Habib Ur Rahman^1,*, Nazir Ahmed Sangi², Moiz Uddin Ahmed¹

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 261-275, 2021, DOI:10.32604/iasc.2021.016107

Abstract The World Health Organization has warned about the spread of communicable and non-communicable diseases especially in the developing countries. The COVID–19 has also emerged as one of the most challengeable pandemics of the whole world. In current medical emergency, the virtual health education is much vital for handling alerts and outbreaks of diseases for a community of users. The Information and Communication Technology provide an opportunity to deal with the challenges related to handling alerts and outbreaks of diseases. The technology infrastructure in the developing countries is surging rise and can be used to develop Technology Enabled Learning Solutions for… More >

Tzer-Long Chen¹, Chien-Yun Chang², Yung-Cheng Yao³, Kuo-Chang Chung^4,*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 73-82, 2021, DOI:10.32604/iasc.2021.015980

Abstract Artificial intelligence, machine learning, and deep learning have achieved great success in the fields of computer vision and natural language processing, and then extended to various fields, such as biology, chemistry, and civil engineering, including big data in the field of logistics. Therefore, many logistics companies move towards the integration of intelligent transportation systems. Only virtual and physical development can support the sustainable development of the logistics industry. This study aims to: 1.) collect timely information from the block chain, 2.) use deep learning to build a customer database so that sales staff in physical stores can grasp customer preferences,… More >

Jiahui Pan^1,6,*, Jianhao Zhang¹, Fei Wang^1,6, Wuhan Liu², Haiyun Huang^3,6, Weishun Tang³, Huijian Liao⁴, Man Li⁵, Jianhui Wu¹, Xueli Li², Dongming Quan², Yuanqing Li^3,6

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 53-71, 2021, DOI:10.32604/iasc.2021.015970

Abstract In this paper, an automatic sleep scoring system based on electroencephalogram (EEG) and electrooculogram (EOG) signals was proposed for sleep stage classification and depression detection. Our automatic sleep stage classification method contained preprocessing based on independent component analysis, feature extraction including spectral features, spectral edge frequency features, absolute spectral power, statistical features, Hjorth features, maximum-minimum distance and energy features, and a modified ReliefF feature selection. Finally, a support vector machine was employed to classify four states (awake, light sleep [LS], slow-wave sleep [SWS] and rapid eye movement [REM]). The overall accuracy of the Sleep-EDF database reached 90.10 ± 2.68% with… More >

Adnan A. Hnaif^1,*, Emran Kanan², Tarek Kanan¹

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 107-119, 2021, DOI:10.32604/iasc.2021.015939

Abstract In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a corpus of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate the data. Therefore, Stop-Word… More >

Fatimah O. Albalawi, Mashael S. Maashi^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 39-52, 2021, DOI:10.32604/iasc.2021.015657

Abstract In the software field, a large number of projects fail, and billions of dollars are spent on these failed projects. Many software projects are also produced with poor quality or they do not exactly meet customers’ expectations. Moreover, these projects may exceed project budget and/or time. The complexity of managing software development projects and the poor selection of software development life cycle (SDLC) models are among the top reasons for such failure. Various SDLC models are available, but no model is considered the best or worst. In this work, we propose a new methodology that solves the SDLC optimization problem… More >

Neelam Gohar^1,*, Sidra Niaz¹, Mamoona Naveed Asghar², Salma Noor¹

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 241-259, 2021, DOI:10.32604/iasc.2021.015461

Abstract Game theory is a popular area of artificial intelligence in which the voter acknowledges his own desires and favors the person he wants to be his representative. In multi-agent systems, social choice functions help aggregate agents’ different preferences over alternatives into a single choice. Since all voting rules are susceptible to manipulation, the analysis of elections is complicated by the possibility of voter manipulation attempts. One approach to understanding elections is to treat them as an iterative process and see if we can reach an equilibrium point. Meir et al. proposed an iterative process to reach a stable outcome, i.e.,… More >

Mohammed Farsi^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 83-92, 2021, DOI:10.32604/iasc.2021.015460

Abstract Microarray cancer data poses many challenges for machine-learning (ML) classification including noisy data, small sample size, high dimensionality, and imbalanced class labels. In this paper, we propose a framework to address these problems by properly utilizing feature-selection techniques. The most important features of the cancer datasets were extracted with Logistic Regression (LR), Chi-2, Random Forest (RF), and LightGBM. These extracted features served as input columns in an applied classification task. This framework’s main advantages are reducing time complexity and the number of irrelevant features for the dataset. For evaluation, the proposed method was compared to models using Support Vector Machine… More >

Meng-Tse Lee^1,*, Ying-Chih Lai², Ming-Lung Chuang¹, Bo-Yu Chen¹

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 227-240, 2021, DOI:10.32604/iasc.2021.015339

Abstract Unmanned Aerial Vehicles (UAV) are widely used in different fields of aviation today. The efficient delivery of packages by drone may be one of the most promising applications of this technology. In logistic UAV missions, due to the limited capacities of power supplies, such as fuel or batteries, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Thus, multiple unmanned vehicles with well-planned routes become necessary to minimize the unnecessary consumption of time, distance, and energy while carrying out the delivery missions. The aim of the present study was to develop a multiple-vehicle mission dispatch system… More >

Uuganbayar Gankhuyag, Ji-Hyeong Han^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 133-152, 2021, DOI:10.32604/iasc.2021.015227

Abstract The automated reconstruction of building information modeling (BIM) objects from unstructured point cloud data for indoor as-built modeling is still a challenging task and the subject of much ongoing research. The most important part of the process is to detect the wall geometry clearly. A popular method is first to segment and classify point clouds, after which the identified segments should be clustered according to their corresponding objects, such as walls and clutter. To perform this process, a major problem is low-quality point clouds that are noisy, cluttered and that contain missing parts in the data. Moreover, the size of… More >

D. Venugopal¹, T. Jayasankar^2,*, N. Krishnaraj³, S. Venkatraman⁴, N. B. Prakash⁵, G. R. Hemalakshmi⁵

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 27-38, 2021, DOI:10.32604/iasc.2021.015205

Abstract Comprehensive evaluation of common complex diseases associated with common gene mutations is currently a hot area of human genome research into causative new developments. A multi-fractal analysis of the genome is performed by placing the entire DNA sequence into smaller fragments and using the chaotic game representation and systematic methods to calculate the general dimensional spectrum of each fragment. This is a time consuming process as it uses floating point to represent large data sets and requires processing time. The proposed Regressive Multi-Array Deep Neural Classifier (RMDNC) system is implemented to reduce the computation time, it is called a polymorphic… More >