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27th International Conference on Pattern Recognition, ICPR 2024

作者： Sharafeldeen, Ahmed Khelifi, Adel Ghazal, Mohammed Yaghi, Maha Mahmoud, Ali Contractor, Sohail El-Baz, Ayman Department of Bioengineering University of Louisville LouisvilleKY United States Computer Science and Information Technology Department Abu Dhabi University Abu Dhabi United Arab Emirates Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Radiology University of Louisville LouisvilleKY United States

ISBN: (纸本)9783031783463

This paper presents a new automated unsupervised segmentation system to accurately delineate the pulmonary region in 3D computed tomography (CT) scans. It operates on a multi-dimensional joint probability model, which leverages a deep learning-based transformer model to optimize the log-likelihood of that probabilistic model. This probability model integrates appearance probability models, that represent various radiodensity distributions in both lung and chest areas within the 3D CT volume using linear combination of Gaussian (LCG), along with their spatial probability model, generated based on a 3D Markov Gibbs random field (MGRF), with potentials estimated analytically. Finally, the generated segmentation is further refined using a transformer model to maximize the log-likelihood of a given region. The proposed method’s efficacy is assessed by analyzing 3D chest scans of 28 patients diagnosed with varying severities of COVID-19. Four metrics are employed for evaluation, namely, Dice similarity coefficient (DSC), overlap coefficient, Hausdorff distance (HD), and absolute volume difference (AVD). The proposed system demonstrates outstanding performance, with scores of 95.60±1.37%, 91.61±2.51%, 6.56±2.68, and 6.23±3.52, respectively. These findings underscore the potential of the proposed system in delineating both normal and pathological lung regions in CT images, when compared to its individual components as well as six state-of-the-art (SOTA) segmentation methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Digital twins: cornerstone to circular economy and sustainability goals

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Environment, Development and Sustainability 2025年 1-42页

作者： Ali, Zain Anwar Zain, Mahreen Hasan, Raza Pathan, Muhammad Salman AlSalman, Hussain Almisned, Faisal Abdulaziz Maynooth International Engineering College Maynooth University Co. Kildare Maynooth W23 V5XH Ireland Electronic Engineering Department Sir Syed University of Engineering & Technology Karachi Pakistan College of Economics and Management Nanjing University of Aeronautics and Astronautics 29 Jiangsu Avenue Nanjing China Department of Science and Engineering Southampton Solent University Southampton SO14 0YN United Kingdom School of Computing Dublin City University Dublin Ireland Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Accurate information is a prerequisite for circulation and greater longevity of materials and products, enabling the economically and environmentally successful transition of circular economy (CE) in the aspiration towards sustainable development. However, a lack of appropriate digital tools is noticed in supplying relevant information and supporting recyclability, reusability, and reducibility processes. This study focuses on a building block of evolving technologies anointed the digital twin (DT). It aims to conceptualize DT as a cornerstone for the CE paradigm, positively influencing sustainability. It also endeavors to scrutinize remarkable implementations of DT-driven CE, accomplishing sustainable development goals (SDGs) in Ireland. An iterative methodology based on four steps is adopted to review an explosion of relevant, recent literature published in 2019–2024 and multiple case studies of Irish construction and manufacturing sectors. By analyzing the literature, this study discovers the evolution and taxonomy of DT, other technologies essential for its deployment, its proactive role in implementing CE and SDGs, and some challenges. Key findings of case studies reveal the current trajectory of employing DT with other digital products, upholding the CE practices within the bounds of the SDGs in Irish companies. In conclusion, DT is a conceivable means for advancing CE solutions and attaining SDGs towards social, economic, and environmental sustainability. The implications of this study are both theoretically and practically significant, encouraging industrialists and academia to adopt DT. It will help scholars gain a thorough understanding and guide industrialists to downsize their carbon footprint and waste, accomplishing SDGs. It will inspire managers with DT-driven information for decision-making and facilitate government and policymakers to collaborate in designing essential policies. © The Author(s) 2025.

关键词： Circular Economy (CE) Digital Twin (DT) Ireland R-principles Sustainability Sustainable Development Goals (SDGs)

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CAMP-GNN: A Constraint-Aware Message-Passing Model for Optimal Resource Allocation in Software Projects

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IEEE Access 2025年 13卷 87965-87977页

作者： Muhammad Asif Fahd M. Aldosari Donia Y. Badawood Abdu Salam Department of Computer Science Bacha Khan University Charsadda Chārsadda Pakistan Department of Computer and Networks Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Data Science Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Abdul Wali Khan University Mardan Mardan Pakistan

Resource allocation in software projects is a critical challenge, with inefficiencies in handling dynamic constraints such as skill mismatches, evolving task dependencies, and budget overruns leading to widespread cost and schedule overruns. To address these issues, this paper introduces CAMP-GNN, a novel constraint-aware graph neural network (GNN) framework designed to optimize resource allocation in software projects, addressing challenges like skill mismatches, evolving task dependencies, and budget overruns. These issues often lead to significant cost and schedule overruns, and CAMP-GNN aims to resolve them by dynamically adapting to project constraints. The framework integrates urgency, skill alignment, and budget slack directly into its message-passing layers, allowing for more precise and context-aware task-resource assignments. A key contribution is SoftStat, the first structured dataset that combines resource pools, allocations, and project details, enabling the modeling of complex task-resource dependencies and financial/temporal constraints. CAMP-GNN also introduces domain-specific message aggregation rules to unify urgency, skill alignment, and budget slack, advancing GNNs beyond basic relational modeling. A novel preprocessing pipeline is proposed to convert raw project timelines, skill dependencies, and budget thresholds into machine-learnable features, ensuring effective integration with computational models. Validated on both synthetic and real-world datasets, CAMP-GNN demonstrates superior performance with a 0.12 MAE, RMSE of 0.18 in allocation, F1 of 0.89 in budget violations, and 085 in deadline violations, 18% cost savings, and a 27% reduction in deadline breaches, outperforming existing methods. Future work will focus on scaling the framework for large-scale projects and improving interpretability for broader adoption.

关键词： Resource management Software Optimization Costs Mathematical models Computational modeling Dynamic scheduling Project management Deep learning Data models

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A Semi-Supervised Deep Learning Approach for Intrusion Detection and Classification for the Internet of Things

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Biomedical Materials and Devices 2025年 1-17页

作者： Uthradevi, G. Thiruvasagam, P. Mythili, S. Manoj, S. Oswalt Department of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Electrical and Electronics Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Biomedical Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul 624622 India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India

Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats and ensuring the security of critical infrastructures. This paper introduces an innovative hybrid model that combines Deep Belief Networks (DBNs), Principal Component Analysis (PCA), and Support Vector Machines (SVM) with adversarial learning to enhance the detection of both known and emerging cyber threats. The proposed model achieves a strong balance of accuracy, efficiency, and scalability, making it highly effective for real-world applications. By incorporating adversarial learning, the system can detect zero-day exploits—previously unseen attacks—and adapt to evolving threats. This is achieved by training the model on adversarial samples, which improves its resilience to variations in attack patterns. SVM further enhances the model’s ability to classify known and unknown threats with high precision. The model was rigorously tested on two benchmark datasets, NSL-KDD and CICIDS2017, achieving outstanding results. It demonstrated a high accuracy rate of 99.73% on NSL-KDD and a low false positive rate of 0.55%, while on CICIDS2017, it achieved a false positive rate of 0.73%. The model also outperformed existing approaches in precision, recall, and F1-score, particularly in detecting complex attack types such as R2L (Remote-to-Local), U2R (User-to-Root), and Botnet attacks. Additionally, PCA reduced the feature space by 40%, significantly lowering the model’s inference time to just 9.0 ms per sample. This makes the model highly suitable for real-time intrusion detection in high-traffic network environments, where speed and efficiency are critical. Overall, this hybrid model strengthens cybersecurity defenses and contributes to building resilient infrastructures. By effectively detecting and mitigating cyber threats, it supports the creation of safer, fairer, and more inclusive digital communities. The integration of adversarial learning and dimensionality reduction ensures rob

关键词： Adversarial learning Cyber security Deep belief networks Dimensionality reduction Feature extraction IDS Networks PCA SVM

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Designing a cellular manufacturing system as a step toward creating sustainable cells under uncertain demand condition

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Journal of Remanufacturing 2025年第1期15卷 151-177页

作者： Almasarwah, Najat Abdelall, Esraa Bhutta, Mohammed Khurrum S. Saraireh, Mohammad Department of Industrial Systems Engineering Mutah University Alkarak 61710 Jordan Industrial Engineering Department Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan Department of Management College of Business Ohio University Athens 45701 OH United States Department of Computer Engineering Mutah University Alkarak 61710 Jordan

This paper focuses on designing a cellular manufacturing system as a step toward creating sustainable cells. The proposed design aims to manufacture final products and remanufacture the returned products to diminish waste, where the return rate for each product is known. Firstly, the product families are formed using the uncapacitated mathematical model. Secondly, a nonlinear stochastic mathematical model is utilized for the propounded design as a step toward creating sustainable cells, where machine duplications are allowed. Besides, a simulation model is developed to handle the uncertainty in demand. Further, the performance of the proposed design with flexible cells is compared to regular cells. The results explain the ability of the proposed design to minimize the total number of machines. Furthermore, allowing machine duplication in the proposed design enhances the system flexibility;thus, machine breakdown will not impact system performance. The total number of machines required to cover the demand increases as the risk level of demand cover decreases. Therefore, the average waiting time and average work in process increase. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Designing a cellular manufacturing system as a step toward creating sustainable cells Flexible cells Nonlinear mathematical model Simulation Stochastic demand

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FairXbar: Improving the Fairness of Deep Neural Networks with Non-Ideal in-Memory Computing Hardware

FairXbar: Improving the Fairness of Deep Neural Networks wit...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Sohan Salahuddin Mugdho Yuanbo Guo Ethan G. Rogers Weiwei Zhao Yiyu Shi Cheng Wang Department of Electrical and Computer Engineering Iowa State University of Science and Technology Ames IA Computer Science and Engineering University of Notre Dame Notre Dame IN

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

While artificial intelligence (AI) based on deep neural networks (DNN) has achieved near-human performance in various cognitive tasks, such data-driven models are known to exhibit implicit bias against specific subgroups, leading to fairness issues. Most existing methods for improving model fairness only consider software-based optimizations, while the impact of hardware is largely unexplored. In this work, we investigate the impact of underlying hardware technology on AI fairness as we deploy DNN-based medical diagnosis algorithms onto in-memory computing hardware accelerators. Based on our newly developed framework that characterizes the importance of DNN weight parameters to fairness, we demonstrate that device variability-induced non-idealities such as stuck-at faults and noises due to variation can be exploited to deliver improved fairness (up to 32% improvement) with significantly reduced trade-off (less than 1% loss) of the overall accuracy. We additionally develop a hardware non-idealities-aware training methodology that further mitigates the bias between unprivileged and privileged demographic groups in our experiments on skin lesion diagnosis datasets. Our work suggests exciting opportunities for leveraging the hardware attributes in a cross-layer co-design to enable equitable and fair AI.

关键词： Training Noise Artificial neural networks In-memory computing Skin Medical diagnosis Lesions Artificial intelligence Hardware acceleration Optimization

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FedNorm: Optimised Federated Learning Strategy Using Hybrid Regularisation Norm 12th

FedNorm: Optimised Federated Learning Strategy Using Hybrid ...

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12th International Conference on Big Data and Artificial Intelligence, BDA 2024

作者： John, Binu Menon, Varun Adhikari, Mainak Department of Computer Science and Engineering SCMS School of Engineering and Technology Kerala Ernakulam683576 India APJ Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India School of Data Science Indian Institute of Science Education and Research Vithura Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9783031818202

Federated Learning (FL) has emerged as a game-changing method for training machine learning (ML) models on decentralized data sources without centralizing the data itself. Conventional FL algorithms frequently exhibit performance instability and inefficiency, especially when working with non-Independent and Identically Distributed (non-IID) data distributions. To overcome these difficulties, we present FedNorm, a new FL method that uses hybrid regularisation approach to improve model stability and accuracy by balancing sparsity and smoothness, to increase generalization across varied data distributions. Our experimental setup uses the MNIST dataset to train models in both Independent and Identically Distributed (IID) and non-IID situations. The findings show that FedNorm routinely outperforms classic algorithms like FedAvg, FedSGD, FedNova and FedSAM, with minimal test loss and high accuracy. FedNorm has an exceptional accuracy rating of 0.98 on both IID and non-IID data. FedNorm addresses the constraints of conventional FL algorithms, resulting in a strong and dependable solution for FL in diverse situations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Robust Secret Image Sharing Scheme Based on Polynomial k-Consistency

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Xiong, Lizhi Ding, Rui Yang, Ching-Nung Fu, Zhangjie Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan

The (k, n)-threshold Secret Image Sharing (SIS) is a naturally fault-tolerant technique for image privacy protection. A secret image is processed through secret sharing to generate n shadow images, which are then distributed to n different recipients. During the recovery phase, the complete secret image can be reconstructed by any k out of n shadow images. Although (k, n)-threshold SIS itself allows for the loss of up to n − k shadow images, if there are pixel errors in the remaining k shadow images, the recovery of the secret image will be declared a failure. Therefore, Robust Secret Image Sharing (RSIS) has been proposed to address the issue. However, the current proposed RSIS schemes only demonstrated limited robustness against noise attacks. This paper presents a novel k-consistencybased RSIS scheme to resist malicious attacks, including noise, JPEG compression, tampering, and cropping. In the sharing phase, a dual-SIS mechanism is first designed to perform two rounds of secret sharing on the secret image. In the recovery phase, high-quality secret image can be reconstructed based on kconsistency after attacking. The experimental results demonstrated that our scheme not only provides comprehensive robustness but also allows for flexible adjustment of shadow images’ sizes, ensuring both security and efficiency during image sharing. © 1991-2012 IEEE.

关键词： malicious attack noise robustness Secret image sharing

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Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

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光：科学与应用（英文版） 2025年第2期14卷 411-421页

作者： Waqas W.Ahmed Haicheng Cao Changqing Xu Mohamed Farhat Muhammad Amin Xiaohang Li Xiangliang Zhang Ying Wu Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia College of Engineering Taibah UniversityMadinah 42353Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Physical Science and Engineering King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Computer Science and Engineering University of Notre DameNotre DameIN 46556USA

We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon *** embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon(a-Si)layer,we achieve a significant enhancement of light *** enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes,requiring precise tuning of plasmon resonances to match the absorption region of the silicon active *** facilitate the device design and improve light absorption without increasing the thickness of the active layer,we develop a deep learning framework,which learns to map from the absorption spectra to the design *** inverse design strategy helps to tune the absorption for selective spectral *** optimized design surpasses the bare silicon planar device,exhibiting a remarkable enhancement of over 100％.Experimental validation confirms the broadband enhancement of light absorption in the proposed *** proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells,photodetectors,and optical filters.

关键词： silicon learning absorption assisted broadband enhanced films machine metascreen plasmonic

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Stock Price Prediction Using Fuzzy-RNN and Portfolio Management with Genetic Algorithm 8th

Stock Price Prediction Using Fuzzy-RNN and Portfolio Manage...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Jaiswal, Hrishikesh Anjali Arya, K.V. Department of Information Technology ABV-Indian Institute of Information Technology and Management Gwalior474015 India Department of Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Gwalior474015 India

ISBN: (纸本)9789819738588

Stock prediction and portfolio management are vital in the financial industry, aiding investors in informed decisions. This study introduces a novel approach termed the Stock Prediction and Portfolio Management Model, which amalgamates advanced machine learning algorithms with portfolio optimization techniques. The Stock Prediction and Portfolio Management Model framework substantially enhances the precision of stock price predictions, leading to an elevated performance of investment portfolios while concurrently mitigating inherent risks. The initial phase of the model focuses on predicting stock prices by harnessing historical data and employing technical indicators like the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD). In the subsequent phase, the SPPMM system meticulously constructs optimal portfolios utilizing genetic algorithms, further solidifying its efficacy in aiding investors in making informed decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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