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29th International Conference on Information Technology, IT 2025

作者： Nasiruzzaman, Md Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. Xiamen University Malaysia School of Computing and Data Science Malaysia Universiteti Metropolitan Tirana Faculty of Computer Science and IT Albania The Doctoral College United Kingdom Xiamen University Malaysia Mahdi H. Miraz Is with the School of Computing and Data Science Malaysia Wrexham University School of Computing Faculty of Arts Science and Technology United Kingdom University of South Wales Faculty of Computing Engineering and Science United Kingdom

ISBN: (纸本)9798331517649

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. © 2025 IEEE.

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Predicting Employee Churn- data Driven Approach for Retention Management

Predicting Employee Churn- Data Driven Approach for Retentio...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Jayaraj, R. Mehrotra, Safal Kumar, Harshit School of Computing Srm Institute of Science and Technology Dept. of Data Science and Business Systems Chennai India

ISBN: (纸本)9798331510022

Employee churn is a critical challenge faced by organizations across industries, leading to disruptions in productivity and increased costs associated with recruitment and training. In this study, we propose an effective solution leveraging Random Forests to predict employee churn accurately and efficiently. Our approach addresses the inherent complexities of employee turnover prediction while considering resource constraints commonly encountered in practical applications. The proposed model incorporates feature engineering techniques and ensemble learning principles to harness the predictive power of Random Forests. Through a comprehensive evaluation on real-world employee datasets, we demonstrate the superior performance of our model in comparison to existing state-of-the-art methods. Our analysis encompasses various evaluation metrics, including accuracy, precision, recall, and F1-score, showcasing the robustness and reliability of our model across diverse organizational contexts. © 2024 IEEE.

关键词： Contrastive Learning

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Offline Behavior Distillation 38

Offline Behavior Distillation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lei, Shiye Zhang, Sen Tao, Dacheng School of Computer Science The University of Sydney Australia College of Computing & Data Science Nanyang Technological University Singapore

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity O(1/(1 − γ)2). We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity O(1/(1 − γ)). Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization. The code is available at https://***/LeavesLei/OBD. © 2024 Neural information processing systems foundation. All rights reserved.

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ZeroMark: Towards dataset Ownership Verification without Disclosing Watermarks 38

ZeroMark: Towards Dataset Ownership Verification without Dis...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Junfeng Li, Yiming Chen, Ruibo Wu, Yihan Liu, Chenxi Huang, Heng Department of Computer Science Institute of Health Computing University of Maryland College Park United States College of Computing and Data Science Nanyang Technology University Singapore

High-quality public datasets significantly prompt the prosperity of deep neural networks (DNNs). Currently, dataset ownership verification (DOV), which consists of dataset watermarking and ownership verification, is the only feasible solution to protect their copyright by preventing unauthorized use. In this paper, we revisit existing DOV methods and find that they all mainly focused on the first stage by designing different types of dataset watermarks and directly exploiting watermarked samples as the verification samples for ownership verification. As such, their success relies on an underlying assumption that verification is a onetime and privacy-preserving process, which does not necessarily hold in practice. To alleviate this problem, we propose ZeroMark to conduct ownership verification without disclosing dataset-specified watermarks. Our method is inspired by our empirical and theoretical findings of the intrinsic property of DNNs trained on the watermarked dataset. Specifically, ZeroMark first generates the closest boundary version of given benign samples and calculates their boundary gradients under the label-only black-box setting. After that, it examines whether the given suspicious method has been trained on the protected dataset by performing a hypothesis test, based on the cosine similarity measured on the boundary gradients and the watermark pattern. Extensive experiments on benchmark datasets verify the effectiveness of our ZeroMark and its resistance to potential adaptive attacks. The codes for reproducing our main experiments are publicly available at GitHub. © 2024 Neural information processing systems foundation. All rights reserved.

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AI-Powered Earth Disaster Management: A Collaborative Innovation Platform 24

AI-Powered Earth Disaster Management: A Collaborative Innova...

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8th International Conference on Innovation in Artificial Intelligence, ICIAI 2024

作者： Huang, Chung-I Sun, Chen-Kai Chang, Jih-Sheng Chang, Wen-Yi Lin, Fang-Pang Wu, Jyh-Horng Chen, Wei-Yu Data Science and Technology Division National Center for High Performance Computing Taiwan High Performance Computing Division National Center for High-performance Computing Taiwan

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Comparison of the SOLA model and ARIMA for Cloud computing Resources Allocations 4

Comparison of the SOLA model and ARIMA for Cloud Computing R...

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4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024

作者： Prince Sekwatlakwatla, Sello Malele, Vusumuzi North-West University Unit for Data Science and Computing Department of Computer Science and Information Systems South Africa

ISBN: (纸本)9798350387988

Cloud computing solutions are becoming more and more popular as a way for organizations to improve productivity, save costs, and simplify procedures. The advantage of cloud services is that they enable users to store data remotely and access on-demand applications and services from a shared pool of configurable computing resources. Cloud computing resource allocation faces challenges in load balancing, effective resource management, and compliance with regulatory and legal requirements, requiring complex tools and techniques for optimal use. Most organizations are moving to cloud services and encouraging their clients to access services online. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times, and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA) and the individual method, which is autoregressive integrated moving average (ARIMA). The results show SGLA prediction accuracy increased with an average of 97.9%, and ARIMA prediction showed an accuracy of 71.5%. In this regard the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Machine learning-based gene expression biomarkers to distinguish Zika and Dengue virus infections: implications for diagnosis

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VirusDisease 2024年第3期35卷 446-461页

作者： Zeba, Ayesha Rajalingam, Aruna Sekar, Kanagaraj Ganjiwale, Anjali Department of Life Science Bangalore University Laboratory for Structural Biology and Bio-Computing Department of Computational and Data Sciences Indian Institute of Science

Zika virus (ZIKV) and Dengue virus (DENV) infections cause severe disease in humans and are significant socio-economic burden worldwide. These flavivirus infections are difficult to diagnose serologically due to antigenic overlap. The phylogenetic analysis shows that ZIKV clusters with DENVs at a higher node of the phylogenetic tree with significant genomic and structural similarity. Our study aims to identify gene biomarkers for the classification of Dengue and Zika viral infections using machine learning algorithms and bioinformatics analysis. The gene expression count matrix for single-cell RNA sequencing dataset GSE110496 was analyzed using binary classifiers, namely Logistic regression, Support Vector Machines, Random Forest, and Decision trees. The GSE110496 dataset represents a unique study of the transcriptional and translational dynamics of DENV and ZIKV infections at 4-, 12-, 24-, and 48-h time points for human hepatoma (Huh7) cells. Out of which 24-h time point has been analyzed in this study, at the optimal threshold of viral molecules. Feature selection was performed using two different approaches Random Forest Classifier (RFC) for gene ranking and Recursive Feature Elimination (RFE). Out of which RFE, showed more accuracy and precision. The classification accuracy of 89.4% and the precision of 90% were obtained using selected 10 gene features. SCY1 Like Pseudokinase 3 (SCYL3), Chromosome 1 Open Reading Frame 112 (C1orf112), Complement factor H (CFH), Heme-binding protein 1 (HEBP1), Cadherin 1 (CDH1), Nibrin (NBN), Histone deacetylase 5 (HDAC5), nuclear receptor subfamily 0, group B, member 2 (NR0B2), Annexin A9 (ANXA9) and Alcohol dehydrogenase 6 (ADH6) are the proposed gene biomarkers in this study. The functional analysis of the reported biomarkers was performed using KEGG and GO with the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt). The relationship of the selected biomarkers with DENV and ZIKV infections analyzed using a gene–gene interaction n

关键词： Biomarkers Dengue Machine learning Single cell RNA sequencing Zika

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Convolutional Neural Network-Based Multi-Fruit Classification and Quality Grading with a Gradio Interface

Convolutional Neural Network-Based Multi-Fruit Classificatio...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Agricultural products are experiencing significant losses, especially after the harvesting phase, which is the period from fruit storage to the product reaching the consumer's hands. A lack of human resources, time consumption, product handling, and human errors during the post-harvest stage of the fruit grading and sorting process result in huge losses. Deep learning (DL) offers an automated process for classifying and identifying fruits. Developing an architecture for a VGG16 convolutional neural network (CNN) within the Gradio user interface, utilizing five distinct fruit types as the model's dataset, such as apple, banana, guava, orange, apple, and pomegranate. For five different sets of fresh and defect fruits, the proposed model got 91.1% and 89.1% of test accuracy for multi-fruit clas- sification and fruit status prediction, respectively. This machine- based automated model has the potential to reduce losses and enhance fruit grading and sorting efficiency, thereby significantly impacting sustainable and productive agricultural industries through increased productivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Gesture-driven Innovation: Exploring the Intersection of Human-Computer Interaction and Virtual Fashion Try-On Systems 2

Gesture-driven Innovation: Exploring the Intersection of Hum...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Prakash, M. Arunkumar, Nithes Gokul, G. SRM Instititue of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9798350365269

Virtual Fashion Try-On systems revolutionize online fashion retail, offering immersive shopping experiences. This paper investigates integrating gesture-driven interaction into these systems to enhance engagement, realism, and usability. The paper highlights the importance of Virtual Fashion Try-On systems in addressing fit uncertainty and tactile interaction limitations in online shopping. It explores gesture-driven interaction to enrich virtual fashion experiences. The paper examines gesture recognition, covering hand gestures, body movements, and facial expressions. Various algorithms and techniques, including computer vision and machine learning, are discussed, addressing challenges like accuracy and real-time performance. Challenges and future directions in gesture-driven Virtual Fashion Try-On are discussed, covering technical hurdles, human-centered design, and ethical considerations. Recommendations for future research and development are outlined. © 2024 IEEE.

关键词： Virtual reality

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GMFLDA: Improved prediction of IncRNA-disease association via graph convolutional network

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IEEE Access 2025年 13卷 85330-85341页

作者： Kim, Kwangsu Ha, Jihwan Pukyong National University Department of Scientific Computing Busan48513 Korea Republic of Pukyong National University Major of Big Data Convergence Division of Data Information Science Busan48513 Korea Republic of

The rapid expansion of diverse networks has created a growing need to integrate multiple heterogeneous structures to effectively capture both inter- and intra-entity relationships. This integration helps preserve the intrinsic meaning of complex biological interactions. Network-based approaches have been highly effective in predicting node labels and uncovering hidden associations between entities. These methods have been widely applied in areas such as user-item recommendations, gene-gene interactions, and lncRNA-disease association prediction. In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. Specifically, GCNs are leveraged to extract and encode high-fidelity feature representations of lncRNAs and diseases, while deep matrix factorization, implemented via a multi-layer perceptron, facilitates the discovery of potential disease-associated lncRNAs. Our model exhibits outstanding predictive performance, achieving AUCs of 0.9183 and 0.9057 in leave-one-out and five-fold cross-validation experiments, respectively. Extensive comparative evaluations demonstrate that GMFLDA surpasses four state-of-the-art methods, underscoring its superior predictive capability. We anticipate that GMFLDA will serve as a powerful computational tool for biomarker discovery, significantly accelerating the identification of disease-associated lncRNAs while mitigating the time and cost constraints of traditional wet-lab experiments. © 2013 IEEE.

关键词： Matrix factorization

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