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  • Open Access

    CORRECTION

    Correction: 3D Model Construction and Ecological Environment Investigation on a Regional Scale Using UAV Remote Sensing

    Chao Chen1,2, Yankun Chen3, Haohai Jin4, Li Chen5,*, Zhisong Liu3, Haozhe Sun4, Junchi Hong4, Haonan Wang4, Shiyu Fang4, Xin Zhang2

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 113-114, 2024, DOI:10.32604/iasc.2024.051760

    Abstract This article has no abstract. More >

  • Open Access

    CORRECTION

    Correction: Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

    M. S. Vivekanandan1, T. Jesudas2,*

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 109-111, 2024, DOI:10.32604/iasc.2024.051758

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

    Sepehr Soltani1, Ehsan Ghafourian2, Reza Salehi3, Diego Martín3,*, Milad Vahidi4

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 93-108, 2024, DOI:10.32604/iasc.2024.042693

    Abstract For many years, researchers have explored power allocation (PA) algorithms driven by models in wireless networks where multiple-user communications with interference are present. Nowadays, data-driven machine learning methods have become quite popular in analyzing wireless communication systems, which among them deep reinforcement learning (DRL) has a significant role in solving optimization issues under certain constraints. To this purpose, in this paper, we investigate the PA problem in a -user multiple access channels (MAC), where transmitters (e.g., mobile users) aim to send an independent message to a common receiver (e.g., base station) through wireless channels. To this end, we first train… 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

    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 between system indicators and the… More >

  • Open Access

    ARTICLE

    A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

    Ghulam Gilanie1,*, Sana Cheema1, Akkasha Latif1, Anum Saher1, Muhammad Ahsan1, Hafeez Ullah2, Diya Oommen3

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 57-71, 2024, DOI:10.32604/iasc.2024.041535

    Abstract Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient. It affects a large percentage of people globally, who fluctuate between depression and mania, or vice versa. A pleasant or unpleasant mood is more than a reflection of a state of mind. Normally, it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio, so automated procedures are the best options to diagnose and verify the severity of bipolar. In this research work, facial micro-expressions have been used for bipolar detection using… More >

  • Open Access

    ARTICLE

    Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

    M. Adimoolam1, K. Maithili2, N. M. Balamurugan3, R. Rajkumar4, S. Leelavathy5, Raju Kannadasan6, Mohd Anul Haq7,*, Ilyas Khan8, ElSayed M. Tag El Din9, Arfat Ahmad Khan10

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 33-55, 2024, DOI:10.32604/iasc.2024.039009

    Abstract At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using… More >

  • Open Access

    ARTICLE

    Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

    Nguyen Vu Anh Duy, Nguyen Le Thai, Nguyen Huu Tho*

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 15-31, 2024, DOI:10.32604/iasc.2024.038272

    Abstract In this paper, we consider a multi-crane scheduling problem in rail stations because their operations directly influence the throughput of the rail stations. In particular, the job is not only assigned to cranes but also the job sequencing is implemented for each crane to minimize the makespan of cranes. A dual cycle of cranes is used to minimize the number of working cycles of cranes. The rail crane scheduling problems in this study are based on the movement of containers. We consider not only the gantry moves, but also the trolley moves as well as the re-handle cases are also… More >

  • Open Access

    ARTICLE

    Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

    M. L. Maruthi Prasad*, R. Santhosh

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 1-13, 2024, DOI:10.32604/iasc.2023.036628

    Abstract This research concentrates to model an efficient thyroid prediction approach, which is considered a baseline for significant problems faced by the women community. The major research problem is the lack of automated model to attain earlier prediction. Some existing model fails to give better prediction accuracy. Here, a novel clinical decision support system is framed to make the proper decision during a time of complexity. Multiple stages are followed in the proposed framework, which plays a substantial role in thyroid prediction. These steps include i) data acquisition, ii) outlier prediction, and iii) multi-stage weight-based ensemble learning process (MS-WEL). The weighted… More >

  • Open Access

    ARTICLE

    A New Flower Pollination Algorithm Strategy for MPPT of Partially Shaded Photovoltaic Arrays

    Muhannad J. Alshareef*

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 297-313, 2023, DOI:10.32604/iasc.2023.046722

    Abstract Photovoltaic (PV) systems utilize maximum power point tracking (MPPT) controllers to optimize power output amidst varying environmental conditions. However, the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation. Under partial shade conditions, the global maximum power point (GMPP) may be missed by most traditional maximum power point tracker. The flower pollination algorithm (FPA) and particle swarm optimization (PSO) are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP. This paper discusses and resolves all issues associated with using the standard FPA method as… 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 >

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