Journals / IASC / Vol.28, No.1

  • ARTICLE

    Artificial Intelligence Based Language Translation Platform

    Manjur Kolhar*, Abdalla Alameen
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 1-9, 2021, DOI:10.32604/iasc.2021.014995
    Abstract The use of computer-based technologies by non-native Arabic-speaking teachers for teaching native Arabic-speaking students can result in higher learner engagement. In this study, a machine translation (MT) system is developed as a learning technology. The proposed system can be linked to a digital podium and projector to reduce multitasking. A total of 25 students from Prince Sattam Bin Abdulaziz University, Saudi Arabia participated in our experiment and survey related to the use of the proposed technology-enhanced MT system. An important reason for using this framework is to exploit the high service bandwidth (up to several bandwidths) made available for interactive… More >

  • ARTICLE

    Building Information Modeling Based Automated Building Regulation Compliance Checking Asp.net Web Software

    Murat Aydın*
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 11-25, 2021, DOI:10.32604/iasc.2021.015065
    (This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)
    Abstract Building regulations used in the architecture, engineering, and construction sectors are legal documents prepared under the control of local authorities for use by individuals. These regulations determine the conditions for ensuring performance and quality throughout the entire construction process. The building regulation inspection process conducted with the traditional manual method is time-consuming and error-prone for architects, engineers, and local authorities. It is known that most of these inspections are carried out with municipalities by local authorities. The mutual interview study and literature review shows that there is no standard rule for the legal auditing process and the same services are… More >

  • ARTICLE

    Multifactorial Disease Detection Using Regressive Multi-Array Deep Neural Classifier

    D. Venugopal1, T. Jayasankar2,*, 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 >

  • ARTICLE

    Selection and Optimization of Software Development Life Cycles Using a Genetic Algorithm

    Fatimah O. Albalawi, Mashael S. Maashi*
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 39-52, 2021, DOI:10.32604/iasc.2021.015657
    (This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
    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 >

  • ARTICLE

    Automatic Sleep Staging Based on EEG-EOG Signals for Depression Detection

    Jiahui Pan1,6,*, Jianhao Zhang1, Fei Wang1,6, Wuhan Liu2, Haiyun Huang3,6, Weishun Tang3, Huijian Liao4, Man Li5, Jianhui Wu1, Xueli Li2, Dongming Quan2, Yuanqing Li3,6
    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 >

  • ARTICLE

    Constructional Cyber Physical System: An Integrated Model

    Tzer-Long Chen1, Chien-Yun Chang2, Yung-Cheng Yao3, Kuo-Chang Chung4,*
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 73-82, 2021, DOI:10.32604/iasc.2021.015980
    (This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
    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 >

  • ARTICLE

    Filter-Based Feature Selection and Machine-Learning Classification of Cancer Data

    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 >

  • ARTICLE

    Soil Moisture Prediction in Peri-urban Beijing, China: Gene Expression Programming Algorithm

    Hongfei Niu1,2, Fanyu Meng3, Huanfang Yue3, Lihong Yang4, Jing Dong2,5, Xin Zhang2,5,*
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 93-106, 2021, DOI:10.32604/iasc.2021.010131
    Abstract Soil moisture is an important indicator for agricultural planting and agricultural water management. People have been trying to guide crop cultivation, formulate irrigation systems, and develop intelligent agriculture by knowing exactly what the soil moisture is in real time. This paper considers the impact of meteorological parameters on soil-moisture change and proposes a soil-moisture prediction method based on the Gene Expression Programming (GEP) algorithm. The prediction model is tested on datasets from Shunyi, Yanqing and Daxing agricultural farms, Beijing. The results show that the GEP model can predict soil moisture with a maximum correlation coefficient of 0.98, and the root-mean-square… More >

  • ARTICLE

    Sentiment Analysis for Arabic Social Media News Polarity

    Adnan A. Hnaif1,*, Emran Kanan2, Tarek Kanan1
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 107-119, 2021, DOI:10.32604/iasc.2021.015939
    (This article belongs to this Special Issue: Secure Big Data Analytics for Smart City)
    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 >

  • ARTICLE

    Oral English Speech Recognition Based on Enhanced Temporal Convolutional Network

    Hao Wu1,*, Arun Kumar Sangaiah2
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 121-132, 2021, DOI:10.32604/iasc.2021.016457
    Abstract In oral English teaching in China, teachers usually improve students’ pronunciation by their subjective judgment. Even to the same student, the teacher gives different suggestions at different times. Students’ oral pronunciation features can be obtained from the reconstructed acoustic and natural language features of speech audio, but the task is complicated due to the embedding of multimodal sentences. To solve this problem, this paper proposes an English speech recognition based on enhanced temporal convolution network. Firstly, a suitable UNet network model is designed to extract the noise of speech signal and achieve the purpose of speech enhancement. Secondly, a network… More >

  • ARTICLE

    Automatic BIM Indoor Modelling from Unstructured Point Clouds Using a Convolutional Neural Network

    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 >

  • ARTICLE

    Hybrid Deep Learning Modeling for Water Level Prediction in Yangtze River

    Zhaoqing Xie1,*, Qing Liu2, Yulian Cao3
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 153-166, 2021, DOI:10.32604/iasc.2021.016246
    Abstract Accurate prediction of water level in inland waterway has been an important issue for helping flood control and vessel navigation in a proactive manner. In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction. The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic water level features through layer-by-layer feature granulation with a greedy layer wise unsupervised learning algorithm. Six representative reaches in Yangtze… More >

  • ARTICLE

    Assessing User’s Susceptibility and Awareness of Cybersecurity Threats

    Maha M. Althobaiti*
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 167-177, 2021, DOI:10.32604/iasc.2021.016660
    (This article belongs to this Special Issue: Humans and Cyber Security Behaviour)
    Abstract Cybersecurity threats, including those involving machine learning, malware, phishing, and cryptocurrency, have become more sophisticated. They target sensitive information and put institutions, governments, and individuals in a continual state of risk. In 2019, phishing attacks became one of the most common and dangerous cyber threats. Such attacks attempt to steal sensitive data, such as login and payment card details, from financial, social, and educational websites. Many universities have suffered data breaches, serving as a prime example of victims of attacks on educational websites. Owing to advances in phishing tactics, strategies, and technologies, the end-user is the main victim of an… More >

  • ARTICLE

    Managing Software Security Risks through an Integrated Computational Method

    Abdullah Alharbi1, Wael Alosaimi1, Hashem Alyami2, Mohd Nadeem3, Mohd Faizan3, Alka Agrawal3, Rajeev Kumar3,4,*, Raees Ahmad Khan3
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 179-194, 2021, DOI:10.32604/iasc.2021.016646
    Abstract Security risk evaluation of web-based healthcare applications is important from a design perspective. The developers as well as the users need to make sure that the applications must be secure. Citing the disastrous effects of unsecured web applications, Accuntix Online states that the IT industry has lost millions of dollars due to security theft and malware attacks. Protecting the integrity of patients’ health data is of utmost importance. Thus, assessing the security risk of web-based healthcare applications should be accorded the highest priority while developing the web applications. To fulfill the security requirements, the developers must meticulously follow the Multi-Criteria… More >

  • ARTICLE

    Mixed Re-Sampled Class-Imbalanced Semi-Supervised Learning for Skin Lesion Classification

    Ye Tian1, Liguo Zhang1,2, Linshan Shen1,*, Guisheng Yin1, Lei Chen3
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 195-211, 2021, DOI:10.32604/iasc.2021.016314
    Abstract Skin cancer is one of the most common types of cancer in the world, melanoma is considered to be the deadliest type among other skin cancers. Quite recently, automated skin lesion classification in dermoscopy images has become a hot and challenging research topic due to its essential way to improve diagnostic performance, thus reducing melanoma deaths. Convolution Neural Networks (CNNs) are at the heart of this promising performance among a variety of supervised classification techniques. However, these successes rely heavily on large amounts of class-balanced clearly labeled samples, which are expensive to obtain for skin lesion classification in the real… More >

  • ARTICLE

    Infrared and Visible Image Fusion Based on NSST and RDN

    Peizhou Yan1, Jiancheng Zou2,*, Zhengzheng Li1, Xin Yang3
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 213-225, 2021, DOI:10.32604/iasc.2021.016201
    Abstract Within the application of driving assistance systems, the detection of driver’s facial features in the cab for a spectrum of luminosities is mission critical. One method that addresses this concern is infrared and visible image fusion. Its purpose is to generate an aggregate image which can granularly and systematically illustrate scene details in a range of lighting conditions. Our study introduces a novel approach to this method with marked improvements. We utilize non-subsampled shearlet transform (NSST) to obtain the low and high frequency sub-bands of infrared and visible imagery. For the low frequency sub-band fusion, we incorporate the local average… More >

  • ARTICLE

    Design and Validation of a Route Planner for Logistic UAV Swarm

    Meng-Tse Lee1,*, 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
    (This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
    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 >

  • ARTICLE

    Analysis of Iterative Process for Nauru Voting System

    Neelam Gohar1,*, 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 >

  • ARTICLE

    A Technology Enabled Learning Model in Healthcare during COVID-19

    Habib Ur Rahman1,*, Nazir Ahmed Sangi2, Moiz Uddin Ahmed1
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 261-275, 2021, DOI:10.32604/iasc.2021.016107
    (This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)
    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 >

  • ARTICLE

    A K-means++ Based User Classification Method for Social E-commerce

    Haoliang Cui1, Shaozhang Niu1, Keyue Li1,*, Chengjie Shi2, Shuai Shao3, Zhenguang Gao4
    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 277-291, 2021, DOI:10.32604/iasc.2021.016408
    Abstract At present, the research on the classification of e-commerce users is relatively mature, but with the rise of mobile social networks, the combination of social networks and e-commerce networks has become a trend and is developing rapidly. Traditional e-commerce user classification methods are not suitable for social e-commerce users. Therefore, based on the research on traditional e-commerce user classification methods, according to the characteristics of social e-commerce users, we improved data preprocessing and parameter tuning methods, and proposed a clustering method of social e-commerce users based on the K-means++ algorithm. The test on the actual data of social e-commerce users… More >

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