA) for positive autoregressions. The objective of this work is to generalize the iARMA model to include negative correlations. A first-order moving average autoregressive model for irregular discrete time series is presented, being an ergodic and strictly stationary Gaussian process. Parameter estimation is performed by Maximum Likelihood, and its performances are evaluated for finite samples through Monte Carlo simulations. The estimation of the autocorrelation function (ACF) is performed using the DCF (Discrete Correlation Function) estimator, evaluating its performance by varying the sample size and average time interval. The model was implemented on real data from two different contexts; the first one consists of the two-week measurement of star flares of the Orion Nebula in the development of the COUP and the second pertains to the measurement of sunspot cycles from 1860 to 1990 and their relationship to temperature variation in the northern hemisphere.
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Open AccessProceeding Paper
Comparative Analysis of Temperature Prediction Models: Simple Models vs. Deep Learning Models
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Zibo Wang, Weiqi Zhang and Eugene Pinsky
Comput. Sci. Math. Forum 2025, 11(1), 6; https://doi.org/10.3390/cmsf2025011006 - 30 Jul 2025
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Accurate and concise temperature prediction models have important applications in meteorological science, agriculture, energy, and electricity. This study aims to compare the performance of simple models and deep learning models in temperature prediction and explore whether simple models can replace deep learning models
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Accurate and concise temperature prediction models have important applications in meteorological science, agriculture, energy, and electricity. This study aims to compare the performance of simple models and deep learning models in temperature prediction and explore whether simple models can replace deep learning models in specific scenarios to save computing resources. Based on 37 years of daily temperature time series data from 10 cities from 1987 to 2024, the Simple Moving Average (SMA), Seasonal Average Method with Lookback Years (SAM-Lookback), and Long Short-Term Memory (LSTM) models are fitted to evaluate the accuracy of simple models and deep learning models in temperature prediction. The performance of different models is intuitively compared by calculating the RMSE and Percentage Error of each city. The results show that LSTM has higher accuracy in most cities, but the prediction results of SMA and LSTM are similar and perform equally well, while SAM-Lookback is relatively weak.
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Open AccessProceeding Paper
Nonlinear Dynamic Inverse Solution of the Diffusion Problem Based on Krylov Subspace Methods with Spatiotemporal Constraints
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Luis Fernando Alvarez-Velasquez and Eduardo Giraldo
Comput. Sci. Math. Forum 2025, 11(1), 5; https://doi.org/10.3390/cmsf2025011005 - 30 Jul 2025
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In this work, we propose a nonlinear dynamic inverse solution to the diffusion problem based on Krylov Subspace Methods with spatiotemporal constraints. The proposed approach is applied by considering, as a forward problem, a 1D diffusion problem with a nonlinear diffusion model. The
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In this work, we propose a nonlinear dynamic inverse solution to the diffusion problem based on Krylov Subspace Methods with spatiotemporal constraints. The proposed approach is applied by considering, as a forward problem, a 1D diffusion problem with a nonlinear diffusion model. The dynamic inverse problem solution is obtained by considering a cost function with spatiotemporal constraints, where the Krylov subspace method named the Generalized Minimal Residual method is applied by considering a linearized diffusion model and spatiotemporal constraints. In addition, a Jacobian-based preconditioner is used to improve the convergence of the inverse solution. The proposed approach is evaluated under noise conditions by considering the reconstruction error and the relative residual error. It can be seen that the performance of the proposed approach is better when used with the preconditioner for the nonlinear diffusion model under noise conditions in comparison with the system without the preconditioner.
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Open AccessProceeding Paper
Time Series Forecasting for Touristic Policies
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Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Dimitrios Apostolopoulos, Andreas Menychtas and Dimosthenis Kyriazis
Comput. Sci. Math. Forum 2025, 11(1), 4; https://doi.org/10.3390/cmsf2025011004 - 30 Jul 2025
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The formulation of touristic policies is a time-consuming process that consists of a wide range of steps and procedures. These policies are highly dependent on the number of tourists and visitors to an area to be as effective as possible. The estimation of
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The formulation of touristic policies is a time-consuming process that consists of a wide range of steps and procedures. These policies are highly dependent on the number of tourists and visitors to an area to be as effective as possible. The estimation of this number is not always easy to achieve, since there is a lack of the corresponding data (i.e., number of visitors per day). Hence, this estimation must be achieved by utilizing alternative data sources. To this end, in this paper, the authors propose a neural network architecture that is trained on waste management data to estimate the number of visitors and tourists in the highly touristic municipality of Vari-Voula-Vouliagmeni, Greece.
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Open AccessEditorial
Statement of Peer Review
by
Hicham Gibet Tani, Mohamed Kouissi, Mohamed Ben Ahmed, Anouar Boudhir Abdelhakim and Lotfi Elaachak
Comput. Sci. Math. Forum 2025, 10(1), 18; https://doi.org/10.3390/cmsf2025010018 - 25 Jul 2025
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(This article belongs to the Proceedings of International Conference on Sustainable Computing and Green Technologies (SCGT’2025))
Open AccessProceeding Paper
Recent Developments in Four-In-Wheel Electronic Differential Systems in Electrical Vehicles
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Anouar El Mourabit and Ibrahim Hadj Baraka
Comput. Sci. Math. Forum 2025, 10(1), 17; https://doi.org/10.3390/cmsf2025010017 - 25 Jul 2025
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This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms
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This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features.
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(This article belongs to the Proceedings of International Conference on Sustainable Computing and Green Technologies (SCGT’2025))
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Open AccessProceeding Paper
Detecting Financial Bubbles with Tail-Weighted Entropy
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Omid M. Ardakani
Comput. Sci. Math. Forum 2025, 11(1), 3; https://doi.org/10.3390/cmsf2025011003 - 25 Jul 2025
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This paper develops a novel entropy-based framework to quantify tail risk and detect speculative bubbles in financial markets. By integrating extreme value theory with information theory, I introduce the Tail-Weighted Entropy (TWE) measure, which captures how information scales with extremeness in asset price
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This paper develops a novel entropy-based framework to quantify tail risk and detect speculative bubbles in financial markets. By integrating extreme value theory with information theory, I introduce the Tail-Weighted Entropy (TWE) measure, which captures how information scales with extremeness in asset price distributions. I derive explicit bounds for TWE under heavy-tailed models and establish its connection to tail index parameters, revealing a phase transition in entropy decay rates during bubble formation. Empirically, I demonstrate that TWE-based signals detect crises in equities, commodities, and cryptocurrencies days earlier than traditional variance-ratio tests, with Bitcoin’s 2021 collapse identified weeks prior to the peak. The results show that entropy decay—not volatility explosions—serves as the primary precursor to systemic risk, offering policymakers a robust tool for preemptive crisis management.
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Open AccessProceeding Paper
An Estimation of Risk Measures: Analysis of a Method
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Marta Ferreira and Liliana Monteiro
Comput. Sci. Math. Forum 2025, 11(1), 2; https://doi.org/10.3390/cmsf2025011002 - 25 Jul 2025
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Extreme value theory comprises a set of techniques for inference at the tail of distributions, where data are scarce or non-existent. The tail index is the main parameter, with risk measures such as value at risk or expected shortfall depending on it. In
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Extreme value theory comprises a set of techniques for inference at the tail of distributions, where data are scarce or non-existent. The tail index is the main parameter, with risk measures such as value at risk or expected shortfall depending on it. In this study, we will analyze a method for estimating the tail index through a simulation study. This will allow for an application using real data including the estimation of the mentioned risk measures.
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Open AccessProceeding Paper
Evaluating Sales Forecasting Methods in Make-to-Order Environments: A Cross-Industry Benchmark Study
by
Marius Syberg, Lucas Polley and Jochen Deuse
Comput. Sci. Math. Forum 2025, 11(1), 1; https://doi.org/10.3390/cmsf2025011001 - 25 Jul 2025
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Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in
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Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in MTO settings across three manufacturing sectors: electrical equipment, steel, and office supplies. A cross-industry benchmark assesses models such as ARIMA, Holt–Winters, Random Forest, LSTM, and Facebook Prophet. The evaluation considers error metrics (MAE, RMSE, and sMAPE) as well as implementation aspects like computational demand and interpretability. Special attention is given to data sensitivity and technical limitations typical in SMEs. The findings show that ML models perform well under high volatility and when enriched with external indicators, but they require significant expertise and resources. In contrast, simpler statistical methods offer robust performance in more stable or seasonal demand contexts and are better suited in certain cases. The study emphasizes the importance of transparency, usability, and trust in forecasting tools and offers actionable recommendations for selecting a suitable forecasting configuration based on context. By aligning technical capabilities with operational needs, this research supports more effective decision-making in data-constrained MTO environments.
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Open AccessProceeding Paper
Overview of Training LLMs on One Single GPU
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Mohamed Ben jouad and Lotfi Elaachak
Comput. Sci. Math. Forum 2025, 10(1), 14; https://doi.org/10.3390/cmsf2025010014 - 9 Jul 2025
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Large language models (LLMs) are developing at a rapid pace, which has made it necessary to better understand how they train, especially when faced with resource limitations. This paper examines in detail how various state-of-the-art LLMs train on a single Graphical Processing Unit
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Large language models (LLMs) are developing at a rapid pace, which has made it necessary to better understand how they train, especially when faced with resource limitations. This paper examines in detail how various state-of-the-art LLMs train on a single Graphical Processing Unit (GPU), paying close attention to crucial elements like throughput, memory utilization and training time. We find important trade-offs between model size, batch size and computational efficiency through empirical evaluation, offering practical advice for streamlining fine-tuning processes in the face of hardware constraints.
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(This article belongs to the Proceedings of International Conference on Sustainable Computing and Green Technologies (SCGT’2025))
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Open AccessProceeding Paper
Optimizing Machine Learning for Healthcare Applications: A Case Study on Cardiovascular Disease Prediction Through Feature Selection, Regularization, and Overfitting Reduction
by
Lamiae Eloutouate, Hicham Gibet Tani, Lotfi Elaachak, Fatiha Elouaai and Mohammed Bouhorma
Comput. Sci. Math. Forum 2025, 10(1), 13; https://doi.org/10.3390/cmsf2025010013 - 7 Jul 2025
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The application of machine learning (ML) to medical datasets offers significant potential for improving disease prediction and patient outcomes. However, challenges such as feature redundancy, overfitting, and suboptimal model performance limit the practical effectiveness of ML algorithms. This study focuses on optimizing ML
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The application of machine learning (ML) to medical datasets offers significant potential for improving disease prediction and patient outcomes. However, challenges such as feature redundancy, overfitting, and suboptimal model performance limit the practical effectiveness of ML algorithms. This study focuses on optimizing ML techniques for cardiovascular disease prediction using the Kaggle Cardiovascular Disease dataset. We systematically apply feature selection methods, including correlation analysis and regularization techniques (L1/L2), to identify the most relevant attributes and address multicollinearity. Advanced ensemble models such as Random Forest, XGBoost, and LightGBM are employed to mitigate overfitting and enhance predictive performance. Through hyperparameter tuning and stratified k-fold cross-validation, we ensure model robustness and generalizability. The results demonstrate that ensemble methods, particularly gradient boosting algorithms, outperform traditional models, achieving superior predictive accuracy and stability. This study highlights the importance of algorithm optimization in ML applications for healthcare, offering a replicable framework for medical datasets and paving the way for more effective diagnostic tools in cardiovascular health.
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(This article belongs to the Proceedings of International Conference on Sustainable Computing and Green Technologies (SCGT’2025))
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Open AccessProceeding Paper
Integrating Machine Learning with Medical Imaging for Human Disease Diagnosis: A Survey
by
Anass Roman, Chaymae Taib, Ilham Dhaiouir and Haimoudi El Khatir
Comput. Sci. Math. Forum 2025, 10(1), 12; https://doi.org/10.3390/cmsf2025010012 - 7 Jul 2025
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Machine learning is revolutionizing healthcare by enhancing diagnosis and treatment personalization. This study explores ML applications in medical imaging, analyzing data from X-rays, CT, MRI, and ultrasound for early disease detection. It reviews key ML models, including SVM, ANN, RF, CNN, and other
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Machine learning is revolutionizing healthcare by enhancing diagnosis and treatment personalization. This study explores ML applications in medical imaging, analyzing data from X-rays, CT, MRI, and ultrasound for early disease detection. It reviews key ML models, including SVM, ANN, RF, CNN, and other methods, demonstrating their effectiveness in detecting cancers such as lung and prostate cancer and other diseases. Despite their accuracy, these methods face challenges such as a reliance on large datasets and significant computational requirements. This study highlights the need for further research to integrate ML into clinical practice, addressing its limitations and unlocking new opportunities for improved patient care.
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(This article belongs to the Proceedings of International Conference on Sustainable Computing and Green Technologies (SCGT’2025))
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Open AccessProceeding Paper
The Evolution and Challenges of Real-Time Big Data: A Review
by
Ikram Lefhal Lalaoui, Essaid El Haji and Mohamed Kounaidi
Comput. Sci. Math. Forum 2025, 10(1), 11; https://doi.org/10.3390/cmsf2025010011 - 1 Jul 2025
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The importance of real-time big data has become crucial in the digital revolution of modern society, in the context of increasing data flows from multiple sources, including social media, internet connected devices (IOT) and financial systems, real-time analysis and processing is becoming a
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The importance of real-time big data has become crucial in the digital revolution of modern society, in the context of increasing data flows from multiple sources, including social media, internet connected devices (IOT) and financial systems, real-time analysis and processing is becoming a strategic tool for fast and accurate decision making, we find applications in different domains such as healthcare, finance, and digital marketing, which is revolutionizing traditional business models. In this article, we explore the recent advances and future prospects of real-time big data. Our research is based on recent work published between 2020 and 2025, examining the technological advances, the difficulties encountered and suggesting ways of optimizing the efficiency of these technologies.
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(This article belongs to the Proceedings of International Conference on Sustainable Computing and Green Technologies (SCGT’2025))
Open AccessProceeding Paper
Advancing Stress Detection and Health Monitoring with Deep Learning Approaches
by
Merouane Mouadili, El Mokhtar En-Naimi and Mohamed Kouissi
Comput. Sci. Math. Forum 2025, 10(1), 10; https://doi.org/10.3390/cmsf2025010010 - 1 Jul 2025
Abstract
Numerous studies in the healthcare field conducted in recent years have highlighted the impact of stress on health and its role in the development of several critical illnesses. Stress monitoring using wearable technologies, such as smartwatches and biosensors, has shown promising results in
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Numerous studies in the healthcare field conducted in recent years have highlighted the impact of stress on health and its role in the development of several critical illnesses. Stress monitoring using wearable technologies, such as smartwatches and biosensors, has shown promising results in improving the management of this issue. Data from both physical and mental health can be leveraged to enhance medical decision-making, support research on new treatments, and deepen our understanding of complex diseases. However, traditional machine learning (ML) systems often face limitations, particularly in real-time processing and resource optimization, which restrict their application in critical situations. In this article, we present the development of a deep learning-based approach that leverages models such as 1D CNN (Convolutional Neural Networks), LSTM (Long Short-Term Memory), and Time-Series Transformers, alongside classical deep learning techniques. We then highlight the transformative potential of TinyML for real-time, low-power health monitoring, focusing on Heart Rate Variability (HRV) analysis. This approach aims to optimize personalized health interventions and enhance the accuracy of medical monitoring.
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