B. Chellapraba^1,*, D. Manohari², K. Periyakaruppan³, M. S. Kavitha⁴

Computer Systems Science and Engineering, Vol.45, No.1, pp. 483-495, 2023, DOI:10.32604/csse.2023.029854

Abstract Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand. Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities. CC was handled as a resource-rich solution to aforementioned challenge. As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing… More >

S. Nithya Roopa¹, P. Anandababu^2,*, Sibi Amaran³, Rajesh Verma⁴

Computer Systems Science and Engineering, Vol.45, No.1, pp. 497-512, 2023, DOI:10.32604/csse.2023.029133

Abstract Recently, energy harvesting wireless sensor networks (EHWSN) have increased significant attention among research communities. By harvesting energy from the neighboring environment, the sensors in EHWSN resolve the energy constraint problem and offers lengthened network lifetime. Clustering is one of the proficient ways for accomplishing even improved lifetime in EHWSN. The clustering process intends to appropriately elect the cluster heads (CHs) and construct clusters. Though several models are available in the literature, it is still needed to accomplish energy efficiency and security in EHWSN. In this view, this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy… More >

Ashit Kumar Dutta^1,*, Basit Qureshi², Yasser Albagory³, Majed Alsanea⁴, Manal Al Faraj¹, Abdul Rahaman Wahab Sait⁵

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2395-2409, 2023, DOI:10.32604/csse.2023.027502

Abstract Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is… More >

Ashit Kumar Dutta^1,*, Nazik M. A. Zakari², Yasser Albagory³, Abdul Rahaman Wahab Sait⁴

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2195-2207, 2023, DOI:10.32604/csse.2023.026461

Abstract Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify… 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

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 which can be resolved with… More >

R. Rajakumar^1,*, S. Sathiya Devi²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2101-2116, 2023, DOI:10.32604/iasc.2023.028889

Abstract Due to the advancements in information technologies, massive quantity of data is being produced by social media, smartphones, and sensor devices. The investigation of data stream by the use of machine learning (ML) approaches to address regression, prediction, and classification problems have received considerable interest. At the same time, the detection of anomalies or outliers and feature selection (FS) processes becomes important. This study develops an outlier detection with feature selection technique for streaming data classification, named ODFST-SDC technique. Initially, streaming data is pre-processed in two ways namely categorical encoding and null value removal. In addition, Local Correlation Integral (LOCI)… More >

T. S. Balaji^1,2, S. Srinivasan^3,*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2189-2203, 2023, DOI:10.32604/iasc.2023.028785

Abstract The vehicular ad hoc network (VANET) is an emerging network technology that has gained popularity because to its low cost, flexibility, and seamless services. Software defined networking (SDN) technology plays a critical role in network administration in the future generation of VANET with fifth generation (5G) networks. Regardless of the benefits of VANET, energy economy and traffic control are significant architectural challenges. Accurate and real-time traffic flow prediction (TFP) becomes critical for managing traffic effectively in the VANET. SDN controllers are a critical issue in VANET, which has garnered much interest in recent years. With this objective, this study develops… 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

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 source in WMSN, novel clustering… 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

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 epidemiology dataset. In the proposed… 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

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 introduces a new quantum black… More >