Ravi Samikannu^1,*, K. Vinoth², Narasimha Rao Dasari³, Senthil Kumar Subburaj⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 983-995, 2023, DOI:10.32604/iasc.2023.026069

Abstract Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit. Renewable energy sources are playing a significant role in the modern energy system with rapid development. In renewable sources like fuel combustion and solar energy, the generated voltages change due to their environmental changes. To develop energy resources, electric power generation involved huge awareness. The power and output voltages are plays important role in our work but it not considered in the existing system. For considering the power and voltage, Gaussian PI Controller-Maxpooling Deep Convolutional Neural… More >

Tamara al Shloul¹, Madiha Javeed², Munkhjargal Gochoo³, Suliman A. Alsuhibany⁴, Yazeed Yasin Ghadi⁵, Ahmad Jalal², Jeongmin Park^6,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 149-161, 2023, DOI:10.32604/iasc.2023.026051

Abstract Due to the recently increased requirements of e-learning systems, multiple educational institutes such as kindergarten have transformed their learning towards virtual education. Automated student health exercise is a difficult task but an important one due to the physical education needs especially in young learners. The proposed system focuses on the necessary implementation of student health exercise recognition (SHER) using a modified Quaternion-based filter for inertial data refining and data fusion as the pre-processing steps. Further, cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal patterns. Furthermore, these… More >

Kavitha Muthukumaran^*, K. Hariharanath

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 521-536, 2023, DOI:10.32604/iasc.2023.025968

Abstract Recently, data science techniques utilize artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to take an influence and grow their businesses. For SMEs, owing to the inexistence of consistent data and other features, evaluating credit risks is difficult and costly. On the other hand, it becomes necessary to design efficient models for predicting business failures or financial crises of SMEs. Various data classification approaches for financial crisis prediction (FCP) have been presented for predicting the financial status of the organization by the use of past data. A major process involved in the design of FCP… More >

A. Soujanya^1,*, N. Nandhagopal²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 675-687, 2023, DOI:10.32604/iasc.2023.025930

Abstract Due to the rising occurrence of skin cancer and inadequate clinical expertise, it is needed to design Artificial Intelligence (AI) based tools to diagnose skin cancer at an earlier stage. Since massive skin lesion datasets have existed in the literature, the AI-based Deep Learning (DL) models find useful to differentiate benign and malignant skin lesions using dermoscopic images. This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet (ARGS-OEN) technique for skin lesion segmentation and classification. The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower… More >

R. Priyadharsini^*, A. Kunthavai

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 261-279, 2023, DOI:10.32604/iasc.2023.025884

Abstract Radio waves and strong magnetic fields are used by Magnetic Resonance Imaging (MRI) scanners to detect tumours, wounds and visualize detailed images of the human body. Wi-Fi and other medical devices placed in the MRI procedure room produces RF noise in MRI Images. The RF noise is the result of electromagnetic emissions produced by Wi-Fi and other medical devices that interfere with the operation of the MRI scanner. Existing techniques for RF noise mitigation involve RF shielding techniques which induce eddy currents that affect the MRI image quality. RF shielding techniques are complex and lead to RF leakage. VLC (Visible… More >

Soma Prathibha^1,*, Deepak Dahiya², C. R. Rene Robin³, Cherukuru Venkata Nishkala⁴, S. Swedha⁵

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 199-214, 2023, DOI:10.32604/iasc.2023.025819

Abstract Thyroid disease is a medical condition caused due to the excess release of thyroid hormone. It is released by the thyroid gland which is in front of the neck just below the larynx. Medical pictures such as X-rays and CT scans can, however, be used to diagnose it. In this proposed model, Deep Learning technology is used to detect thyroid diseases. A Convolution Neural Network (CNN) based modified ResNet architecture is employed to detect five different types of thyroid diseases namely 1. Hypothyroid 2. Hyperthyroid 3. Thyroid cancer 4. Thyroiditis 5. Thyroid nodules. In the proposed work, the training method… More >

C. D. Prem Kumar^1,*, S. Saravana Sundaram²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 881-899, 2023, DOI:10.32604/iasc.2023.025766

Abstract The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique. The presented ODTL-CMFD technique aims to derive a DL model for the classification of… More >

J. Priya^*, C. Palanisamy

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 243-259, 2023, DOI:10.32604/iasc.2023.025719

Abstract The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internet and computing resources. In recent years, many more IoT applications have been extensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstacles faced by the extensive acceptance and usage of these emerging technologies are security and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, the existing system has issues with specific security issues, privacy-preserving rate, information loss, etc.… More >

Mahmoud Helal^1,2, Elsayed Fathallah^3,4, Abdulaziz H Alghtani¹, Hussein Shawki Osman⁵, Jong Wan Hu^6,7,*, Hasan Eleashy⁸

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 769-793, 2023, DOI:10.32604/iasc.2023.025618

Abstract To increase the payload, reduce energy consumption, improve work efficiency and therefore must accordingly reduce the total hull weight of the submersible. This paper introduces a design optimization process for the pressure-hull of submarines under uniform external hydrostatic pressure using both finite element analysis (FEA) and optimization tools. A comprehensive study about the optimum design of the pressure hull, to minimize the weight and increase the volume, to reach minimum buoyancy factor and maximum operating depth minimizing the buoyancy factor (B.F) is taken as an objective function with constraints of plate and frame yielding, general instability and deflection. The optimization… More >

Yassir Edrees Almalki¹, Maryam Zaffar^2,*, Muhammad Irfan³, Mohammad Ali Abbas², Maida Khalid², K.S. Quraishi⁴, Tariq Ali⁵, Fahad Alshehri⁶, Sharifa Khalid Alduraibi⁶, Abdullah A. Asiri⁷, Mohammad Abd Alkhalik Basha⁸, Alaa Alduraibi⁶, M.K. Saeed⁷, Saifur Rahman³

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 163-180, 2023, DOI:10.32604/iasc.2023.025580

Abstract The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients’ images of category X-rays are utilized to evaluate the performance of… More >