Rihem Farkh^1,2, Saad Alhuwaimel^3,*, Sultan Alzahrani³, Khaled Al Jaloud¹, Mohammad Tabrez Quasim⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2811-2824, 2022, DOI:10.32604/cmc.2022.020259 - 29 March 2022

Abstract Several applications of machine learning and artificial intelligence, have acquired importance and come to the fore as a result of recent advances and improvements in these approaches. Autonomous cars are one such application. This is expected to have a significant and revolutionary influence on society. Integration with smart cities, new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles. The autonomous automobile, often known as self-driving systems or driverless vehicles, is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement. Cars are on… More >

Yuhao Chen¹, Jiajun Liu¹, Yaxi Peng¹, Ziyi Liu², Zhipeng Yang^1,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1501-1516, 2022, DOI:10.32604/iasc.2022.026346 - 24 March 2022

Abstract As a neural fingerprint, functional connectivity networks (FCNs) have been used to identify subjects from group. However, a number of studies have only paid attention to cerebral cortex when constructing the brain FCN. Other areas of the brain also play important roles in brain activities. It is widely accepted that the human brain is composed of many highly complex functional networks of cortex. Moreover, recent studies have confirmed correlations between signals of cortex and white matter (WM) bundles. Therefore, it is difficult to reflect the functional characteristics of the brain through a single-layer FCN. In… More >

Ye Yang¹, Dongjie Zhu^2,*, Xiaofang Li³, Haiwen Du⁴, Yundong Sun⁴, Zhixin Huo², Mingrui Wu², Ning Cao¹, Russell Higgs⁵

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1379-1391, 2022, DOI:10.32604/iasc.2022.025181 - 24 March 2022

Abstract In recent years, graph representation learning has played a huge role in the fields and research of node clustering, node classification, link prediction, etc., among which many excellent models and methods have emerged. These methods can achieve better results for model training and verification of data in a single space domain. However, in real scenarios, the solution of cross-domain problems of multiple information networks is very practical and important, and the existing methods cannot be applied to cross-domain scenarios, so we research on cross-domain representation is based on multi-network space integration. This paper conducts representation More >

D. Devina Merin^1,*, P. Jagatheeswari²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1849-1860, 2022, DOI:10.32604/iasc.2022.024495 - 24 March 2022

Abstract The Deep learning (DL) network is an effective technique that has extended application in medicine, robotics, biotechnology, biometrics and communication. The unique architecture of DL networks can be trained according to classify any complex tasks in a limited duration. In the proposed work a deep convolution neural network of DL is trained to classify the antimicrobial activity of silver nanoparticles (AgNP). The process involves two processing steps; synthesis of silver nanoparticles and classification (SEM) of AgNP based on the antimicrobial activity. AgNP images from scanning electron microscope are pre-processed using Adaptive Histogram Equalization in the More >

R. Saravana Ram¹, K. Venkatachalam², Mehedi Masud³, Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1639-1652, 2022, DOI:10.32604/iasc.2022.023962 - 24 March 2022

Abstract In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT… More >

Ajina Mohamed Ameer^*, M. Victor Jose

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1349-1361, 2022, DOI:10.32604/iasc.2022.023924 - 24 March 2022

Abstract This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in… More >

Iqra Abid¹, Sultan Almakdi², Hameedur Rahman³, Ahmed Almulihi⁴, Ali Alqahtani², Khairan Rajab^2,5, Abdulmajeed Alqhatani^2,*, Asadullah Shaikh²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1407-1421, 2022, DOI:10.32604/iasc.2022.023753 - 24 March 2022

Abstract Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided skin cancer diagnosis system is the accurate segmentation of skin malignancy. To achieve this, a specialized skin image analysis technique must be used for the separation of cancerous parts from important healthy skin. This procedure is called Dermatography. Researchers have often used multiple techniques for the analysis of skin images, but, because of their low accuracy, most of these methods have turned out to be at best, inconsistent. Proper clinical treatment… More >

S. Benisha^1,*, J. Anitha Roseline²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1363-1378, 2022, DOI:10.32604/iasc.2022.023012 - 24 March 2022

Abstract The scope of this research is to manage the speed of Permanent Magnet Brushless DC Motor Drive (PMBLDCMD) for various less capacity applications. In the circuit, a 1ϕ AC power is given to Diode Rectifier and the converted DC supply is given to condenser, which leads to abnormal pulsating current. Because of this pulsating current, the power quality disturbances arise at the supply point. Hence, the PMBLDCMD requires Power Factor Correction (PFC) converter for many household and profitable applications. The rotors of PMBLDCMD are driven by 3ϕ voltage source inverter (VSI), which performs electronic commutation.… More >

P. Saravanakumar¹, T. V. P. Sundararajan², Rajesh Kumar Dhanaraj³, Kashif Nisar^4,*, Fida Hussain Memon^5,6, Ag. Asri Bin Ag. Ibrahim⁴

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1835-1847, 2022, DOI:10.32604/iasc.2022.018953 - 24 March 2022

Abstract Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes… More >

V. Ulagamuthalvi¹, G. Kulanthaivel^2,*, A. Balasundaram³, Arun Kumar Sivaraman⁴

Computer Systems Science and Engineering, Vol.43, No.1, pp. 275-289, 2022, DOI:10.32604/csse.2022.023737 - 23 March 2022

Abstract One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image… More >