Nadia Tabassum¹, Tahir Alyas², Muhammad Hamid^3,*, Muhammad Saleem⁴, Saadia Malik⁵, Syeda Binish Zahra²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1127-1140, 2022, DOI:10.32604/cmc.2022.019760

Abstract Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network, and users can use them at any time. After assessing and choosing cloud providers, however, customers confront the variety and difficulty of quality of service (QoS). To increase customer retention and engagement success rates, it is critical to research and develops an accurate and objective evaluation model. Cloud is the emerging environment for distributed services at various layers. Due to the benefits of this environment, globally cloud is being taken as a standard environment for individuals as well as for… More >

R. Manjula Devi¹, M. Premkumar², Pradeep Jangir³, B. Santhosh Kumar⁴, Dalal Alrowaili⁵, Kottakkaran Sooppy Nisar^6,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 557-579, 2022, DOI:10.32604/cmc.2022.019611

Abstract In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset. The proposed technique transforms the… More >

HongGeun Ji^1,2, Soyoung Oh¹, Jina Kim³, Seong Choi^1,2, Eunil Park^1,2,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 669-678, 2022, DOI:10.32604/cmc.2022.019521

Abstract In the field of natural language processing (NLP), the advancement of neural machine translation has paved the way for cross-lingual research. Yet, most studies in NLP have evaluated the proposed language models on well-refined datasets. We investigate whether a machine translation approach is suitable for multilingual analysis of unrefined datasets, particularly, chat messages in Twitch. In order to address it, we collected the dataset, which included 7,066,854 and 3,365,569 chat messages from English and Korean streams, respectively. We employed several machine learning classifiers and neural networks with two different types of embedding: word-sequence embedding and the final layer of a… More >

Shubham Mahajan^1,*, Akshay Raina², Mohamed Abouhawwash^3,4, Xiao-Zhi Gao⁵, Amit Kant Pandit¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1541-1556, 2022, DOI:10.32604/cmc.2022.019496

Abstract Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction and Deconvolutional Modules in combination… More >

Lijo Jacob Varghese¹, K. Dhayalini², Suma Sira Jacob³, Ihsan Ali^4,*, Abdelzahir Abdelmaboud⁵, Taiseer Abdalla Elfadil Eisa⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1053-1067, 2022, DOI:10.32604/cmc.2022.019435

Abstract Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper… More >

R. Sujitha^*, B. Paramasivan

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1557-1572, 2022, DOI:10.32604/cmc.2022.019323

Abstract With the modernization of machine learning techniques in healthcare, different innovations including support vector machine (SVM) have predominantly played a major role in classifying lung cancer, predicting coronavirus disease 2019, and other diseases. In particular, our algorithm focuses on integrated datasets as compared with other existing works. In this study, parallel-based SVM (P-SVM) and multiclass-based multiple submodels (MMSM-SVM) were used to analyze the optimal classification of lung diseases. This analysis aimed to find the optimal classification of lung diseases with id and stages, such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses, respectively. For… More >

Mohammad Sh. Daoud¹, Areej Fatima², Waseem Ahmad Khan³, Muhammad Adnan Khan^4,5,*, Sagheer Abbas³, Baha Ihnaini⁶, Munir Ahmad³, Muhammad Sheraz Javeid⁷, Shabib Aftab³

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 109-121, 2022, DOI:10.32604/cmc.2022.019295

Abstract The numbers of multimedia applications and their users increase with each passing day. Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems. In this article, a fuzzy logic empowered adaptive backpropagation neural network (FLeABPNN) algorithm is proposed for joint channel and multi-user detection (CMD). FLeABPNN has two stages. The first stage estimates the channel parameters, and the second performs multi-user detection. The proposed approach capitalizes on a neuro-fuzzy hybrid system that combines the competencies of both fuzzy logic and neural networks. This study analyzes the… More >

Shynar Mussiraliyeva¹, Batyrkhan Omarov^1,*, Paul Yoo^1,2, Milana Bolatbek¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 915-934, 2022, DOI:10.32604/cmc.2022.019189

Abstract In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical Features, TF-IDF, POS, LIWC, and… More >

Fareeha Sameen¹, Abdul Momin Kazi², Majida Kazmi^1,*, Munir A Abbasi³, Saad Ahmed Qazi^1,4, Lampros K Stergioulas^3,5

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 375-395, 2022, DOI:10.32604/cmc.2022.019167

Abstract Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to… More >

Mavra Mehmood¹, Talha Javed², Jamel Nebhen³, Sidra Abbas^2,*, Rabia Abid¹, Giridhar Reddy Bojja⁴, Muhammad Rizwan¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 91-107, 2022, DOI:10.32604/cmc.2022.019127

Abstract Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination… More >