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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

    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 >

  • Open Access

    ARTICLE

    A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

    Yu Zhao, Zhijie Zhou*, Hongdong Fan, Xiaoxia Han, Jie Wang, Manlin Chen

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 73-91, 2024, DOI:10.32604/iasc.2024.042285 - 29 March 2024

    Abstract In industrial production and engineering operations, the health state of complex systems is critical, and predicting it can ensure normal operation. Complex systems have many monitoring indicators, complex coupling structures, non-linear and time-varying characteristics, so it is a challenge to establish a reliable prediction model. The belief rule base (BRB) can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities. Since each indicator of the complex system can reflect the health state to some extent, the BRB is built based on the causal relationship… More >

  • Open Access

    ARTICLE

    Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

    Zhuo Chen1,*, Ningning Wang2, Wenbo Jin3, Dui Li1

    Energy Engineering, Vol.121, No.4, pp. 1007-1026, 2024, DOI:10.32604/ee.2023.045270 - 26 March 2024

    Abstract A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines. To ensure the safe operation of crude oil pipelines, an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines. Aiming at the shortcomings of the ENN prediction model, which easily falls into the local minimum value and weak generalization ability in the implementation process, an optimized ENN prediction model based on the IRSA is proposed. The validity of the new model was confirmed by the accurate prediction of two sets of… More > Graphic Abstract

    Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

  • Open Access

    ARTICLE

    Productivity Prediction Model of Perforated Horizontal Well Based on Permeability Calculation in Near-Well High Permeability Reservoir Area

    Shuangshuang Zhang1,*, Kangliang Guo1, Xinchen Gao1, Haoran Yang1, Jinfeng Zhang2, Xing Han3

    Energy Engineering, Vol.121, No.1, pp. 59-75, 2024, DOI:10.32604/ee.2023.041709 - 27 December 2023

    Abstract To improve the productivity of oil wells, perforation technology is usually used to improve the productivity of horizontal wells in oilfield exploitation. After the perforation operation, the perforation channel around the wellbore will form a near-well high-permeability reservoir area with the penetration depth as the radius, that is, the formation has different permeability characteristics with the perforation depth as the dividing line. Generally, the permeability is measured by the permeability tester, but this approach has a high workload and limited application. In this paper, according to the reservoir characteristics of perforated horizontal wells, the reservoir… More >

  • Open Access

    ARTICLE

    Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity

    Xinzheng Wang1,2,*, Bing Guo1, Yan Shen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1183-1206, 2024, DOI:10.32604/cmes.2023.029552 - 17 November 2023

    Abstract Predicting students’ academic achievements is an essential issue in education, which can benefit many stakeholders, for instance, students, teachers, managers, etc. Compared with online courses such as MOOCs, students’ academic-related data in the face-to-face physical teaching environment is usually sparsity, and the sample size is relatively small. It makes building models to predict students’ performance accurately in such an environment even more challenging. This paper proposes a Two-Way Neural Network (TWNN) model based on the bidirectional recurrent neural network and graph neural network to predict students’ next semester’s course performance using only their previous course More > Graphic Abstract

    Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity

  • Open Access

    ARTICLE

    Application Research on Two-Layer Threat Prediction Model Based on Event Graph

    Shuqin Zhang, Xinyu Su*, Yunfei Han, Tianhui Du, Peiyu Shi

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526 - 26 December 2023

    Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines… More >

  • Open Access

    ARTICLE

    Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

    Seungwoo Kang1, Seyha Ros1, Inseok Song1, Prohim Tam1, Sa Math2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020 - 29 November 2023

    Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model… More >

  • Open Access

    ARTICLE

    Revolutionizing Tight Reservoir Production: A Novel Dual-Medium Unsteady Seepage Model for Optimizing Volumetrically Fractured Horizontal Wells

    Xinyu Zhao1,2,*, Mofeng Li2, Kai Yan2, Li Yin3

    Energy Engineering, Vol.120, No.12, pp. 2933-2949, 2023, DOI:10.32604/ee.2023.041580 - 29 November 2023

    Abstract This study presents an avant-garde approach for predicting and optimizing production in tight reservoirs, employing a dual-medium unsteady seepage model specifically fashioned for volumetrically fractured horizontal wells. Traditional models often fail to fully capture the complex dynamics associated with these unconventional reservoirs. In a significant departure from these models, our approach incorporates an initiation pressure gradient and a discrete fracture seepage network, providing a more realistic representation of the seepage process. The model also integrates an enhanced fluid-solid interaction, which allows for a more comprehensive understanding of the fluid-structure interactions in the reservoir. This is… More >

  • Open Access

    ARTICLE

    Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

    Chia-Wei Jan1, Yu-Jhih Chiu1, Kuan-Lin Chen2, Ting-Chun Yao3, Ping-Huan Kuo1,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078 - 09 November 2023

    Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of… More >

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