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  • Open Access

    ARTICLE

    Optimizing Household Wastes (Rice, Vegetables, and Fruit) as an Environmentally Friendly Electricity Generator

    Deni Ainur Rokhim1,2, Isma Yanti Vitarisma1, Sumari Sumari1,*, Yudhi Utomo1, Muhammad Roy Asrori1

    Journal of Renewable Materials, Vol.12, No.2, pp. 275-284, 2024, DOI:10.32604/jrm.2023.043419

    Abstract The high consumption of electricity and issues related to fossil energy have triggered an increase in energy prices and the scarcity of fossil resources. Consequently, many researchers are seeking alternative energy sources. One potential technology, the Microbial Fuel Cell (MFC) based on rice, vegetable, and fruit wastes, can convert chemical energy into electrical energy. This study aims to determine the potency of rice, vegetable, and fruit waste assisted by Cu/Mg electrodes as a generator of electricity. The method used was a laboratory experiment, including the following steps: electrode preparation, waste sample preparation, incubation of the waste samples, construction of a… More >

  • Open Access

    ARTICLE

    ASLP-DL —A Novel Approach Employing Lightweight Deep Learning Framework for Optimizing Accident Severity Level Prediction

    Saba Awan1,*, Zahid Mehmood2,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337

    Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic… More >

  • Open Access

    ARTICLE

    Optimizing Deep Neural Networks for Face Recognition to Increase Training Speed and Improve Model Accuracy

    Mostafa Diba*, Hossein Khosravi

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590

    Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories of feature maps, the reduction… More >

  • Open Access

    ARTICLE

    An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

    Qianqi Sun, Fenhua Bai*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1733-1754, 2024, DOI:10.32604/cmes.2023.044558

    Abstract Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development. Nevertheless, scalability challenges, characterized by diminished broadcast efficiency, heightened communication overhead, and escalated storage costs, have significantly constrained the broad-scale application of blockchain. This paper introduces a novel Encode-and CRT-based Scalability Scheme (ECSS), meticulously refined to enhance both block broadcasting and storage. Primarily, ECSS categorizes nodes into distinct domains, thereby reducing the network diameter and augmenting transmission efficiency. Secondly, ECSS streamlines block transmission through a compact block protocol and robust RS coding, which not only reduces the size of broadcasted blocks but also ensures transmission… More >

  • Open Access

    ARTICLE

    Numerical Study on Reduction in Aerodynamic Drag and Noise of High-Speed Pantograph

    Deng Qin1, Xing Du2, Tian Li1,*, Jiye Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2155-2173, 2024, DOI:10.32604/cmes.2023.044460

    Abstract Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly, energy efficient and rapid advances in train technology. Using computational fluid dynamics theory and the K-FWH acoustic equation, a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs. A component optimization method is proposed as a possible solution to the problem of aerodynamic drag and noise in high-speed pantographs. The results of the study indicate that the panhead, base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs. Therefore, a gradual optimization process is implemented to… More >

  • Open Access

    ARTICLE

    Optimizing the Diameter of Plugging Balls in Deep Shale Gas Wells

    Yi Song1, Zheyu Hu2,*, Cheng Shen1, Lan Ren2, Xingwu Guo1, Ran Lin2, Kun Wang3, Zhiyong Zhao4

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 609-624, 2024, DOI:10.32604/fdmp.2023.030521

    Abstract Deep shale gas reserves that have been fractured typically have many relatively close perforation holes. Due to the proximity of each fracture during the formation of the fracture network, there is significant stress interference, which results in uneven fracture propagation. It is common practice to use “balls” to temporarily plug fracture openings in order to lessen liquid intake and achieve uniform propagation in each cluster. In this study, a diameter optimization model is introduced for these plugging balls based on a multi-cluster fracture propagation model and a perforation dynamic abrasion model. This approach relies on proper consideration of the multiphase… More > Graphic Abstract

    Optimizing the Diameter of Plugging Balls in Deep Shale Gas Wells

  • Open Access

    ARTICLE

    Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

    Hassen Louati1,2, Ali Louati3,*, Elham Kariri3, Slim Bechikh2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806

    Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression of a convolutional neural network… More >

  • Open Access

    ARTICLE

    Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

    Abdul Qadir Khan1, Guangmin Sun1,*, Yu Li1, Anas Bilal2, Malik Abdul Manan1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2481-2504, 2023, DOI:10.32604/cmc.2023.043239

    Abstract In the emerging field of image segmentation, Fully Convolutional Networks (FCNs) have recently become prominent. However, their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters, which can often be a cumbersome manual task. The main aim of this study is to propose a more efficient, less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images. To this end, our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network (FCEDN). The optimization is handled by a novel Genetic Grey Wolf Optimization (G-GWO) algorithm. This algorithm employs the Genetic Algorithm (GA) to generate a diverse set of… 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

    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 achieved through the incorporation of… More >

  • Open Access

    ARTICLE

    Optimizing Power Allocation for D2D Communication with URLLC under Rician Fading Channel: A Learning-to-Optimize Approach

    Owais Muhammad1, Hong Jiang1,*, Mushtaq Muhammad Umer1, Bilal Muhammad2, Naeem Muhammad Ahtsam3

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3193-3212, 2023, DOI:10.32604/iasc.2023.041232

    Abstract To meet the high-performance requirements of fifth-generation (5G) and sixth-generation (6G) wireless networks, in particular, ultra-reliable and low-latency communication (URLLC) is considered to be one of the most important communication scenarios in a wireless network. In this paper, we consider the effects of the Rician fading channel on the performance of cooperative device-to-device (D2D) communication with URLLC. For better performance, we maximize and examine the system’s minimal rate of D2D communication. Due to the interference in D2D communication, the problem of maximizing the minimum rate becomes non-convex and difficult to solve. To solve this problem, a learning-to-optimize-based algorithm is proposed… More >

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