S. Kavi Priya^*, K. Pon Karthika

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160 - 05 January 2023

Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and… More >

Valarmathi Ramasamy^1,*, Dhandapani Samiappan², R. Ramesh³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1365-1380, 2023, DOI:10.32604/iasc.2023.032155 - 05 January 2023

Abstract Owing to the recent trends in remote health monitoring, real-time applications for measuring Heartbeat Rate and Respiration Rate (HARR) from video signals are growing rapidly. Photo Plethysmo Graphy (PPG) is a method that is operated by estimating the infinitesimal change in color of the human face, rigid motion of facial skin and head parts, etc. Ballisto Cardiography (BCG) is a nonsurgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses. The resilience against motion artifacts induced by luminance fluctuation and the patient’s mobility variation… More >

Umamaheswari Ramalingam^1,*, Senthil Kumar Murugesan², Karthikeyan Lakshmanan², Chidhambararajan Balasubramaniyan³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1733-1744, 2023, DOI:10.32604/iasc.2023.032108 - 05 January 2023

Abstract A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS).… More >

E. Bharath^1,*, T. Rajagopalan²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1417-1433, 2023, DOI:10.32604/iasc.2023.032102 - 05 January 2023

Abstract Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made… More >

G. Murugesan^1,*, J. Jeyapriya², M. Hemalatha³, S. Rajeshkannan⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2099-2109, 2023, DOI:10.32604/iasc.2023.032068 - 05 January 2023

Abstract The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of… More >

P. S. Ramapraba^1,*, P. Epsiba², K. Umapathy³, E. Sivanantham⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2177-2189, 2023, DOI:10.32604/iasc.2023.032040 - 05 January 2023

Abstract The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the… More >

V. Nandagopal^1,*, T. S. Balaji Damodhar², P. Vijayapriya³, A. Thamilmaran³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1315-1329, 2023, DOI:10.32604/iasc.2023.032039 - 05 January 2023

Abstract

The development of non-linear loads at consumers has significantly impacted power supply systems. Since, the poor power quality has been found in the three-phase distribution system due to unbalanced loads, harmonic current, undesired voltage regulation, and extreme reactive power demand. To overcome this issue, Distributed STATicCOMpensator (DSTATCOM) is implemented. DSTATCOM is a shunt-connected Voltage Source Converter (VSC) that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor. DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation. A rectified resistive and

… More >

Anju Shukla¹, Mohammad Zubair Khan², Shishir Kumar^3,*, Abdulrahman Alahmadi², Reem Ibrahim A. Altamimi², Ahmed H. Alahmadi²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2281-2297, 2023, DOI:10.32604/iasc.2023.031931 - 05 January 2023

Abstract Integrated CloudIoT is an emerging field of study that integrates the Cloud and the Internet of Things (IoT) to make machines smarter and deal with real-world objects in a distributed manner. It collects data from various devices and analyses it to increase efficiency and productivity. Because Cloud and IoT are complementary technologies with distinct areas of application, integrating them is difficult. This paper identifies various CloudIoT issues and analyzes them to make a relational model. The Interpretive Structural Modeling (ISM) approach establishes the interrelationship among the problems identified. The issues are categorised based on driving… More >

Nabilah Abughazalah¹, Iqra Ishaque², Majid Khan^2,*, Ammar S. Alanazi³, Iqtadar Hussain^4,5

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1451-1463, 2023, DOI:10.32604/iasc.2023.031855 - 05 January 2023

Abstract In today’s digital world, the most inevitable challenge is the protection of digital information. Due to the weak confidentiality preserving techniques, the existing world is facing several digital information breaches. To make our digital data indecipherable to the unauthorized person, a technique for finding a cryptographically strong Substitution box (S-box) have presented. An S-box with sound cryptographic assets such as nonlinearity (NL), strict avalanche criterion (SAC), bit independence criteria (BIC), bit independence criteria of nonlinearity (BIC-NL), Bit independence criteria of Strict avalanche criteria (BIC-SAC), and Input/output XOR is considered as the robust S-box. The Decision-Making… More >

R. Ann Caroline Jenifer^*, M. A. Bhagyaveni, V. Saroj Malini, M. Shanmugapriya

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2165-2176, 2023, DOI:10.32604/iasc.2023.031840 - 05 January 2023

Abstract Fifth generation (5G) wireless networks must meet the needs of emerging technologies like the Internet of Things (IoT), Vehicle-to-everything (V2X), Video on Demand (VoD) services, Device to Device communication (D2D) and many other bandwidth-hungry multimedia applications that connect a huge number of devices. 5G wireless networks demand better bandwidth efficiency, high data rates, low latency, and reduced spectral leakage. To meet these requirements, a suitable 5G waveform must be designed. In this work, a waveform namely Shaped Offset Quadrature Phase Shift Keying based Orthogonal Frequency Division Multiplexing (SOQPSK-OFDM) is proposed for 5G to provide bandwidth… More >