Amel Austine^*, R. Suji Pramila

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2263-2276, 2023, DOI:10.32604/csse.2023.032323 - 09 February 2023

Abstract In a cellular network, direct Device-to-Device (D2D) communication enhances Quality of Service (QoS) in terms of coverage, throughput and amount of power consumed. Since the D2D pairs involve cellular resources for communication, the chances of interference are high. D2D communications demand minimum interference along with maximum throughput and sum rate which can be achieved by employing optimal resources and efficient power allocation procedures. In this research, a hybrid optimization model called Genetic Algorithm-Adaptive Bat Optimization (GA-ABO) algorithm is proposed for efficient resource allocation in a cellular network with D2D communication. Simulation analysis demonstrates that the More >

S. Kavi Priya^*, N. Saranya

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2571-2587, 2023, DOI:10.32604/csse.2023.031761 - 09 February 2023

Abstract Around the globe, chronic diseases pose a serious hazard to healthcare communities. The majority of the deaths are due to chronic diseases, and it causes burdens across the world. Through analyzing healthcare data and extracting patterns healthcare administrators, victims, and healthcare communities will get an advantage if the diseases are early predicted. The majority of the existing works focused on increasing the accuracy of the techniques but didn’t concentrate on other performance measures. Thus, the proposed work improves the early detection of chronic disease and safeguards the lives of the patients by increasing the specificity… More >

R. Sujatha^1,*, T. Abirami²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031552 - 09 February 2023

Abstract The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A… More >

Aisha M. Mashraqi, Hanan T. Halawani^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2555-2570, 2023, DOI:10.32604/csse.2023.031246 - 09 February 2023

Abstract Sentiment Analysis (SA) is one of the Machine Learning (ML) techniques that has been investigated by several researchers in recent years, especially due to the evolution of novel data collection methods focused on social media. In literature, it has been reported that SA data is created for English language in excess of any other language. It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language. An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text.… More >

V. P. Nithya^*, N. Mohanasundaram, R. Santhosh

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2601-2618, 2023, DOI:10.32604/csse.2023.030503 - 09 February 2023

Abstract The prediction of mild cognitive impairment or Alzheimer’s disease (AD) has gained the attention of huge researchers as the disease occurrence is increasing, and there is a need for earlier prediction. Regrettably, due to the high-dimensionality nature of neural data and the least available samples, modelling an efficient computer diagnostic system is highly solicited. Learning approaches, specifically deep learning approaches, are essential in disease prediction. Deep Learning (DL) approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging. A novel 3D-Convolutional Neural Network (3D-CNN) architecture is proposed to predict AD with… More >

Manar Ahmed Hamza^1,*, Shaha Al-Otaibi², Sami Althahabi³, Jaber S. Alzahrani⁴, Abdullah Mohamed⁵, Abdelwahed Motwakel¹, Abu Sarwar Zamani¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1371-1383, 2023, DOI:10.32604/csse.2023.030232 - 09 February 2023

Abstract Internet of things (IoT) and cloud computing (CC) becomes widespread in different application domains such as business, e-commerce, healthcare, etc. The recent developments of IoT technology have led to an increase in large amounts of data from various sources. In IoT enabled cloud environment, load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization. The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics. In this view, this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling (C3SOA-LS) technique for… More >

P. M. Siva Raja^1,*, S. Brinthakumari², K. Ramanan³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2589-2599, 2023, DOI:10.32604/csse.2023.028542 - 09 February 2023

Abstract Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation. When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain, magnetic resonance imaging (MRI) is a great tool. It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture. Radiologists have a difficult time sorting and classifying tumors from multiple images. Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation (NTKFIBC-IS).… More >

Chuiju You¹, Guanjun Lin^1,*, Jinming Qiu¹, Ning Cao¹, Yundong Sun², Russell Higgs³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1277-1292, 2023, DOI:10.32604/csse.2023.026150 - 09 February 2023

Abstract A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal More >

Romana Basit¹, Xinyang Li¹, Zheqing Huang¹, Qiang Zhou^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1509-1526, 2023, DOI:10.32604/cmes.2023.025308 - 06 February 2023

Abstract Direct Numerical Simulations have been carried out to study the forced convection heat transfer of flow through fixed prolate particles for a variety of aspect ratios ar = {5/4, 5/3, 5/1} with Reynolds number (Re) up to 100. Three variations of the solid volume fraction c = {0.1, 0.2, 0.3} with four Hermans orientation factors S = {−0.5, 0, 0.5, 1} are studied. It has been found that changes in S cause prominent variations in the Nusselt number. In general, Nusselt number increases with the decrease of S. For all three aspect ratios, the Nusselt number remains More > Graphic Abstract

Shahzaib Ashraf¹, Bushra Batool², Muhammad Naeem^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1785-1811, 2023, DOI:10.32604/cmes.2023.024851 - 06 February 2023

Abstract This research proposes multicriteria decision-making (MCDM)-based real-time Mesenchymal stem cells (MSC) transfusion framework. The testing phase of the methodology denotes the ability to stick to plastic surfaces, the upregulation and downregulation of certain surface protein markers, and lastly, the ability to differentiate into various cell types. First, two scenarios of an enhanced dataset based on a medical perspective were created in the development phase to produce varying levels of emergency. Second, for real-time monitoring of COVID-19 patients with different emergency levels (i.e., mild, moderate, severe, and critical), an automated triage algorithm based on a formal… More >