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1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Al-Anqoudi, Maha Said Ada, Mireilla Bikanga McQuistin, Stephen University of Technology and Applied Science College of Computing and Information Sciences Nizwa Oman University of Glasgow School of Computer Science Glasgow United Kingdom University of St Andrews School of Computer Science St Andrews United Kingdom

ISBN: (纸本)9798350348637

We have witnessed the widespread adoption of online teaching and learning platforms in recent years. Teachers employ a variety of learning activities and techniques to follow their students' learning progress, including summative assessments. However, concerns have been raised about the trustworthiness of summative assessments, such as quizzes and tests. At the same time, online teachers have found other learning activities that are not typically included in predictive machine learning models of online platforms are effective in helping them identify student learning status. This study uses two feature selection techniques, filter and embedded, along with a teacher self-reported questionnaire to evaluate which learning activities influence students' performance in summative assessments like quizzes. Our findings reveal that features from non-summative learning activities can effectively predict students' performance in an online environment. Teachers agree on the importance of the identified learning activities' features in helping them determine their students' learning status. The study provides practical recommendations for educators, course designers, and policy-makers to optimize online assessment strategies. © 2024 IEEE.

关键词： feature selection learning activities online education prediction students' performance trustworthiness assessment

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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Priority Based Fair Scheduling: Enhancing Efficiency in Cloud Job Distribution 8

Priority Based Fair Scheduling: Enhancing Efficiency in Clou...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Murad, Saydul Akbar Azmi, Zafril Rizal M Brishti, Faria Jerin Saib, Md Bairagi, Anupam Kumar College of Computing Applied Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pahang Malaysia Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh South China University of Technology Guangzhou China Khulna University Khulna Bangladesh

ISBN: (纸本)9798350310931

In recent years, there has been a growing interest in cloud computing as a means to enhance user access to shared computing resources, including software and hardware, through the internet. However, the efficient utilization of these cloud resources has been a challenge, often resulting in wastage or degraded service performance due to inadequate scheduling. To overcome this challenge, numerous researchers have focused on improving existing Priority Rule (PR) cloud schedulers by developing dynamic scheduling algorithms, but they have fallen short of meeting user satisfaction. In this study, we introduce a new PR scheduler called Priority Based Fair Scheduling (PBFS), which takes into account key parameters such as CPU Time, Job Arrival Time, and Job Length. We evaluate the performance of PBFS by comparing it with five existing algorithms, and the results demonstrate that PBFS surpasses the performance of the other algorithms. The experiment was conducted using the CloudSim simulator, utilizing a dataset of 300 and 400 jobs. In order to assess the performance, three key metrics were employed: flow time, makespan time, and total tardiness. These metrics were chosen to evaluate and analyze the effectiveness of the proposed scheduling algorithm. © 2023 IEEE.

关键词： Cloud computing

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TNEST: Training Sparse Neural Network for FPGA Based Edge Application 2nd

TNEST: Training Sparse Neural Network for FPGA Based Edge A...

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2nd International Conference on Advances in computing Research, ACR 2024

作者： Das, Rammi Karn, Rupesh Raj Heikkonen, Jukka Kanth, Rajeev Nepal College of Information Technology Pokhara University Kathmandu Nepal Center for Cyber Security New York University Abu Dhabi United Arab Emirates Department of Computing University of Turku Turku Finland Department of Information Technology Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031569494

Machine learning (ML) hardware inference has developed ultra-low-power edge devices that accelerate inferential applications performance. An FPGA (Field Programmable Gate Array) is a popular option for such systems. The FPGA has power budget, memory, compute resource, area, etc. constraints but possesses several key advantages, including bandwidth saving, speed, real-time inference, offline activity, etc. Neural networks have been extensively used in Edge systems due to their significant popularity in AI. Requirements of complex neural networks have only been recently realized for edge applications, so the research community has yet to develop a standard model for such applications. The sparse neural architecture reduces the active neurons and connections, making the entire system computationally more efficient and utilizing less memory. Such essential facts are more and more evident in edge-based IoT applications. In this work, we have customized neural network training algorithms to fit precisely for the Edge systems. Rather than a traditional top-down approach where MLs are fully trained and then pruned to fit within edge device resources, we adopted a generative approach where MLs are prepared with the least number of parameters, and further components are added as the need arises to improve inference accuracy. Our generative model shows significant savings in FPGA resource consumption compared to the top-down approach for the same precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： High level synthesis

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A Study on Process Safety Management (PSM) Effectiveness Based on Human, Technological and Organizational Factors in an Oleochemical Plant 3

A Study on Process Safety Management (PSM) Effectiveness Bas...

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3rd Innovative Manufacturing, Mechatronics and Materials Forum 2022, iM3F 2022

作者： Ezuan, A.J.M. Azizan, R. Nurulain, A. Faculty of Industrial Sciences and Technology College of Computing and Applied Sciences Universiti Malaysia Pahang Gambang Pahang Kuantan26300 Malaysia

ISBN: (数字)9781839537813

ISBN: (纸本)9781839538063

Oleochemical industry produces chemical products such as fatty alcohols, methyl esters and glycerin. However, the industrial revolution increases the industry risk level to people and the environment. Their activities could result in risk of exposures to hazardous chemicals. The decay of process safety performance in most chemicals industries noticed in recent years are due to human, technological and organizational factors. Thus, this study intends to emphasize on the Process Safety Management (PSM) effectiveness based on these three factors. A total of 118 questionnaires were distributed, and 40 valid questionnaires were returned with response rate of 35%. Methods used to conduct this study were questionnaire, semi structured interview and field observation. Fourteen elements of process safety management (PSM) were identified. Strength, Weaknesses, Opportunities and Threat (SWOT) Analysis of PSM implementation based on human, technological and organizational factors in an oleochemical plant were identified. The level of effectiveness of PSM implementation survey was conducted using a questionnaire adapted from the United State Department of Energy Process Safety Management for Highly Hazardous Chemicals. The elements of PSM were analyzed by ranking all the mean score obtained for each element. The result revealed that the level of effectiveness for element of mechanical integrity was the highest (13.48) while the level of effectiveness for element of trade secret was the lowest (1.95). © 2022 IET Conference Proceedings. All rights reserved.

关键词： Alcohols

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A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

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Journal of Information Security 2023年第4期14卷 422-436页

作者： Peter Awonnatemi Agbedemnab Mohammed Akolgo Moses Apambila Agebure Department of Information Systems and Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Computer Science School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.

关键词： Discrete Wavelet Transform (DWT) Digital Watermarking Encryption Genetic Algorithm (GA) Residue Number System (RNS) GARN

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Forecasting of Groundwater Quality Utilizing an Efficient K-Means with Principal Component Analysis Approach 2

Forecasting of Groundwater Quality Utilizing an Efficient K-...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Adnan, Myasar Mundher Manjula, B.M. Mishra, Antim Dev Ganti, Swathi Bala Dhandayuthapani, V. The Islamic University of Babylon Babylon Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Engineering and Technology Sushant College Gurgaon India Department of Nano Technology Andhra University Visakhapatnam India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman

ISBN: (纸本)9798331504960

Groundwater stands as a pivotal freshwater resource in numerous regions worldwide, but the burgeoning population and diverse economic activities have adversely impacted its quality. However, the K-means algorithm is sensitive to the initial placement of centroids, where outliers can significantly influence the position of centroids resulting in suboptimal clusters. To address this, it is recommended to remove or clip outliers before applying K-means scales well to large datasets making it suitable for handling substantial amounts of data. In this proposed method, attributes are first calculated using Principal Component Analysis (PCA) determines the weight of each attribute based on eigenvalues. The PCA, by determining attribute weights and Rapid Centroid Estimation (RCE), by establishing the initial cluster centre, contribute to the overall effectiveness of the method. The classification step utilizes a Linear Support Vector Machine (SVM), suitable for linear hyper line data when the dataset can be classified into two classes using a single straight line. The results obtained showcase the superiority of the proposed method, achieving a Root Mean Squared Error (RMSE) of 1.23, R-squared (R2) of 0.90, Mean Absolute Deviation (MAD) of 0.28 and E of 0.97 on the Indian groundwater dataset. These outcomes affirm better detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Mean square error

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A Systematic Review of 5G Applications and Impacts in Automotive Manufacturing and Mobility

A Systematic Review of 5G Applications and Impacts in Automo...

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computing and Data Analytics (ICCDA), International Conference on

作者： Moein Hossein Torkzadeh Vinu Sherimon English Language Center University of Technology and Applied Sciences Muscat Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Sultanate of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The integration of 5G and Internet of Vehicles (IOV) technologies will quicken intelligent transportation system developments, improving urban mobility and safety. This systematic literature review paper summarizes the current research on 5G's uses and impacts in the automotive manufacturing and mobility industries, emphasizing its potential to change transportation scenarios. This systematic literature review examines how 5G technology may impact automobile manufacturing and transportation, with a focus on IOV applications. A comprehensive search across IEEE Xplore, Google Scholar, and ProQuest, yielded 31 relevant publications. Five papers were chosen after applying an exclusion and inclusion criteria to demonstrate how 5G will improve vehicle communication, autonomous driving, and traffic management. The review emphasizes the relevance of Vehicle-to-Everything (V2X) communication technology in enabling real-time information transmission among intelligent connected vehicles, a key to future autonomous driving environmental sensing.

关键词： 5G mobile communication Transportation Throughput Manufacturing Autonomous vehicles Vehicle-to-everything Internet of Vehicles Systematic literature review Industrial Internet of Things Automotive engineering

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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Computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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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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