M. Vinoth Kumar^1,*, M. Prakash², M. Naresh Kumar³, H. Abdul Shabeer⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1277-1293, 2023, DOI:10.32604/iasc.2023.025591

Abstract The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the level of disease severity, the… More >

S. Siamala Devi¹, Chandrakala Kuruba², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1873-1887, 2023, DOI:10.32604/iasc.2023.025504

Abstract Wireless sensor networks (WSNs) are projected to have a wide range of applications in the future. The fundamental problem with WSN is that it has a finite lifespan. Clustering a network is a common strategy for increasing the lifetime of WSNs and, as a result, allowing for faster data transmission. The clustering algorithm’s goal is to select the best cluster head (CH). In the existing system, Hybrid grey wolf sunflower optimization algorithm (HGWSFO)and optimal cluster head selection method is used. It does not provide better competence and output in the network. Therefore, the proposed Hybrid Grey Wolf Ant Colony Optimisation… More >

Ganesh Jayaraman^1,*, V R Sarma Dhulipala²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1705-1719, 2023, DOI:10.32604/iasc.2023.025253

Abstract Nowadays, wireless sensor networks play a vital role in our day to day life. Wireless communication is preferred for many sensing applications due its convenience, flexibility and effectiveness. The sensors to sense the environmental factor are versatile and send sensed data to central station wirelessly. The cluster based protocols are provided an optimal solution for enhancing the lifetime of the sensor networks. In this paper, modified K-means ++ algorithm is used to form the cluster and cluster head in an efficient way and the Advanced Energy-Efficient Cluster head selection Algorithm (AEECA) is used to calculate the weighted factor of the… More >

V. Jesus Bobin^*, M. MarsalineBeno

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2493-2511, 2023, DOI:10.32604/iasc.2023.024679

Abstract Three-phase induction motors are becoming increasingly utilized in industrial field due to their better efficiency and simple manufacture. The speed control of an induction motor is essential in a variety of applications, but it is difficult to control. This research analyses the three-phase induction motor’s performance using field-oriented control (FOC) and direct torque control (DTC) techniques. The major aim of this work is to provide a critical evaluation of developing a simple speed controller for induction motors with improving the performance of Induction Motor (IM). For controlling a motor, different optimization approaches are accessible; in this research, a Fuzzy Logic… More >

J. Shanmugapriya^1,*, K. Baskaran²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1581-1594, 2023, DOI:10.32604/iasc.2023.024514

Abstract Smart Grids (SG) is a power system development concept that has received significant attention nationally. SG signifies real-time data for specific communication requirements. The best capabilities for monitoring and controlling the grid are essential to system stability. One of the most critical needs for smart-grid execution is fast, precise, and economically synchronized measurements, which are made feasible by Phasor Measurement Units (PMU). PMUs can provide synchronized measurements and measure voltages as well as current phasors dynamically. PMUs utilize GPS time-stamping at Coordinated Universal Time (UTC) to capture electric phasors with great accuracy and precision. This research tends to Deep Learning… More >

E. Jessy Mol^*, M. Mary Linda

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1471-1489, 2023, DOI:10.32604/iasc.2023.024027

Abstract The prominence of Renewable Energy Sources (RES) in the process of power generation is exponentially increased in the recent days since these sources assist in minimizing the environmental contamination. A grid-tied DFIG (Doubly Fed Induction Generator) based WECS (Wind Energy Conversion System) is introduced in this work, in which a Landsman converter is implemented to improvise the output voltage of PV without any fluctuations. A novel GA (Genetic Algorithm) assisted ANN (Artificial Neural Network) is employed for tracking the Maximum power from PV. Among the rotor and grid side controllers, the former is implemented by combining the stator flux with… More >

D. Anitha^1,*, R. A. Karthika²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2463-2477, 2023, DOI:10.32604/iasc.2023.023361

Abstract Achieving sound communication systems in Under Water Acoustic (UWA) environment remains challenging for researchers. The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts. The development of machine and deep learning algorithms has reduced the burden of achieving reliable and good communication schemes in the underwater acoustic environment. This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA), Time Division Multiple Access(TDMA), and Orthogonal Frequency Division Multiplexing(OFDM) techniques using the hybrid combination of the convolutional neural networks(CNN) and ensemble single… More >

G. Karthiha^*, Dr. S. Allwin

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1753-1765, 2023, DOI:10.32604/iasc.2023.022631

Abstract In spite of the advancement in computerized imaging, many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis. In this way, the research in the zone of image denoising is very dynamic. Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise suppression is a basic and productive methodology. In this paper, Bilateral Filter (BF) based Modular Neural Networks (MNN) has been utilized for speckle noise suppression in the ultrasound image. Initial step the BF filter is used to filter the input image. From the output of BF,… More >

Walid El-Shafai^1,*, Randa Ali¹, Ahmed Sedik², Taha El-Sayed Taha¹, Mohammed Abd-Elnaby³, Fathi E. Abd El-Samie¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1843-1856, 2023, DOI:10.32604/iasc.2023.019198

Abstract The use of intelligent machines to work and react like humans is vital in emerging smart cities. Computer-aided analysis of complex and huge MRI (Magnetic Resonance Imaging) scans is very important in healthcare applications. Among AI (Artificial Intelligence) driven healthcare applications, tumor detection is one of the contemporary research fields that have become attractive to researchers. There are several modalities of imaging performed on the brain for the purpose of tumor detection. This paper offers a deep learning approach for detecting brain tumors from MR (Magnetic Resonance) images based on changes in the division of the training and testing data… More >

Tao Li¹, Zhiwei Jiang^1,*, Rui Han², Jinyue Xia³, Yongjun Ren⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 941-956, 2023, DOI:10.32604/iasc.2023.029644

Abstract A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network’s generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer vision field. Sub-pixel convolution is used instead of transposed convolution or interpolation… More >