Adil Hussain Seh¹, Jehad F. Al-Amri², Ahmad F. Subahi³, Alka Agrawal¹, Rajeev Kumar^4,*, Raees Ahmad Khan¹

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 697-712, 2021, DOI:10.32604/iasc.2021.018048

Abstract The Adoption of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud services, web-based software systems, and other wireless sensor devices in the healthcare infrastructure have led to phenomenal improvements and benefits in the healthcare sector. Digital healthcare has ensured early diagnosis of the diseases, greater accessibility, and mass outreach in terms of treatment. Despite this unprecedented success, the privacy and confidentiality of the healthcare data have become a major concern for all the stakeholders. Data breach reports reveal that the healthcare data industry is one of the key targets of cyber invaders. In fact the last few… More >

Chenjing Su¹, Xiaoyu Li^1,*, Mengru Li², Qinsheng Zhu², Hao Fu², Shan Yang³

Journal of Quantum Computing, Vol.3, No.2, pp. 79-87, 2021, DOI:10.32604/ jqc.2021.016651

Abstract As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this new model is successfully constructed… More >

Hyun Yoo¹, Ji-Won Baek², Kyungyong Chung^3,*

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 319-334, 2021, DOI:10.32604/iasc.2021.018115

Abstract With the convergence and development of the Internet of things (IoT) and artificial intelligence, closed-circuit television, wearable devices, and artificial neural networks have been combined and applied to crime prevention and follow-up measures against crimes. However, these IoT devices have various limitations based on the physical environment and face the fundamental problem of privacy violations. In this study, voice data are collected and emotions are classified based on an acoustic sensor that is free of privacy violations and is not sensitive to changes in external environments, to overcome these limitations. For the classification of emotions in the voice, the data… More >

Faseeha Matloob¹, Shabib Aftab^1,2, Munir Ahmad², Muhammad Adnan Khan^3,*, Areej Fatima⁴, Muhammad Iqbal², Wesam Mohsen Alruwaili⁵, Nouh Sabri Elmitwally^5,6

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 403-421, 2021, DOI:10.32604/iasc.2021.017562

Abstract Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and… More >

Dah-Jing Jwo^*, Jia-Chyi Wu, Kuan-Lin Ho

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 555-574, 2021, DOI:10.32604/cmc.2021.018320

Abstract This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal… More >

Omer Ali^1,2, Mohamad Khairi Ishak^1,*, Muhammad Kamran Liaquat Bhatti²

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 747-766, 2021, DOI:10.32604/cmc.2021.018098

Abstract The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which are worth investigating. Current supervised… More >

Galimkair Mutanov, Vladislav Karyukin^*, Zhanl Mamykova

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 913-930, 2021, DOI:10.32604/cmc.2021.017827

Abstract The volume of social media data on the Internet is constantly growing. This has created a substantial research field for data analysts. The diversity of articles, posts, and comments on news websites and social networks astonishes imagination. Nevertheless, most researchers focus on posts on Twitter that have a specific format and length restriction. The majority of them are written in the English language. As relatively few works have paid attention to sentiment analysis in the Russian and Kazakh languages, this article thoroughly analyzes news posts in the Kazakhstan media space. The amassed datasets include texts labeled according to three sentiment… More >

Fahad Alotaibi¹, Muhammad Attique^2,3, Yaser Daanial Khan^2,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1039-1055, 2021, DOI:10.32604/cmc.2021.017297

Abstract Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration. Though, the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides (“AIP”) are usually very costly and remain time-consuming. Therefore, before wet-lab investigations, it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process. Several anti-inflammatory prediction tools have been proposed in the recent past, yet, there is a space to induce enhancement in prediction performance in terms of precision and efficiency. An exceedingly accurate anti-inflammatory prediction model is proposed, named AntiFlamPred… More >

Haseeb Ahmad¹, Shahbaz Ahmad¹, Muhammad Asif¹, Mobashar Rehman^2,*, Abdullah Alharbi³, Zahid Ullah⁴

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1215-1232, 2021, DOI:10.32604/cmc.2021.016659

Abstract Cricket databases contain rich and useful information to examine and forecasting patterns and trends. This paper predicts Star Cricketers (SCs) from batting and bowling domains by employing supervised machine learning models. With this aim, each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their peers. Prediction is performed by applying Bayesian-rule, function and decision-tree-based models. Experimental evaluations are performed to validate the applicability of the proposed approach. In particular, the impact of the individual features on the prediction of SCs are analyzed. Moreover, the category and model-wise feature evaluations… More >

Amber Ayoub¹, Kainaat Mahboob¹, Abdul Rehman Javed², Muhammad Rizwan¹, Thippa Reddy Gadekallu², Mustufa Haider Abidi^3,*, Mohammed Alkahtani^4,5

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1253-1269, 2021, DOI:10.32604/cmc.2021.015655

Abstract Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and… More >