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  • Open Access

    ARTICLE

    Models for Predicting the Minimum Miscibility Pressure (MMP) of CO2-Oil in Ultra-Deep Oil Reservoirs Based on Machine Learning

    Kun Li1, Tianfu Li2,*, Xiuwei Wang1, Qingchun Meng1, Zhenjie Wang1, Jinyang Luo1,2, Zhaohui Wang1, Yuedong Yao2

    Energy Engineering, Vol.122, No.6, pp. 2215-2238, 2025, DOI:10.32604/ee.2025.062876 - 29 May 2025

    Abstract CO2 flooding for enhanced oil recovery (EOR) not only enables underground carbon storage but also plays a critical role in tertiary oil recovery. However, its displacement efficiency is constrained by whether CO2 and crude oil achieve miscibility, necessitating precise prediction of the minimum miscibility pressure (MMP) for CO2-oil systems. Traditional methods, such as experimental measurements and empirical correlations, face challenges including time-consuming procedures and limited applicability. In contrast, artificial intelligence (AI) algorithms have emerged as superior alternatives due to their efficiency, broad applicability, and high prediction accuracy. This study employs four AI algorithms—Random Forest Regression (RFR), Genetic… More >

  • Open Access

    ARTICLE

    Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions

    Faisal Ghazi Beshaw1, Thamir Hassan Atyia2, Mohd Fadzli Mohd Salleh1, Mohamad Khairi Ishak3, Abdul Sattar Din1,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3553-3583, 2025, DOI:10.32604/cmc.2025.061400 - 16 April 2025

    Abstract The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was… More >

  • Open Access

    ARTICLE

    Prediction and Comparative Analysis of Rooftop PV Solar Energy Efficiency Considering Indoor and Outdoor Parameters under Real Climate Conditions Factors with Machine Learning Model

    Gökhan Şahin1,*, Ihsan Levent2, Gültekin Işık2, Wilfried van Sark1, Sabir Rustemli3

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1215-1248, 2025, DOI:10.32604/cmes.2025.063193 - 11 April 2025

    Abstract This research investigates the influence of indoor and outdoor factors on photovoltaic (PV) power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency. To predict plant efficiency, nineteen variables are analyzed, consisting of nine indoor photovoltaic panel characteristics (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Maximum Voltage (Umpp), Maximum Current (Impp), Filling Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), Module Temperature) and ten environmental factors (Air Temperature, Air Humidity, Dew Point, Air Pressure, Irradiation, Irradiation Propagation, Wind Speed, Wind… More >

  • Open Access

    ARTICLE

    Ecological Factors Influencing Morphology and Tropane Alkaloid Content in Anisodus tanguticus (Maxim.) Pascher

    Chen Chen1,2, Fengqin Liu1, Jianan Li2, Bo Wang2, Yuanming Xiao2, Tao Sun1, Guoying Zhou2,*

    Phyton-International Journal of Experimental Botany, Vol.94, No.3, pp. 973-986, 2025, DOI:10.32604/phyton.2025.062421 - 31 March 2025

    Abstract Anisodus tanguticus (Maxim.) Pascher, a medicinal plant in the Solanaceae family, is widely distributed across the Qinghai-Tibet Plateau. Its medicinal properties, particularly the tropane alkaloids, are influenced by various ecological factors, but the underlying ecological mechanisms remain poorly understood. This study aimed to investigate how key environmental variables influence both the morphological traits and tropane alkaloid contents of A. tanguticus, with the goal of providing data to support the sustainable cultivation and management of this species. We collected samples from 71 sites across its natural habitat, analyzing the effects of factors such as soil nutrients, altitude, and… More >

  • Open Access

    ARTICLE

    Classifying Network Flows through a Multi-Modal 1D CNN Approach Using Unified Traffic Representations

    Ravi Veerabhadrappa*, Poornima Athikatte Sampigerayappa

    Computer Systems Science and Engineering, Vol.49, pp. 333-351, 2025, DOI:10.32604/csse.2025.061285 - 19 March 2025

    Abstract In recent years, the analysis of encrypted network traffic has gained momentum due to the widespread use of Transport Layer Security and Quick UDP Internet Connections protocols, which complicate and prolong the analysis process. Classification models face challenges in understanding and classifying unknown traffic because of issues related to interpret ability and the representation of traffic data. To tackle these complexities, multi-modal representation learning can be employed to extract meaningful features and represent them in a lower-dimensional latent space. Recently, auto-encoder-based multi-modal representation techniques have shown superior performance in representing network traffic. By combining the… More >

  • Open Access

    ARTICLE

    An Enhanced Task Migration Technique Based on Convolutional Neural Network in Machine Learning Framework

    Hamayun Khan1,*, Muhammad Atif Imtiaz2, Hira Siddique3, Muhammad Tausif Afzal Rana4, Arshad Ali5, Muhammad Zeeshan Baig6, Saif ur Rehman7, Yazed Alsaawy5

    Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

    Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

  • Open Access

    ARTICLE

    Unmasking Social Robots’ Camouflage: A GNN-Random Forest Framework for Enhanced Detection

    Weijian Fan1,*, Chunhua Wang2, Xiao Han3, Chichen Lin4

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 467-483, 2025, DOI:10.32604/cmc.2024.056930 - 03 January 2025

    Abstract The proliferation of robot accounts on social media platforms has posed a significant negative impact, necessitating robust measures to counter network anomalies and safeguard content integrity. Social robot detection has emerged as a pivotal yet intricate task, aimed at mitigating the dissemination of misleading information. While graph-based approaches have attained remarkable performance in this realm, they grapple with a fundamental limitation: the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles. To unravel this challenge and thwart the camouflage tactics, this work proposed an innovative social… More >

  • Open Access

    ARTICLE

    Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

    Liyao Yang1, Hongyan Ma1,2,3,*, Yingda Zhang1, Wei He1

    Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

    Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >

  • Open Access

    ARTICLE

    Overfitting in Machine Learning: A Comparative Analysis of Decision Trees and Random Forests

    Erblin Halabaku, Eliot Bytyçi*

    Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 987-1006, 2024, DOI:10.32604/iasc.2024.059429 - 30 December 2024

    Abstract Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data. This paper presents a comprehensive analysis of machine learning algorithms, focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models. It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP (ACV-CCP). This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter, leveraging cross-validation within the pruning process to achieve More >

  • Open Access

    ARTICLE

    A Hybrid WSVM-Levy Approach for Energy-Efficient Manufacturing Using Big Data and IoT

    Surbhi Bhatia Khan1,2,*, Mohammad Alojail3, Mahesh Thyluru Ramakrishna4, Hemant Sharma5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4895-4914, 2024, DOI:10.32604/cmc.2024.057585 - 19 December 2024

    Abstract In Intelligent Manufacturing, Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors, ensuring more informed decision-making and adaptive system management. It also promotes decision making and provides scientific analysis to enhance the efficiency of the operation, cost reduction, maximizing the process of production and so on. Various methods are employed to enhance productivity, yet achieving sustainable manufacturing remains a complex challenge that requires careful consideration. This study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid… More >

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