Mohammad Sh. Daoud¹, Shabib Aftab^2,3, Munir Ahmad², Muhammad Adnan Khan^4,5,*, Ahmed Iqbal³, Sagheer Abbas², Muhammad Iqbal², Baha Ihnaini^6,7

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1287-1300, 2022, DOI:10.32604/iasc.2022.020362

Abstract Production of high-quality software at lower cost has always been the main concern of developers. However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible. This issue can be resolved by identifying defects at the early stages of the development lifecycle. As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality. An artificial neural network is considered as one of… More >

Sung-Jung Hsiao¹, Wen-Tsai Sung^2,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 729-748, 2022, DOI:10.32604/iasc.2022.020332

Abstract In this study, a multisensory physiological measurement system was built with wireless transmission technology, using a DSPIC30F4011 as the master control center and equipped with physiological signal acquisition modules such as an electrocardiogram module, blood pressure module, blood oxygen concentration module, and respiratory rate module. The physiological data were transmitted wirelessly to Android-based mobile applications via the TCP/IP or Bluetooth serial ports of Wi-Fi. The Android applications displayed the acquired physiological signals in real time and performed a preliminary abnormity diagnosis based on the measured physiological data and built-in index diagnostic data provided by doctors, such as blood oxygen concentration,… More >

Abdullah Baz¹, Mohammed Baz^2,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1051-1069, 2022, DOI:10.32604/iasc.2022.020304

Abstract COVID-19 constitutes one of the devastating pandemics plaguing humanity throughout the centuries; within about 18 months since its appearing, the cumulative confirmed cases hit 173 million, whereas the death toll approaches 3.72 million. Although several vaccines became available for the public worldwide, the speed with which COVID-19 is spread, and its different mutant strains hinder stopping its outbreak. This, in turn, prompting the desperate need for devising fast, cheap and accurate tools via which the disease can be diagnosed in its early stage. Reverse Transcription Polymerase Chain Reaction (RTPCR) test is the mainstay tool used to detect the COVID-19 symptoms.… More >

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 >