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8th International Congress on Information and Communication technology, ICICT 2023

作者： Thamae, Reneuoe Abdullah, Hanifa Mujinga, Mathias School of Computing College of Science Engineering and Technology University of South Africa Pretoria South Africa

ISBN: (纸本)9789819930425

Digital transformation has influenced organizations’ operations significantly. However, non-compliance with cybersecurity policy (CSP) is a growing concern for organizations. technology alone cannot protect organizational cyber assets such as computer systems, networks, and data. Human aspects should be considered when designing and implementing a CSP. The lack of effective CSP training and awareness programs (CSPTAP) is attributable to employees’ non-compliance with the CSP. This paper aims to develop a framework to enhance employees’ compliance with the CSP by implementing effective CSPTAP. Drawing from the present literature and reflecting on the existing behavior change wheel (BCW) framework, and capability, opportunity, motivation, and behavior (COM-B) model, the cybersecurity policy compliance (CSPC) framework is developed. The CSPC framework comprises the following key concepts: learning existing policies, conducting employees’ gap analysis, reviewing existing policies/developing new policies, provision of relevant content and delivery mode, and periodic auditing. These key concepts are essential elements in cybersecurity policy compliance. The model indicates that the implementation of the essential elements will substantially influence employees’ compliance with CSP. Moreover, when organizations consider these key elements, cybersecurity policy training and awareness, can positively enhance employees’ CSP compliance. The proposed development of CSPTAP provides a firm base for future empirical work including action research. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Personnel training

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AOBL-IPACO: A novel and optimized algorithm to mitigate losses in electrical grid systems

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International Journal of Information technology (Singapore) 2025年第3期17卷 1919-1927页

作者： Gupta, Rahul Khanna, Ashish Virdee, Bal School of Electrical and Electronics Engineering Maharaja Agrasen Institute of Technology Himachal Pradesh India School of Computer Science Engineering & amp Technology Maharaja Agrasen Institute of Technology Delhi India School of Computing & amp Digital London Metropolitian University London United Kingdom

This paper shows the framework for solving the economic optimization problem in electrical grid system using Advanced Oppositional Based Learning (AOBL) technique with Invasive Plant Ant Colony Optimization (IPACO) algorithm. The work focuses on application of newly developed algorithm called Advance Oppositional Invasive Plant Ant Colony Optimization (AOIPACO) to solve the various constraints of single objective problem. The artificial algorithm leads to direction of exploration for new space of ant colonies with process of threats elimination from native candidates in energy management. During simulation process, the proposed algorithm reduces the power loss along with improvement of cost factor and computational time. The proposed AOIPACO approach is implemented on standard IEEE-5, 13 and 40 bus systems and generates optimal results as compared to other conventional techniques while solving the complex problem of optimization. Further, this meta heuristic technique help to improve the quality of power market system with minimum computation error at fast convergence rate. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cost function Economic and Emission Dispatch (EED) Invasive Ant Colony Optimization Invasive Plant Optimization Oppositional Base Learning (OBL)

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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 278-289页

作者： Zhao, Wenyu Zhou, Dong Cao, Buqing Zhang, Kai Chen, Jinjun Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan425100 China Swinburne University of Technology Department of Computing Technologies MelbourneVIC3122 Australia Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China

The cross-modal molecule retrieval (Text2Mol) task aims to bridge the semantic gap between molecules and natural language descriptions. A solution to this nontrivial problem relies on a graph convolutional network (GCN) and cross-modal attention with contrastive learning for reasonable results. However, there exist the following issues. First, the cross-modal attention mechanism is only in favor of text representations and cannot provide helpful information for molecule representations. Second, the GCN-based molecule encoder ignores edge features and the importance of various substructures of a molecule. Finally, the retrieval learning loss function is rather simplistic. This article further investigates the Text2Mol problem and proposes a novel adversarial modality alignment network (AMAN) based method to sufficiently learn both description and molecule information. Our method utilizes a SciBERT as a text encoder and a graph transformer network as a molecule encoder to generate multimodal representations. Then, an adversarial network is used to align these modalities interactively. Meanwhile, a triplet loss function is leveraged to perform retrieval learning and further enhance the modality alignment. Experiments on the ChEBI-20 dataset show the effectiveness of our AMAN method compared with baselines. © 2020 IEEE.

关键词： Semantics

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Web applications testing techniques: a systematic mapping study

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International Journal of Web engineering and technology 2023年第4期17卷 372-412页

作者： Hanna, Samer Ahmad, Amro Al-Said Department of Software Engineering Faculty of Information Technology Philadelphia University Jordan School of Computing and Mathematics University of Keele United Kingdom

Due to the importance of web application testing techniques for detecting faults and assessing quality attributes, many research papers were published in this field. For this reason, it became essential to analyse, classify and summarise the research in the field. To achieve this goal, this research conducted a systematic mapping study on 98 research papers in the field of web applications testing published between 2008 and 2021. The results showed that the most commonly used web applications testing techniques in literature are model-based testing and security testing. Besides, the most commonly used models in model-based testing are finite-state machines. The most targeted vulnerability in security testing is SQL injection. Test automation is the most targeted testing goal in both model-based and security testing. For other web applications testing techniques, the main goals of testing were test automation, test coverage, and assessing security quality attributes. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Mapping

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Quantum Secure Email Communication for Phishing Prevention 7

Quantum Secure Email Communication for Phishing Prevention

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Chandre, Pankaj Sontakke, Palash Patil, Rajkumar Shendkar, Bhagyashree D Dhotre, Dhanraj Vanarote, Viresh Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India Mit Art Design and Technology University Mit School of Computing Department of Information Technology Pune India

ISBN: (纸本)9798331527822

The rise of sophisticated phishing attacks has necessitated stronger security measures for email communication, especially in sensitive environments. This paper proposes a Quantum Secure Email Communication Framework designed to prevent phishing by integrating Quantum Key Distribution (QKD) with traditional email security mechanisms. The framework leverages quantum keys to encrypt email communication, ensuring that only legitimate users with the correct quantum key can send and receive secure emails. The architecture also incorporates a Phishing Detection Engine that validates email content and sender information using quantum-encrypted keys, alerting users to potential phishing threats. The system operates through the secure exchange of quantum keys between the sender's email server and the user via a QKD Channel, with quantum encryption applied to secure outgoing and incoming emails. In parallel, the phishing detection module analyses all email traffic for signs of malicious activity, using a QKD-based Email Validator to confirm the legitimacy of emails. This hybrid approach ensures both the privacy of communication and the proactive detection of phishing attempts, offering a robust defense mechanism in the increasingly hostile landscape of digital communication. © 2024 IEEE.

关键词： Phishing

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A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5733-5745页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Phung, Son Lam Le, Hoang Thanh Baali, Hamza University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Hamad Bin Khalifa University Information and Computing Technology Division College of Science and Engineering Doha Qatar

Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at https://***/ftivive/SINet. © 2024 IEEE.

关键词： Deep learning

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A DECOUPLED,LINEARLY IMPLICIT AND UNCONDITIONALLY ENERGY STABLE SCHEME FOR THE COUPLED CAHN-HILLIARD SYSTEMS

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Journal of Computational Mathematics 2025年第3期43卷 708-730页

作者： Dan Zhao Dongfang Li Yanbin Tang Jinming Wen School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China College of Information Science and Technology Jinan UniversityGuangzhou 510632China

We present a decoupled,linearly implicit numerical scheme with energy stability and mass conservation for solving the coupled Cahn-Hilliard *** time-discretization is done by leap-frog method with the scalar auxiliary variable(SAV)*** only needs to solve three linear equations at each time step,where each unknown variable can be solved *** is shown that the semi-discrete scheme has second-order accuracy in the temporal *** convergence results are proved by a rigorous analysis of the boundedness of the numerical solution and the error estimates at different *** examples are presented to further confirm the validity of the methods.

关键词： Coupled Cahn-Hilliard system Leap-frog method Scalar auxiliary variable Error estimate

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Adaptive Decentralized Control of Connected Cars for Safe Platooning with Unconnected Motorized Two-Wheelers 18

Adaptive Decentralized Control of Connected Cars for Safe Pl...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Barooah, Uddipan School of Computing and Electrical Engineering Indian Institute of Technology Mandi Himachal Pradesh 175005 India

ISBN: (纸本)9798350387278

Connected Vehicles (CVs) play a pivotal role in enhancing traffic safety and efficiency by leveraging real-time coordination through vehicle-to-vehicle (V2V) communication. By utilizing advanced driver assistance systems (ADAS), CVscan significantly reduce accidents and improve traffic flow through cooperative maneuvers, like platooning. However, in developing countries, CVs have to interoperate with vehicles of various types, dynamics, and levels of connectivity, which necessitates the study of mixed traffic platooning. This paper focuses on such a mixed traffic platoon consisting of connected cars (CCs) with heterogeneous dynamics, and human-driven unconnected motorized two-wheelers (UMTWs). While the CCs are automated and can share real-time information via V2V communication, UMTWs depend entirely on the driver's perception and reaction. The objective is to design adaptive decentralized controllers for each CC to ensure safe platooning, even in the presence of UMTWs operating on urban roads. We utilize the variable time headway (VTH) spacing policy for each CC to maintain an efficient collision-free safe distance from its preceding vehicle. This adaptive approach caters to dynamic traffic conditions, thereby ensuring smooth and secure traffic flow on urban roads. The analytical results are substantiated through numerical simulations, demonstrating the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Deep learning on medical image analysis

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CAAI Transactions on Intelligence technology 2025年第1期10卷 1-35页

作者： Jiaji Wang Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Biological Sciences Xi'an Jiaotong-Liverpool UniversitySuzhouJiangsuChina Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia School of Computer Science and Engineering Southeast UniversityNanjingJiangsuChina

Medical image analysis plays an irreplaceable role in diagnosing,treating,and monitoring various *** neural networks(CNNs)have become popular as they can extract intricate features and patterns from extensive *** paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained *** paper also discusses how transfer learning has been applied to different areas within medical image *** comprehensive overview aims to assist researchers,clinicians,and policymakers by providing detailed insights,helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

关键词： CNN deep learning medical image transfer learning

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