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  • Open Access

    REVIEW

    An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges

    Nidhi Parashar1, Prashant Johri1, Arfat Ahmad Khan5, Nitin Gaur1, Seifedine Kadry2,3,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240 - 18 July 2024

    Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

  • Open Access

    EDITORIAL

    Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

    Bo Yang1,*, Yucun Qian1, Jianzhong Xu2, Yaxing Ren3, Yixuan Chen4

    Energy Engineering, Vol.121, No.8, pp. 2085-2091, 2024, DOI:10.32604/ee.2024.050083 - 19 July 2024

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

    Qin Qian1, Mingjing Lu1,2,*, Anhai Zhong1, Feng Yang1, Wenjun He1, Min Li1

    Energy Engineering, Vol.121, No.8, pp. 2167-2190, 2024, DOI:10.32604/ee.2024.049430 - 19 July 2024

    Abstract The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells… More >

  • Open Access

    ARTICLE

    Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

    Youshen Jiang1, Tongqing Zhou1, Zhilin Wang2, Zhiping Cai1,*, Qiang Ni3

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 585-597, 2024, DOI:10.32604/iasc.2023.030221 - 11 July 2024

    Abstract Due to the increasingly severe challenges brought by various epidemic diseases, people urgently need intelligent outbreak trend prediction. Predicting disease onset is very important to assist decision-making. Most of the existing work fails to make full use of the temporal and spatial characteristics of epidemics, and also relies on multivariate data for prediction. In this paper, we propose a Multi-Scale Location Attention Graph Neural Networks (MSLAGNN) based on a large number of Centers for Disease Control and Prevention (CDC) patient electronic medical records research sequence source data sets. In order to understand the geography and… More >

  • Open Access

    ARTICLE

    Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

    K. S. Rajendra Prasad1, Sathya Sai2, T. R. Seetharam3, Adithya Garimella1,*

    Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 889-917, 2024, DOI:10.32604/fhmt.2024.047703 - 11 July 2024

    Abstract Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing downwards. Computations are performed by varying the value of from 5 to 30 K and ratio from 1.1 to 1.5. Variation of all the thermophysical properties of supercritical Nitrogen is considered. The wall temperatures are chosen in such a way that two values of T are less than is the temperature at which the fluid has a maximum value of C for the given pressure), More >

  • Open Access

    ARTICLE

    Computational Fluid Dynamics Approach for Predicting Pipeline Response to Various Blast Scenarios: A Numerical Modeling Study

    Farman Saifi1,*, Mohd Javaid1, Abid Haleem1, S. M. Anas2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2747-2777, 2024, DOI:10.32604/cmes.2024.051490 - 08 July 2024

    Abstract Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infrastructure systems and networks capable of withstanding blast loading. Initially centered on high-profile facilities such as embassies and petrochemical plants, this concern now extends to a wider array of infrastructures and facilities. Engineers and scholars increasingly prioritize structural safety against explosions, particularly to prevent disproportionate collapse and damage to nearby structures. Urbanization has further amplified the reliance on oil and gas pipelines, making them vital for urban life and prime targets for terrorist activities. Consequently, there is a growing imperative for computational… More >

  • Open Access

    REVIEW

    Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

    Mojtaba Yari1,*, Manoj Khandelwal2, Payam Abbasi3, Evangelos I. Koutras4, Danial Jahed Armaghani5,*, Panagiotis G. Asteris4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2207-2238, 2024, DOI:10.32604/cmes.2024.048071 - 08 July 2024

    Abstract Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to More >

  • Open Access

    ARTICLE

    Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

    Xiuyang Meng1,2, Chunling Wang1,2,*, Jingran Yang1,2, Mairui Li1,2, Yue Zhang1,2, Luo Wang1,2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4259-4281, 2024, DOI:10.32604/cmc.2024.050325 - 20 June 2024

    Abstract Suicide has become a critical concern, necessitating the development of effective preventative strategies. Social media platforms offer a valuable resource for identifying signs of suicidal ideation. Despite progress in detecting suicidal ideation on social media, accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge. To tackle this, we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships (TCNN-SN). This model enhances predictive performance by leveraging social network relationship features More >

  • Open Access

    ARTICLE

    Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

    Jing Gao*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158 - 20 June 2024

    Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

  • Open Access

    ARTICLE

    A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

    Mingyuan Li1,*, Yuanli Gu1, Qingqiao Geng2, Hongru Yu1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3877-3896, 2024, DOI:10.32604/cmc.2024.047765 - 20 June 2024

    Abstract This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more… More >

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