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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 >

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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
（This article belongs to the Special Issue: Optimization Problems Based on Mathematical Algorithms and Soft Computing)
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 >

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M. Adimoolam¹, K. Maithili², N. M. Balamurugan³, R. Rajkumar⁴, S. Leelavathy⁵, Raju Kannadasan⁶, Mohd Anul Haq^7,*, 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 >

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Ghulam Gilanie^1,*, 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 >

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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
（This article belongs to the Special Issue: Intelligent reasoning and decision-making towards the explainability of AI)
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 >

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Sepehr Soltani¹, Ehsan Ghafourian², Reza Salehi³, Diego Martín^3,*, 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 >

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M. S. Vivekanandan¹, T. Jesudas^2,*
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 >

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Chao Chen^1,2, Yankun Chen³, Haohai Jin⁴, Li Chen^5,*, 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 >

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