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1st IEEE International Conference on Communication engineering and Emerging Technologies, ICoCET 2024

作者： Jayachandran, Murugakummar Abdul Rani, Khairul Najmy Wahab, Alawiyah Abd Universiti Malaysia Perlis Faculty of Electronic Engineering & Technology Perlis Malaysia Universiti Malaysia Perlis Centre of Excellence Advanced Communication Engineering Faculty of Electronic Engineering & Technology Perlis Malaysia School of Computing College of Arts & Sciences Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9798331504144

Underground high-voltage cable usage is expanding due to their resistance to pollutants, severe weather, heavy rain, and storms. However, these cables are prone to damage due to improper installation, poor jointing, and potential harm from third-party activities such as curb edging or trenching. Additionally, environmental factors, abrasion, and animal threats further complicate the process of locating and repairing faults. This project introduces a diagnosis system utilizing the Internet of Things (IoT) to identify and locate faults in underground cables. By enabling faster fault detection, the system aims to reduce the time, money, and effort associated with repairs. The IoT-based solution utilizes a set of voltage dividers mounted on the cable to detect faults. When a fault occurs due to a shortening between two cables, a voltage is generated, which is then detected by an ESP32 microcontroller. This microcontroller notifies the user by providing the fault location in kilometers. The gathered data is displayed on a liquid crystal display (LCD) and transmitted online via a Wi-Fi module to the Blynk cloud database for storage and further analysis. © 2024 IEEE.

关键词： Liquid crystal displays

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AI Technology in IT Service Desk (ITSD) 7

AI Technology in IT Service Desk (ITSD)

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Karahamo, Mohamed Alden, Feras Zen Nour Hindia, M.H.D. Daud, Ali Yusny Deprtment of IT Schlumberger Oilfield UK Limited London United Kingdom College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia School of Computing College of Arts and Sciences Universiti Utara Malaysia Sintok Kedah Malaysia

ISBN: (纸本)9798331519513

This research assesses the impact of applying AI technologies to address IT Service Desk (ITSD) problems. The necessity of this study arises with the increasing dependency on technology in recent years. IT Service Desk plays a crucial role in providing remote IT support to the businesses’ end-users regardless of their location. The high demand for the IT Service Desk resulted in the need for additional IT resources to manage the increasing number of IT requests, which could increase the IT support activities. In many cases, increasing the number of IT agents is not adequate, which leads to quality degradation caused by delayed or inaccurate resolutions provided by the IT agents. The main objectives of this study are to show how the adoption of AI technologies may improve the effectiveness of IT services. Also, this study aims to evaluate the challenges on the implementation of AI solutions within the IT Service Desk by highlighting the future potential of AI solutions in the IT support world. © 2024 IEEE.

关键词： Natural language processing systems

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CenFormer: Transformer-based Network from Centroid Generation for Point Cloud Completion

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Phong Nguyen, Tran Thanh Phung, Son Lam Gopaldasani, Vinod Whitelaw, Jane Le, Hoang Thanh Bouzerdoum, Abdesselam University of Wollongong Faculty of Engineering and Information Sciences Wollongong Australia University of Wollongong Faculty of the Arts Social Sciences and Humanities Wollongong Australia Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar

Point clouds captured from 3D scanners are often sparse and incomplete due to occlusions, limited viewpoints, and sensor constraints. These limitations hinder applications in robotics, autonomous navigation, and augmented reality. Hence, point cloud completion is crucial for generating reliable 3D object representations. Existing methods often struggle to capture structural patterns effectively, which leads to low-quality reconstructions. To address these challenges, we propose Centroid Transformer (CenFormer), a novel transformer-based network for point cloud completion. CenFormer introduces two distinct types of centroids, namely Preserved and Dispersed, to facilitate fine-grained reconstruction. The proposed design includes three innovations: i) a Centroid Generation Block to aggregate features for preserved centroids, ii) a Centroid Dispersion Block to predict offsets for dispersed centroids, and iii) a Fine-grained Point Generation Block to refine local patterns around centroids. These components jointly enable the network to effectively capture local structural details and strategically target missing regions for fine-grained 3D shape reconstruction. Experiments on various benchmark datasets demonstrate that CenFormer significantly outperforms state-of-the-art methods in both visualization results and quantitative metrics. © 2020 IEEE.

关键词： 3D Object Reconstruction Deep Learning Point Cloud Completion Point Cloud Understanding Transformer

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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Computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

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Computer Modeling in engineering & sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

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Exploring Electromagnetic Time-Reversal as a Method to Monitor Cable Temperature in Medium-Voltage Networks 10

Exploring Electromagnetic Time-Reversal as a Method to Monit...

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10th International Conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Ragusa, Antonella Wouters, Peter A. A. F. Sasse, Hugh Duffy, Alistair Rachidi, Farhad Rubinstein, Marcos De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands School of Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland Institute for Information and Communication Technologies University of Applied Sciences and Arts Western Switzerland Yverdon-les-Bains Switzerland

ISBN: (纸本)9788986510225

The paper addresses the research question of whether the electromagnetic time reversal (EMTR) technique can be used to estimate the propagation speed of a signal on power cables. This value changes in the long term due to ageing of the cable insulation and in the short term due to the cable temperature variation. A first numerical investigation of the EMTR effectiveness in estimating the propagation speed in a simple cable system is described showing that EMTR is a promising technique to be used also for the monitoring of power cable temperature. Future research will be directed to implementation of the methodology in more complex networks. © 2024 The Korean Institute of Electrical Engineers (KIEE).

关键词： Power cables

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Towards a Design Framework for Conversational Agents for Diabetes Prevention 35

Towards a Design Framework for Conversational Agents for Dia...

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35th Australian Computer-Human Interaction Conference, OzCHI 2023

作者： Sawad, Abdullah Bin Alakhtar, Rayed Alturki, Badraddin Narayan, Bhuva Lin, Shanshan Prasad, Mukesh Kocaballi, A. Baki Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia The Applied College King Abdulaziz University Jeddah Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Faculty of Arts and Social Sciences University of Technology Sydney Sydney Australia Faculty of Health University of Technology Sydney Sydney Australia

ISBN: (纸本)9798400717079

Type 2 diabetes (T2D) has emerged as a significant catalyst for various health conditions. The availability and cost of health professionals pose challenges, limiting access to personalised lifestyle support. To address this issue, utilising Conversational Agents (CAs) presents an opportunity to improve scalability and adoption by improving efficiency and engagement. Thus, this study focuses on the potential impact and response to T2D. This paper aims to develop a framework for designing CA functions that support individuals at risk of T2D to be more active and raise their awareness. The study involves a mixed methods approach, including a survey conducted among 30 participants in Sydney and Jeddah, followed by semi-structured interviews conducted with 10 participants. While descriptive statistics were used to analyse the survey, the interviews were analysed using thematic analysis. Drawing upon the interviews and relevant literature, this study proposes developing a preliminary framework to help design CAs that support individuals with prediabetes to adopt a more active lifestyle. © 2023 ACM.

关键词： Computer programming

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Energy Efficient Ant Colony System for Packet Routing in Wireless Sensor Network 3

Energy Efficient Ant Colony System for Packet Routing in Wir...

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3rd International Conference on Emerging Electrical Energy, Electronics and computing Technologies 2021, ICE4CT 2021

作者： Nasir, Husna Jamal Abdul Ku-Mahamud, Ku Ruhana Kamioka, Eiji Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Malaysia School of Computing UUM College of Arts and Sciences Universiti Utara Malaysia Malaysia Graduate School of Engineering and Science Shibaura Institute of Technology Japan

Routing packets in Wireless Sensor Network (WSN) is challenging due to the distribution of sensor nodes with different ability. Inefficient routing may lead to higher failure rate, higher latency and higher energy consumption. One of the common approaches to solve this problem is by using bio-inspired routing algorithms due to their abilities to adapt with dynamic environment. This paper proposed an improved ant colony system for packing routing in WSN that focuses on exploration and exploitation techniques. In the proposed routing algorithm, the best path to be used for packet transmission will be determined by considering the remaining energy of each sensor node to reduce the hotspot problem. Local pheromone update and global pheromone update are used with the aim to prevent imbalanced energy depletion of sensor nodes and to balance the packet distribution. The proposed routing algorithm was validated against several bio-inspired routing algorithms in medium and large sized networks. The results suggested that it has outperformed in terms of success rate, packet loss rate, latency and energy efficiency. © Published under licence by IOP Publishing Ltd.

关键词： Sensor nodes

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Fractional calculus in modelling hereditariness and nonlocality in transmission lines 11

Fractional calculus in modelling hereditariness and nonlocal...

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11th International Conference of the Balkan Physical Union, BPU 2022

作者： Zorica, Dušan Cvetićanin, Stevan M. Rapaić, Milan R. Department of Physics Faculty of Sciences University of Novi Sad Trg D. Obradovića 4 Novi Sad Serbia Mathematical Institute Serbian Academy of Arts and Sciences Kneza Mihaila 36 Beograd Serbia Department of Power Electronic and Telecommunication Engineering Faculty of Technical Sciences University of Novi Sad Trg D. Obradovića 6 Novi Sad Serbia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg D. Obradovića 6 Novi Sad Serbia

Transmission lines are traditionally modelled by considering Heaviside's elementary circuit that contains a resistor and inductor in the series branch, accounting for the energy losses and magnetic effects, while the shunt branch contains a resistor and a capacitor, accounting for the energy losses and capacitive phenomena. Classical telegrapher's equations, modelling the signal propagation in a transmission line, are obtained by assuming the infinitesimal length of the elementary circuit and by passing to a continuum. The generalization of elementary circuit is two-fold: topological by adding the capacitor in the series branch in order to account for the charge accumulation effects along the line and constitutive in order to account for the memory effects that transmission line may display. The constitutive generalization is performed by changing the constitutive relation describing the capacitive and inductive material properties using the fractional calculus approach accounting for the short-tail memory. On the other hand, the inclusion of nonlocal material properties of a transmission line is performed by considering the magnetic coupling of inductors in the series branch of Heaviside's elementary circuit, so that the magnetic flux is obtained as a superposition of local and constitutively given nonlocal magnetic flux through the cross-inductivity kernel. Signal propagation is studied in the case of power, exponential, and Gauss type cross-inductivity kernels. The presented results are published in [1-3]. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Electric lines

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Dijkstra's Algorithm with Bellman-Ford for Shortest Path Discovery in Big Data 2

Dijkstra's Algorithm with Bellman-Ford for Shortest Path Dis...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Karthikeyan, M. Sekar, Satheeshkumar Basha, H. Anwer Epsiba, P. Ganesh, E.N. Meenakshi, B. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra's method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm's operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra's Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes 100-500, the former's ms value ranged from 5.2 to 52.3 and the latter's ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra's Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra's Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3. © 2024 IEEE.

关键词： Graph algorithms

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