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International Scientific-Professional Conference on Information Technology (IT)

作者： Md Nasiruzzaman Maaruf Ali Iftekhar Salam Mahdi H. Miraz School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Computer Science and IT Universiteti Metropolitan Tirana Albania The Doctoral College University of Wales Trinity Saint David (UWTSD) Wales UK Mahdi H. Miraz is with the School of Computing and Data Science Xiamen University Malaysia Malaysia School of Computing Faculty of Arts Science and Technology Wrexham University UK Faculty of Computing Engineering and Science University of South Wales UK

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA’s benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats.

关键词： Multi-factor authentication Education Machine learning Market research Threat assessment Real-time systems Zero Trust Complexity theory Information technology Identity management 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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Impact Analysis of Short Circuit Current Limit on Damping Phenomenon for Different PV Penetration Levels

Impact Analysis of Short Circuit Current Limit on Damping Ph...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Rafiqul Islam Chowdhury Abu Talha Maher Al-greer Shameem Ahmad Farzana Hossin School of Computing Engineering & Digital Technologies Teesside University Middlesbrough UK Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

This paper presents a transient analysis of the frequency response in a power system under four different scenarios. The study considers the integration of photovoltaic (PV) systems and the utilization of a Static Var Compensator (SVC), each leading to distinct frequency response behaviors during pre-fault, fault, and post-fault conditions. The results demonstrate that the frequency response remains within the permissible limits defined by the UK's grid code, ensuring compliance with regulatory standards. Additionally, the inclusion of PV integration introduces significant damping effects, which can be effectively mitigated by incorporating an SVC rated at 150% of the inverter's short circuit current or by increasing the inverter's short circuit current to 200% of the rated value. These findings provide valuable insights for managing frequency response in power systems with PV integration, enabling system operators to maintain stable and compliant operations.

关键词： Photovoltaic systems Damping Short-circuit currents Stability criteria Static VAr compensators Power system stability Frequency response Inverters Circuit stability Transient analysis

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Multi-modal Medical Image Fusion Using Wavelets and Morphological Filters for Diagnosis of Neurological Disorders 8th

Multi-modal Medical Image Fusion Using Wavelets and Morpholo...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Singh, Anchal Bhateja, Vikrant Tiwari, Brij Bihari Singh, Amar Patel, Anjoo Zhang, Yudong Lay-Ekuakille, Aime’ Omar, Zaid Veer Bahadur Singh Purvanchal University Shahganj Road Uttar Pradesh Jaunpur222003 India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Innovation Engineering University of Salento Ed. "Corpo O" Via Monteroni Lecce73100 Italy Faculty of Electrical Engineering University Teknologi Malaysia Johor Bahru81310 Malaysia

ISBN: (纸本)9789819784219

Screening of neurological disorders can be carried out using various medical image modalities. Outcome of these modalities yields two categories namely: Multi-Spectral (MS) and Panchromatic (PAN) images. The primary aim of medical image fusion is to combine the complementary information in PAN and MS images to minimize redundancy and enhance the diagnostic accuracy. In this paper, the proposed methodology incorporates preprocessing of PAN and MS images with combination of Morphological Top-hat and Bottom-hat transformation operators for contrast enhancement in the first stage. Further, the proposed fusion algorithm involves sub-band decomposition of the images using Discrete Wavelet Transform (DWT). The image coefficients of Low Frequency (LF) band are combined using Average Fusion rule whereas the coefficients from the High Frequency (HF) band are combined using the Max-Max Fusion rule. The resultant fused coefficients are reconstructed using Inverse Discrete Wavelet Transform (IDWT) to restore the structural and textural information of the input image. The quantitative Image Quality Assessment (IQA) of fused images are carried out using some dedicated fusion metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete wavelet transforms

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Population-Based Meta-Heuristic Optimization Algorithm Booster: An Evolutionary and Learning Competition Scheme

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Neurocomputing 2025年 643卷

作者： Wang, Jun Dong, Junyu Zhou, Huiyu Dong, Xinghui State Key Laboratory of Physical Oceanography and Faculty of Information Science and Engineering Ocean University of China China School of Computing and Mathematical Sciences University of Leicester United Kingdom

In a Population-Based Meta-Heuristic Optimization Algorithm (PMOA), individuals in the population will constantly generate new promising individuals, to form new populations. Although the population continuously changes, the variations in each individual are traceable in most algorithms. An individual in the population comes from the individual in the previous population. The direction of the evolution of populations can be identified on top of this historical inheritance relationship, which improves the efficiency of PMOAs and solves optimization problems more effectively. Since Recurrent Neural Networks (RNNs) are able to capture the temporal dependencies in sequences, we are motivated to propose a novel but simple Evolutionary and Learning Competition Scheme (ELCS), also referred to as the PMOA Booster, in which individuals keep changing for the better fitness based on the heuristic rules of the PMOA while an RNN is used to learn the process that each individual changes in order to guide the generation of promising individuals. The ELCS automatically selects the RNN or PMOA which generates more individuals with the better fitness. We test the proposed scheme using the benchmark of IEEE Congress on Evolutionary Computation 2022 competition (CEC 2022). The results show that this scheme is able to boost the performance of both the classical and state-of-the-art PMOAs and outperforms its counterparts. Also, the ELCS produces promising results in two real-world industrial scenarios. We believe that the effectiveness of the proposed ELCS is due to the adaptive competition between the RNN and the PMOA. © 2025 Elsevier B.V.

关键词： Particle swarm optimization (PSO)

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt19385 Jordan Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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On Solving the Maximum Flow Problem with Conflict Constraints

arXiv

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arXiv 2025年

作者： Montemanni, Roberto Smith, Derek H. Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy Faculty of Computing Engineering and Science University of South Wales Wales Pontypridd United Kingdom

The Maximum Flow Problem with Conflict Constraints is a generalization that adds conflict constraints to a classical optimization problem on networks used to model several real-world applications. In the last few years several approaches, both heuristic and exact, have been proposed to attack the problem. In this paper we consider a mixed integer linear program and solve it with an open-source solver. Computational results on the benchmark instances commonly used in the literature of the problem are reported. All the 160 instances benchmark instances normally used in the literature are solved to optimality for the first time, with 28 instances closed for the first time. Moreover, in the process, 6 improvements to the best-known heuristic solutions are also found. © 2025, CC BY.

关键词： Integer linear programming

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6G and Healthcare Paradigm: Hope & Hype

6G and Healthcare Paradigm: Hope & Hype

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Muhammad Arslan Usman Taimoor Muzaffar Gondal Muhammad Rehan Usman Faculty of Engineering Computing and Environment (ECE) Kingston University London United Kingdom School of Engineering Centre for Green and Smart Energy Systems Edith Cowan University Joondalup Australia

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Recently, new paradigms have been created in multiple fields due to 5G becoming globally commercial. This has compelled the researchers across the globe to start paving the roadmap of 6G by including complex concepts like, telepresence, AI based robots to cobots and medical cyber physical systems (MCPS) etc. This evolution has sparked significant change in the thinking, perception, and behavioral attributes of healthcare professionals and patients in terms of healthcare services across the globe. Online services have changed the perception of people by offering real-time remote access to the healthcare professional, saving time and money and most of all reducing mental fatigue of patients by bypassing the core hurdles in the conventional healthcare system. If just ease in online access, which is a tiny fraction of what 6G can promise, has such a positive impact on the behavior of patients and professionals, then one can imagine what impact it may have on the perception of healthcare sector. This work focuses on providing paradigm changes in healthcare sector due to the evolution of 6G in perspective of telehealth advancement, e-Health, and m-Health (e/m-healthcare). The paper will further provide the opportunities, challenges and the hope & hype in healthcare advancement associated with the progression in 6G technology.

关键词： 6G mobile communication Telepresence 5G mobile communication Telemedicine Focusing Medical services Market research Fatigue Real-time systems Computer crime

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A Hybrid Deep Learning Algorithm for Forecasting of Global Currency Market 2nd

A Hybrid Deep Learning Algorithm for Forecasting of Global C...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Yong, Yoke Leng Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Faculty of Computing and Digital Technology Help University Bukit Damansara Kuala Lumpur Malaysia Department of Business Management Scholar School System Birmingham United Kingdom Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The global currency market, which has millions of active participants and is the largest financial market in the world, has been greatly affected by the emergence of deep learning techniques. There is widespread recognition of deep learning’s effectiveness for price movement predictions, particularly in financial markets where its effectiveness has been shown. When compared to other machine learning methods, hybrid models like CNN-LSTM have shown to be the most effective and efficient. Nevertheless, there is a lack of thorough comparative research across deep learning hybrids, which makes it difficult to validate these claims empirically, especially in the diversified and extremely unpredictable FX market. To fill that void, this research builds a generic CNN-LSTM model to predict key currency pairs and compares it to another hybrid deep learning model. The objective is to evaluate and strengthen the reliability of deep learning techniques for FOREX forecasting. In order to maximize the generalizability of the model, this study mimics a categorization scenario using stringent cross-validation procedures. Important measures, including accuracy, precision, recall, and F1-score, were used to assess the models’ performance. The results show that there is a performance hierarchy, with CNN-LSTM, CNN-RNN. These findings highlight the need to evaluate deep learning models in different real-world contexts. In conclusion, this study highlights the difficulties of FOREX forecasting and the necessity for further evaluations of the predictive power of machine learning models in an ever-changing market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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