V. Balaji^*, S. Kanaga Suba Raja

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1301-1315, 2022, DOI:10.32604/iasc.2022.020287

Abstract Autism spectrum disorder (ASD), is a neurological developmental disorder. It affects how people communicate and interact with others, as well as how they behave and learn. The symptoms and signs appear when a child is very young. Derived with increased usage of machine learning procedure in the medicinal analysis investigations. In this paper, our objective is to find out the most significant attributes and automate the process using classification techniques and pattern clustering using K-means clustering. We have analyzed ASD datasets of children towards determining the best performance of classifier for these binary datasets considering recall, precision, accuracy and classification… More >

Thejovathi Murari¹, L. Prathiba², Kranthi Kumar Singamaneni^3,*, D. Venu⁴, Vinay Kumar Nassa⁵, Rachna Kohar⁶, Satyajit Sidheshwar Uparkar⁷

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1241-1256, 2022, DOI:10.32604/iasc.2022.020203

Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural network (BDA-OENN) for clinical decision… More >

Kalaichelvi Nallusamy^*, K. S. Easwarakumar

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 749-765, 2022, DOI:10.32604/iasc.2022.020189

Abstract Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a… More >

Anand Muni Mishra¹, Shilpi Harnal¹, Khalid Mohiuddin², Vinay Gautam¹, Osman A. Nasr², Nitin Goyal¹, Mamdooh Alwetaishi³, Aman Singh^4,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1157-1173, 2022, DOI:10.32604/iasc.2022.020174

Abstract Automation of agricultural food production is growing in popularity in scientific communities and industry. The main goal of automation is to identify and detect weeds in the crop. Weed intervention for the duration of crop establishment is a serious difficulty for wheat in North India. The soil nutrient is important for crop production. Weeds usually compete for light, water and air of nutrients and space from the target crop. This research paper assesses the growth rate of weeds due to macronutrients (nitrogen, phosphorus and potassium) absorbed from various soils (fertile, clay and loamy) in the rabi crop field. The weed… More >

U. Selvi^*, S. Pushpa

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1185-1196, 2022, DOI:10.32604/iasc.2022.020164

Abstract Due to the recent advancement in technologies, a huge amount of data is generated where individual private information needs to be preserved. A proper Anonymization algorithm with increased Data utility is required to protect individual privacy. However, preserving privacy of individuals whileprocessing huge amount of data is a challenging task, as the data contains certain sensitive information. Moreover, scalability issue in handling a large dataset is found in using existing framework. Many an Anonymization algorithm for Big Data have been developed and under research. We propose a method of applying Machine Learning techniques to protect and preserve the personal identities… More >

Prabhakaran Mathialagan^*, Malathy Chidambaranathan

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 929-946, 2022, DOI:10.32604/iasc.2022.020163

Abstract Nasal cavity and paranasal sinus tumours that occur in the respiratory tract are the most life-threatening disease in the world. The human respiratory tract has many sites which has different mucosal lining like frontal, parred, sphenoid and ethmoid sinuses. Nasal cavity tumours can occur at any different mucosal linings and chances of prognosis possibility from one nasal cavity site to another site is very high. The paranasal sinus tumours can metastases to oral cavity and digestive tracts may lead to excessive survival complications. Grading the respiratory tract tumours with dysplasia cases are more challenging using manual pathological procedures. Manual microscopic… More >

Kalaphath Kounlaxay¹, Dexiang Yao¹, Min Woo Ha^2,3, Soo Kyun Kim^4,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1119-1130, 2022, DOI:10.32604/iasc.2022.020151

Abstract Virtual reality (VR) is an advanced technology widely used in many fields. Education is essential for human resources development, and the use of technology in education can enhance teaching and learning methods. This study aims to present new methods and tools for visual and interactive education in organic chemistry. The experimental design and chemical equipment used in this research are based on the basic theory of organic chemistry, and the related materials are simulated as three-dimensional (3D) models to perform the experiments in a VR system. Chemical reactions are simulated by mixing the chemicals, and the students can observe the… More >

Tuğrul Çavdar¹, Faruk Baturalp Günay^2,*, Nader Ebrahimpour¹, Muhammet Talha Kakız³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1197-1222, 2022, DOI:10.32604/iasc.2022.020127

Abstract Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an… More >

T. Manoj Kumar^1,*, P. Karthigaikumar²

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1025-1036, 2022, DOI:10.32604/iasc.2022.020090

Abstract

Data security is an essential aspect of data communication and data storage. To provide high-level security against all kinds of unauthorized accesses, cryptographic algorithms have been applied to various fields such as medical and military applications. Advanced Encryption Standard (AES), a symmetric cryptographic algorithm, is acknowledged as the most secure algorithm for the cryptographic process globally. Several modifications have been made to the original architecture after it was proposed by two Belgian researchers, Joan Daemen and Vincent Rijment, at the third AES candidate Conference in 2000. The existing modifications aim to increase security and speed. This paper proposes an efficient… More >

Nehmdoh A. Sabiha^*, Hend I. Alkhammash

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1107-1118, 2022, DOI:10.32604/iasc.2022.020070

Abstract Ferroresonance occurrence represents a very dangerous phenomenon to electric power systems. Concerning the recent trend of the applications of grid-connected wind farms, this phenomenon can lead to undesired overvoltages stressing the wind farm components. In this paper, the ferroresonance overvoltages are studied and mitigated for the grid-connected wind farm. Single-pole switching of the breaker is considered, where it is the most famous reason behind the ferroresonance transient events in the electric power systems. During the ferroresonance period, the transient voltage of the network is increased to more than three times the voltage level and associated with harmonics. Surge arrester is… More >