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14th IEEE International Conference on Control System, computing and Engineering, ICCSCE 2024

作者： Qassabi, Saeed Saud Sulaiman Al Wilfred Blessing, N.R. Tahir, Nooritawati Md University of Technology and Applied Sciences-Muscat College of Computing and Information Sciences Muscat Oman University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Malaysia Universiti Teknologi MARA College of Engineering Malaysia Applied College Riyadh Saudi Arabia

ISBN: (纸本)9798350364507

The traditional methods of searching for flats are often inefficient and lack necessary features, making it difficult for both landlords and tenants to find what they are looking for. The 'Sakanat' mobile application is designed to bridge this gap by offering a user-friendly platform for property listing, searching, and management. Landlords can easily upload details, photos, and set rental terms, while tenants can efficiently find suitable flats based on various criteria using advanced search filters. Rent payments can be made directly through the app using a secure payment gateway, and tenants can also access a variety of additional services such as maintenance companies and flat transfer services. Sakanat leverages Flutter for cross-platform app development and Firebase for backend services and data storage. Development will prioritize user-friendliness, performance, and security. By facilitating connections between landlords and tenants, simplifying rent collection, and offering valuable additional services, Sakanat has the potential to revolutionize the flat rental experience. © 2024 IEEE.

关键词： Gateways (computer networks)

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Multi-lingual news stack based ensemble classification for deep news feed analysis

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Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks 2

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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Chemical Reaction and Viscous Dissipative Effects on Buongiorno’s Nanofluid Model Past an Inclined Plane: A Numerical Investigation

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International Journal of applied and Computational Mathematics 2024年第2期10卷 1-20页

作者： Reddy, Ramesh Gaffar, S. Abdul Department of Mathematics Madanapalle Institute of Technology & amp Preparatory Studies Center Mathematics and Computing Skills Unit University of Technology and Applied Sciences Salalah Oman

The use of nanofluid in thermal applications dramatically enhanced the pattern of heat and mass transmission, which is essential in numerous engineering and industrial areas. Numerous innovative applications in solar energy and thermal engineering can be attributed to the consideration of nanofluid. Additionally, motile microorganisms, which have applications in enzymes, bioengineering, biomedicine, biofuels and petroleum sciences, perfectly improve the stability of nanofluid. The study of nanofluid has several dynamic applications in renewable energy and thermodynamic engineering problems. The aim of this paper is to discuss the chemical viscous dissipative transport of Buongiorno’s nanofluid across an inclined plane, considering Brownian movement and thermophoresis effects. The governing equations and the associated boundary conditions are normalized using the non-similarity transformation approach. The key variables and corresponding non-similarity solutions are provided to summarize the transpiration parameters. The Keller’s Box method is used to obtain the mathematical solutions. The numerical findings are given for various thermos-physical parameter values both physically and quantitatively. The graphical effects of various thermos-physical factors on momentum, energy, nanoparticle volume fraction concentration, shear stress rate, heat and mass transfer rates are examined and well discussed. The results show strong associations when compared to previously published literature. A slight increase in velocity with a rise in Kr values. Whereas, a slight decrease in temperature with an increase in Kr values and a strong decrease in nanoparticle volume fraction concentration with an increase in Kr values. A significant increase in velocity and temperature is seen with an increase in Ec and Nb values while nanoparticle volume fraction concentration is seen to decrease slightly. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Chemical reaction Heat transfer rate Keller’s box technique Mass transfer rate Nanofluid Skin friction Viscous dissipation

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Unveiling the barriers to digital transformation in higher education institutions: a systematic literature review

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Discover Education 2025年第1期4卷 1-41页

作者： Singun, Amando Jr. College of Computing and Information Sciences University of Technology and Applied Sciences PO Box: 74 Al Khuwair Muscat PC: 133 Oman

This study investigates the challenges hindering the implementation of Digital Transformation (DT) in Higher Education Institutions (HEIs) by thoroughly reviewing the literature. It identifies multiple dimensions and subdimensions of these barriers to offer valuable insights to help HEIs navigate their transformation processes successfully. By doing so, they can effectively address the changing requirements of students, faculty, administration, and other stakeholders in an increasingly digital environment. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, synthesizing data from twenty (20) relevant peer-reviewed articles published between 2019 and 2024. The author employed NVivo and Zotero as methodological tools for coding, thematic analysis, and text mining to derive insights from the selected articles, thereby facilitating the development of a Concept-Centric Matrix of the Barriers to Digital Transformation (DT) in Higher Education Institutions (HEIs). The result of this study is summarized in a Concept-centric Matrix on Barriers to DT in HEIs consisting of nine (9) dimensions such as (i) Digital Vision, Strategy, and Policies;(ii) Digital Leadership and Management;(iii) Digital Organization;(iv) Digital Resources;(v) Digital Competence;(vi) Digital Stakeholder Management;(vii) Digital Culture;(viii) Digital Academic;and (ix) Digital Ethics. Each dimension has subdimensions of barriers (n = 80). Despite various sectors anticipating significant DT, many HEIs feel inadequately prepared to adapt because of the barriers to its implementation. The ongoing DT within HEIs reveals a complex landscape marked by various intertwined barriers that necessitate a strategic reevaluation and a commitment to fostering an inclusive, responsive educational environment. © The Author(s) 2025.

关键词： Barriers Concept-centric matrix Digital transformation Education HEI Higher education Higher education institutions NVivo PRISMA Systematic literature review University

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On the System Performance of Mobile Edge computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 668-685页

作者： Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In the Faculty of Electrical-Electronic Engineering Duy Tan University the Institute of Research and Development Duy Tan University the Faculty of Information Technology Duy Tan University IEEE the Applied Network Technology(ANT)Laboratory Department of Computer ScienceCollege of ComputingKhon Kaen University

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m *** proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)*** derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered *** results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power ***,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system *** approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar *** use Monte Carlo simulations to confirm the validity of our analysis.

关键词： Mobile edge computing(MEC) Nakagami-m fading offloading selection combining(SC)/maximal ratio combining(MRC) successful computation probability(SCP) uplink nonortho-gonal multiple access(NOMA) wireless sensor networks(WSNs)

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Predicting Lung Cancer Risk Using Artificial Intelligence 2

Predicting Lung Cancer Risk Using Artificial Intelligence

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Shah, Syed Mehr Ali Jamil, Danish Khan, Muhammad Numan Ali Al-Jarwani, Fatma Mahfoodh Mohammed College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences shinas Oman

ISBN: (纸本)9798331542573

This research proposes a method to accurately forecast the risk of lung cancer in its initial stages by the combination of medical diagnosis and artificial intelligence. Two distinct models are employed: a symptoms-based model utilizing linear regression, and VGG19, a component of CNNs is employed for the analysis of chest x-ray images. The objective of this dual-model approach is to improve comprehensiveness and precision in the early prediction of lung cancer risk. The JSRT and Chest Xray-14 datasets were utilized to train and validate the convolutional neural network (CNN). The Regression model was trained using the Survey Lung cancer Dataset. This initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI. Initial positive results show that this combined approach has a lot of potential for early detection © 2024 IEEE.

关键词： Lung cancer

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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

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Internet of Medical Things (IoMT) Fog-Based Smart Health Monitoring 2

Internet of Medical Things (IoMT) Fog-Based Smart Health Mon...

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2nd IEEE International Conference on Advances in computing, Communication and applied Informatics, ACCAI 2023

作者： Stephen, Vimal Kumar Udinookkaran, Prajith De Vera, Rudiardo Percival Al-Harthy, Faiza Rashid Ammar University of Technology and Applied Sciences Ibra College of Computing and Information Sciences Oman

ISBN: (纸本)9798350315905

Real-time monitoring is one of the Emerging duties in IoMT, and numerous systems have been developed to ensure the patient health monitor with efficient sensors capable of sensing, processing, and wireless communication may be used to collect data for environmental and smart health monitoring. These sensors are connected via wireless sensor networks. They transfer data to the cloud via IoT protocols and technology for storage and processing. This allows for the prediction of possible equipment breakdowns using past data. At times, the volume of data transmitted to the cloud or the time it takes to transfer data to the cloud and back to the sensors/actuators might be excessive. Moving some of the processing closer to the sensors can help minimize the amount of network and cloud resources consumed in these instances. In order to give cloud access, fog installations need to determine the architecture for joining sensors and gateways. Sensors often create data streams that may be pre-processed, aggregated, or filtered before they reach the cloud. Gateways can also be utilized to undertake data analytics. As a result, fog organization is critical for balancing computational load and network resource utilization on public clouds in order to reduce latency and save money. Service irregularity detection is a type of predictive maintenance that may be carried out even if no data from prior equipment failures is available. When available, machine-learning algorithms based on binary classification are used to predict breakdowns in the near future, allowing for repairs or replacements to be scheduled. The prediction models are trained and assessed using historical data, which includes information on prior equipment failures. Because historical data might be massive, real-time cloud storage is a possibility, leading in cloud-based predictive maintenance. This paper proposes a fog-based smart-health monitoring system with a single feed-forward neural network learning method to re

关键词： Internet of things

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Analysis of Software Cost Models Based on Exponential Software Reliability Growth Models 30th

Analysis of Software Cost Models Based on Exponential Softwa...

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Advances in Systems Engineering - Proceedings of the 30th International Conference on Systems Engineering, ICSEng 2023

作者： Williams, D. R. Prince Vimbi, Viswan Shanmugam, Thirumurugan Al Amayreh, Ahmed College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman

ISBN: (纸本)9783031405785

A prominent characteristic of the reliability of software is the estimation of its quality. Measuring and managing software quality during its life cycle is essential to producing and maintaining reliable software systems with good quality. Software Cost Models are used to predict the cost of a software system at the beginning of software testing. In this paper, we have discussed and analyzed three cost models: The software Reliability Cost Model, Software Reliability Cost Model with penalty cost, and Software Reliability Cost Model with risk cost. Finally, numerical illustration and data provided in the literature have predicted the best software cost model based on Software Reliability, total software cost, and the optimal time to deliver the software to the customer. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software reliability

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