Nisha Vasudevan^1,*, Vasudevan Venkatraman², A. Ramkumar¹, T. Muthukumar³, A. Sheela⁴, M. Vetrivel⁵, R. J. Vijaya Saraswathi⁶, F. T. Josh⁷

Energy Engineering, Vol.120, No.8, pp. 1747-1761, 2023, DOI:10.32604/ee.2023.027821

Abstract MigroGrid (MG) has emerged to resolve the growing demand for energy. But because of its inconsistent output, it can result in various power quality (PQ) issues. PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources. Similarly, the employment of nonlinear loads will introduce harmonics into the system and, as a result, cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system. Thus, this research focuses on power quality enhancement in the MG using hybrid shunt filters. However, the… More >

S. Deva Kumar, S. Venkatramaphanikumar^*, K. Venkata Krishna Kishore

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165

Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge the model’s width by substituting… More >

E. Jenifer Sweetlin^*, S. Saudia

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 343-367, 2023, DOI:10.32604/iasc.2023.036742

Abstract This paper proposes a hybrid feature selection sequence complemented with filter and wrapper concepts to improve the accuracy of Machine Learning (ML) based supervised classifiers for classifying the survivability of breast cancer patients into classes, living and deceased using METABRIC and Surveillance, Epidemiology and End Results (SEER) datasets. The ML-based classifiers used in the analysis are: Multiple Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The workflow of the proposed ML algorithm sequence comprises the following stages: data cleaning, data balancing, feature selection via a filter and wrapper sequence, cross validation-based training, testing and… More >

P. Venkatesh^1,*, N. Visali²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1001-1012, 2023, DOI:10.32604/iasc.2023.036268

Abstract In the conventional technique, in the evaluation of the severity index, clustering and loading suffer from more iteration leading to more computational delay. Hence this research article identifies, a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects. The polynomial load modelling or ZIP (constant impedances (Z), Constant Current (I) and Constant active power (P)) is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security. The process of finding the severity of the line using a Hybrid Line Stability Ranking Index (HLSRI) is used for… More >

R. D. Dhaniya¹, K. M. Umamaheswari^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1129-1143, 2023, DOI:10.32604/iasc.2023.035905

Abstract Current revelations in medical imaging have seen a slew of computer-aided diagnostic (CAD) tools for radiologists developed. Brain tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging (MRI). In this work, we reported on new observations based on binary brain tumor categorization using HYBRID CNN-LSTM. Initially, the collected image is pre-processed and augmented using the following steps such as rotation, cropping, zooming, CLAHE (Contrast Limited Adaptive Histogram Equalization), and Random Rotation with panoramic stitching (RRPS). Then, a method called particle swarm optimization (PSO) is used to segment tumor regions in an MR image. After… More >

Rajesh Bharati^*, Vahida Attar

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 763-775, 2023, DOI:10.32604/iasc.2023.035503

Abstract Online Transaction Processing (OLTP) gets support from data partitioning to achieve better performance and scalability. The primary objective of database and application developers is to provide scalable and reliable database systems. This research presents a novel method for data partitioning and load balancing for scalable transactions. Data is efficiently partitioned using the hybrid graph partitioning method. Optimized load balancing (OLB) approach is applied to calculate the weight factor, average workload, and partition efficiency. The presented approach is appropriate for various online data transaction applications. The quality of the proposed approach is examined using OLTP database benchmark. The performance of the… More >

Ashvini Kulkarni^1,2, P. Augusta Sophy Beulet^1,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 127-146, 2023, DOI:10.32604/iasc.2023.035144

Abstract It is widely acknowledged that navigation is a significant source of between sites. The Global Positioning System (GPS) has numerous navigational advancements, and hence it is used widely. GPS navigation can be compromised at any level between position, location, and estimation, to the detriment of the user. Consequently, a navigation system requires the precise location and underpinning tracking of an object without signal loss. The objective of a hybrid environment prediction system is to foresee the location of the user and their territory by employing a variety of sensors for position estimation and monitoring navigation. This article presents a state… More >

M. Muthukumar¹, Rajasekar Rangasamy², Irshad Hussain^3,4, Salman A. AlQahtani^4,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1111-1127, 2023, DOI:10.32604/iasc.2023.034369

Abstract The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing (S-ELHR), a low latency routing protocol that aims to provide a stable mechanism for routing in unmanned aerial vehicles (UAV). The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set (CDS) using a parameter known as the Stability Metric (SM). The SM considers the node’s energy usage, connectivity time, and node’s degree. Only the highest SM nodes are chosen to form CDS. Each node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors, by… More >

S. R. Deepa¹, M. Subramoniam^2,*, R. Swarnalatha³, S. Poornapushpakala², S. Barani²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211

Abstract The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and Tompkins algorithm is employed to… More >

Saleh Naif Almuayqil¹, Reham Arnous^2,*, Noha Sakr³, Magdy M. Fadel³

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5177-5192, 2023, DOI:10.32604/cmc.2023.038625

Abstract Skin segmentation participates significantly in various biomedical applications, such as skin cancer identification and skin lesion detection. This paper presents a novel framework for segmenting the skin. The framework contains two main stages: The first stage is for removing different types of noises from the dermoscopic images, such as hair, speckle, and impulse noise, and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network (U-Net). The framework uses variational Autoencoders (VAEs) for removing the hair noises, the Generative Adversarial Denoising Network (DGAN-Net), the Denoising U-shaped U-Net (D-U-NET), and Batch Renormalization U-Net (Br-U-NET) for… More >