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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Bolisetty, Prashanth Kumar Bora, Satyanarayana College of Computing and Information Sciences University of Technology And Applied Sciences Department of Information Technology Shinas Oman University of Technology and Applied Applied Sciences Preparatory Studies Center Musandam Oman

ISBN: (纸本)9798350352931

Performance prediction for students is relatively a new topic of research that aims to apply quantitative and qualitative approaches to evaluate student performance. This paper reviews existing approaches in predicting student performance across various learning environments. It explores fundamental statistical models, Machine Learning (ML) algorithms, and Deep Learning (DL) models, highlighting their pertinent characteristics and suitability for analyzing academic, behavioral and demographic data. The paper focuses on analyzing the existing methodologies for student performance prediction. The datasets used consist of student grades, attendance records, socioeconomic status, and interaction rates, along with an assessment of their efficiency in feature selection and data preprocessing. Lastly, the explainability and interpretability of the proposed predictive models are explored to enable reliability for educational stakeholders on these predictions for timely interventions. This survey concludes with a discussion of future scopes, focusing on enhancing prediction accuracy, ensuring fairness in models, and integrating multiple data modalities into performance analysis. © 2024 IEEE.

关键词： Prediction models

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SMSecure: Leveraging Machine Learning for Smishing Detection 1st

SMSecure: Leveraging Machine Learning for Smishing Detection

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1st International Conference on Data Engineering and Machine Intelligence, ICDEMI 2023

作者： Samad, Saleem Raja Abdul Balasubramaniyan, Sundaravadivazhagan Ganesan, Pradeepa Al-Kaabi, Amna Salim Ammaippan, Hariraman Sam, Jeyakumar Manickam Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Al-Musannah Al-Musannah Oman Electrical Engineering Department College of Engineering and Technology University of Technology and Applied Sciences-Shinas Shinas Oman

ISBN: (纸本)9789819776153

The SMS facilitates the transmission of concise text messages between mobile phone users, serving a range of functions in both personal and business domains such as appointment confirmation, authentication, alerts, notifications, and banking updates. It plays a vital role in daily communication due to its accessibility, reliability, and compatibility. Its accessibility, reliability, and compatibility make it essential for daily communication. However, SMS unintentionally generates an environment where smishing can occur. This is because SMS is extensively available and reliable. Smishing attackers take advantage of this trust to deceive victims into divulging sensitive information or performing malicious actions. Early detection saves users from being victimized. Researchers introduced different methods for accurately detecting smishing attacks. Machine Learning models and Techniques for Language Processing are a promising approach for combating the escalating menace of SMS phishing attacks. By analyzing large datasets of SMS messages, machine learning models with natural language processing methods can differentiate between legitimate and fraudulent messages. To detect smishing attacks, this paper presents a method (SMSecure) that leverages machine learning models and language processing techniques. The results show that random forest and extreme gradient boosting attain higher accuracy levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Phishing

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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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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

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Computers, Materials & Continua 2023年第11期77卷 1739-1757页

作者： Tariq Qayyum Zouheir Trabelsi Asadullah Tariq Muhammad Ali Kadhim Hayawi Irfan Ud Din College of Information Technology United Arab Emirates UniversityAl Ain17551UAE Department of Computing School of EECSNational University of Sciences and TechnologyIslamabad44000Pakistan College of Technological Innovation Zayed UniversityAbu Dhabi144534UAE Department of Computer Science The Superior UniversityLahore54000Pakistan

Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)*** FL effectively tackles privacy concerns,it also imposes significant resource *** traditional FL,trained models are transmitted to a central server for global aggregation,typically in the *** approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same *** need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV *** include diverse and distributed data sources,varying data quality,and limited communication *** employing dynamic client selection,we can prioritize relevant and high-quality data sources,enhancing model *** address this issue,we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed *** global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data *** approach optimizes both model performance and resource allocation,making FL in IoV more effective and *** selection of the global aggregation node is based on workload and communication speed ***,our framework overcomes the constraints associated with network,computational,and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined *** approach surpasses Federated Averaging(FedAvg)and Hierarchical FL(HFL)regarding energy consumption,delay,and accuracy,yielding superior results.

关键词： IoT Federated Learning sensors IoV OMNeT++ edge computing

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Artificial Intelligence and Machine Learning for Accessibility: Smart Reader as an Assistive Tool for the Visually Impaired 2

Artificial Intelligence and Machine Learning for Accessibili...

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

作者： Palarimath, Suresh Radhakrishnan, Venkateswaran Veerasamy, Devarajan Valsalan, Prajoona Viswan, Vimbi Thumu, Muni Balaji College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sohar Oman

ISBN: (纸本)9798331505745

The amalgamation of artificial intelligence (AI) and machine learning (ML) has greatly improved accessibility for visually impaired individuals, fostering increased autonomy through pioneering assistive technology. This study introduces the Smart Reader, a device that integrates optical character recognition (OCR) and text-to-speech (TTS) technologies to enhance accessibility to printed documents. The Smart Reader utilizes a camera to take text images, translates them into digital representation through OCR, and converts the text into audible speech via TTS. The system has an intuitive user interface, an automatic page-turning mechanism, and a dictionary lookup function, guaranteeing a seamless reading experience. The findings indicate that the Smart Reader significantly improves access to written information, hence enhancing the quality of life for visually impaired individuals, especially in educational environments such as schools and libraries. The device is engineered for user-friendliness, enhancing accessibility and autonomy. The Smart Reader utilizes AI and ML, marking a significant progression in assistive technology, promoting an inclusive society where visually impaired individuals can engage with printed material with assurance. In addition to functionality, it seeks to empower people by connecting capability with opportunity, guaranteeing equitable access to information for everyone. © 2025 IEEE.

关键词： Contrastive Learning

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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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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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Implementation and Use of Clinical Decision Support System in Malawi National ART EMR System

Implementation and Use of Clinical Decision Support System i...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Bisani, Pachawo Nyirenda, Goodall University of Malawi Department of Computing Zomba Malawi Malawi University of Business and Applied Sciences Department of Computing and Information Technology Blantyre Malawi

ISBN: (纸本)9798350327816

The introduction of Malawi national antiretroviral therapy (NART) electronic medical record (EMR) system has improved the management and reporting of ART patient data. Designed to aid in decision-making by providers for individual patients in their care, the system has been integrated with a Clinical Decision Support System (CDSS). NART EMR system was introduced in 2006, and by 2021, it had been extended to over 206 healthcare facilities in the country. Despite several evaluations conducted on the NART EMR, there has been limited attention given to evaluating the implementation and usage of the CDSS within the NART EMR system. Hence, the study sought to establish how CDSS in the NART EMR system has been implemented and used by healthcare providers in Malawi to improve decision making on an individual patient on antiretroviral therapy (ART) at any point in time. The study employed a qualitative methodology, gathering data through observations, interviews, and an in-depth analysis of the system. The study establishes that smooth integration of CDSS in EMR system into regular and normal clinical workflow contributes to the success of CDSS use. Despite the successes of CDSS in EMR system, issues of system quality and information quality must be addressed during design and implementation of the CDSS to enhance efficiency and effectiveness of the system. The study suggests that those responsible for implementation should prioritize improving the underlying logic of the clinical decision support intervention in order to address existing concerns and improve the accuracy of the provided information. Additionally, the study recommends active consultation with the system's real users during the implementation process. © 2023 IEEE.

关键词： Antiretroviral Therapy clinical decision support system electronic medical record system information quality

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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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