R. Bhaskaran^1,*, S. Saravanan¹, M. Kavitha², C. Jeyalakshmi³, Seifedine Kadry⁴, Hafiz Tayyab Rauf⁵, Reem Alkhammash⁶

Computer Systems Science and Engineering, Vol.44, No.1, pp. 235-247, 2023, DOI:10.32604/csse.2023.024399 - 01 June 2022

Abstract Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data More >

Mashael M. Asiri¹, Saud S. Alotaibi², Dalia H. Elkamchouchi³, Amira Sayed A. Aziz⁴, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵, Abu Sarwar Zamani⁵, Ishfaq Yaseen⁵

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5491-5507, 2022, DOI:10.32604/cmc.2022.031345 - 28 July 2022

Abstract Wireless Sensor Network (WSN) is a vital element in Internet of Things (IoT) as the former enables the collection of huge quantities of data in energy-constrained environment. WSN offers independent access to the target region and performs data collection in an effective manner. But energy constraints remain a challenging issue in WSN since it operates on in-built battery. The studies conducted earlier recommended that the energy spent on communication process must be considerably reduced to improve the efficiency of WSN. Cluster organization and optimal selection of the routes are considered as NP hard optimization problems… More >

R. Uma Mageswari¹, Sara A. Althubiti², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gyanendra Prasad Joshi⁵, Woong Cho^6,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4179-4192, 2022, DOI:10.32604/cmc.2022.030806 - 16 June 2022

Abstract Traditional Wireless Sensor Networks (WSNs) comprise of cost-effective sensors that can send physical parameters of the target environment to an intended user. With the evolution of technology, multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target domain. The existence of multimedia sensors, integrated with effective signal processing and multimedia source coding approaches, has led to the increased application of Wireless Multimedia Sensor Network (WMSN). This sort of network has the potential to capture, transmit, and receive multimedia content. Since energy is a major… More >

Saud S. Alotaibi¹, Amal Al-Rasheed², Sami Althahabi³, Manar Ahmed Hamza^4,*, Abdullah Mohamed⁵, Abu Sarwar Zamani⁴, Abdelwahed Motwakel⁴, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3305-3318, 2022, DOI:10.32604/cmc.2022.029385 - 16 June 2022

Abstract Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using… More >

Sara A. Althubiti¹, José Escorcia-Gutierrez^2,3,*, Margarita Gamarra⁴, Roosvel Soto-Diaz⁵, Romany F. Mansour⁶, Fayadh Alenezi⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2423-2439, 2022, DOI:10.32604/cmc.2022.028878 - 16 June 2022

Abstract Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper… More >

A. Chinnappa^*, C. Vijayakumaran

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4017-4031, 2022, DOI:10.32604/cmc.2022.027858 - 16 June 2022

Abstract Internet of Things (IoT) allows several low resources and controlled devices to interconnect, calculate processes and make decisions in the communication network. In the heterogeneous environment for IoT devices, several challenging issues such as energy, storage, efficiency, and security. The design of encryption techniques enables the transmission of the data in the IoT environment in a secured way. The proper selection of optimal keys helps to boost the encryption performance. With this motivation, the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission (SCQCKH-SDT) in IoT environment. The proposed SCQCKH-SDT… More >

Areej A. Malibari¹, Reem M. Alshehri², Fahd N. Al-Wesabi³, Noha Negm³, Mesfer Al Duhayyim⁴, Anwer Mustafa Hilal^5,*, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4277-4290, 2022, DOI:10.32604/cmc.2022.027030 - 16 June 2022

Abstract In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based… More >

M. Sangeetha^1,*, S. Malathi²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1587-1601, 2022, DOI:10.32604/iasc.2022.025192 - 25 May 2022

Abstract Software testing is an effective means of verifying software stability and trustworthiness. It is essential in the software development process and needs a huge quantity of resources such as labor, money, and time. Automated software testing can be used to save manual work, shorten testing times, and improve testing performance. Recently, Software Bug Prediction (SBP) models have been developed to improve the software quality assurance (SQA) process through the prediction of bug parts. Advanced deep learning (DL) models can be used to classify faults in software parts. Because hyperparameters have a significant impact on the… More >

Mahmoud Ragab^1,2,3,*, Sultanah M. Alshammari⁴, Amer H. Asseri^2,5, Waleed K. Almutiry⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 801-815, 2022, DOI:10.32604/cmc.2022.029140 - 18 May 2022

Abstract Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography (CT), or magnetic resonance imaging (MRI). An automated brain cancer classification using computer aided diagnosis (CAD) models can be designed to assist radiologists. With the recent advancement in computer vision (CV) and deep learning (DL) models, it is possible to automatically detect the tumor from images using a computer-aided design. This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features (HGSO-FHDF) technique for brain cancer classification. The proposed HGSO-FHDF technique aims… More >

K. Pradeep Mohan Kumar¹, S. Dhanasekaran², I. S. Hephzi Punithavathi³, P. Duraipandy⁴, Ashit Kumar Dutta⁵, Irina V. Pustokhina^6,*, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1541-1555, 2022, DOI:10.32604/cmc.2022.028338 - 18 May 2022

Abstract Financial crisis prediction (FCP) models are used for predicting or forecasting the financial status of a company or financial firm. It is considered a challenging issue in the financial sector. Statistical and machine learning (ML) models can be employed for the design of accurate FCP models. Though numerous works have existed in the literature, it is needed to design effective FCP models adaptable to different datasets. This study designs a new bird swarm algorithm (BSA) with fuzzy min-max neural network (FMM-NN) model, named BSA-FMMNN for FCP. The major intention of the BSA-FMMNN model is to… More >