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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Ambrogio, Giuseppina Borgia, Carmine Bortolini, Marco Catapano, Gerardo Conforti, Domenico De Napoli, Luigi Gagliardi, Francesco Galizia, Francesco Gabriele Guido, Rosita Longo, Francesco Mirabelli, Federica Mirabelli, Giovanni Mundo, Domenico Regattieri, Alberto Saffioti, Maria Rosaria Scordamaglia, Gabriele Solina, Vittorio Umbrello, Domenico Veltri, Pierpaolo University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions. © 2024 The Authors. Published by Elsevier B.V.

关键词： Tissue engineering

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Diabetic Retinopathy Detection Using PCA-Firefly-DNN Based XAI Framework 12th

Diabetic Retinopathy Detection Using PCA-Firefly-DNN Based X...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhattacharya, Sweta Uppamma, Posham School of Computer Science Engineering and Information Systems VIT Vellore Tamil Nadu Vellore63201 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9789819789450

Millions of individuals worldwide are impacted by diabetic retinopathy, which is a leading cause of blindness and vision loss. The early detection of DR is extremely important and machine learning approaches have shown promising results in this regard. The present study thus uses a DR dataset acquired from the publicly available UCI machine learning repository. The dataset is normalized using the StandardScalar technique and then feature extraction is performed using Principal Component Analysis to extract the most significant features. Further, firefly algorithm is implemented for dimensionality reduction. This dataset is fed into a DNN framework which generates superior results in comparison to the contemporary ML models. To make the findings of the model usable for clinical decision-making explainable AI approach is used to ensure transparency and interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Optimization of 6G resource allocation using CyberTwin function-based service enhancement scheme

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Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Aldrees, Asma Min, Hong Daradkeh, Yousef Ibrahim Dutta, Ashit Kumar Anjum, Mohd Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha61421 Saudi Arabia School of Computing Gachon University Seongnam13120 Korea Republic of Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Al-Kharj16273 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences Almaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Computer Engineering Aligarh Muslim University Aligarh202002 India

The most innovative Service-Optimized Logging for Resource Allocation (SOL-RA) is in situations that support 6G connection. The CyberTwin architecture optimizes the quality of service provided to end users in 6G communications by using the dependability of terahertz connections and interactions of a machine-type nature. Elastically sharing elastic resources among users is made possible by the proposed resource allocation strategy, which makes use of proprietary logger functions. This ensures that efficient allocation is achieved without overlapping or lengthy wait periods. Within the framework of SOL-RA, a categorization mechanism is implemented for dense requests, which differentiates them as either stationary or priority services. Resource allocations are dynamically done based on this categorization, which enhances the organization's responsiveness to different needs. Through a tree classifier learning mechanism, CyberTwin logs play an essential part in the processing of requests and the preparation of resources. This helps to ensure that resources are distributed without inefficiency. Requests that are generating a backlog are identified and processed in parallel by the system, which significantly reduces the amount of time that is wasted waiting. In order to guarantee a specialized distribution for the categorized outputs, resource allocations are directed by CyberTwin data logs. An examination of the performance of the SOL-RA scheme takes into account important metrics such service latency, service backlog, resource usage, and request-to-response ratio. This research offers insights into the efficacy of the scheme in maximizing service quality and resource consumption in 6G settings. © The Author(s) 2025.

关键词： Resource allocation

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Multi-View Test-Time Adaptation for Semantic Segmentation in Clinical Cataract Surgery

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2307-2318页

作者： Li, Heng Ou, Mingyang Li, Haojin Qiu, Zhongxi Niu, Ke Fu, Huazhu Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Beijing Information Science and Technology University Computer School Beijing100192 China Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way 138632 Singapore Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen518055 China

Cataract surgery, a widely performed operation worldwide, is incorporating semantic segmentation to advance computer-assisted intervention. However, the tissue appearance and illumination in cataract surgery often differ among clinical centers, intensifying the issue of domain shifts. While domain adaptation offers remedies to the shifts, the necessity for data centralization raises additional privacy concerns. To overcome these challenges, we propose a Multi-view Test-time Adaptation algorithm (MUTA) to segment cataract surgical scenes, which leverages multi-view learning to enhance model training within the source domain and model adaptation within the target domain. In the training phase, the segmentation model is equipped with multi-view decoders to boost its robustness against variations in cataract surgery. During the inference phase, test-time adaptation is implemented using multi-view knowledge distillation, enabling model updates in clinics without data centralization or privacy concerns. We conducted experiments in a simulated cross-center scenario using several cataract surgery datasets to evaluate the effectiveness of MUTA. Through comparisons and investigations, we have validated that MUTA effectively learns a robust source model and adapts the model to target data during the practical inference phase. © 1982-2012 IEEE.

关键词： Semantic Segmentation

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Hybrid Power-Frequency Multiple Access With SIC-Free for Holographic Reconfigurable Intelligent Surface-Based systems

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IEEE Wireless Communications Letters 2025年第5期14卷 1566-1570页

作者： Le, Anh-Tu Vu, Thai-Hoc Da Costa, Daniel Benevides Voznak, Miroslav VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava708 00 Czech Republic University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia

This paper proposes a hybrid power-frequency multiple access (HPMA) strategy to enhance the reliability, energy, and capacity of wireless communication systems. HPMA, a technique that reconciles all mutual benefits of orthogonal multiple access (OMA) and non-OMA (NOMA), removes the need for successive interference cancellation (SIC) and bypasses the user pairing inherent to NOMA. Focusing on holographic reconfigurable intelligent surfaces-aided systems servicing two users under imperfect channel estimation conditions, we establish theoretical frameworks for outage probability (OP) and ergodic capacity (EC), where both metrics are quantified with exact closed-form expressions and asymptotic analyses for scenarios with high signal-to-noise ratio (SNR) as well as for low-rate region cases. The former scenario provides insights into system diversity, modulation, and coding gains, and the ergodic slope;whereas the latter scenario elucidates trade-offs between energy and reliability and the method for maximizing energy-spectral efficiency. Numerical results demonstrate that HPMA outperforms conventional OMA and NOMA, improving OP in most SNR ranges in addition to enhancing EC and energy efficiency at low to moderate SNRs. © 2012 IEEE.

关键词： Hybrid power

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Yang, Xu Xing, Hongyan Ji, Xinyuan Xu, Wei Pedrycz, Witold School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34010 Turkey

Changes in the Atmospheric Electric Field Signal (AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means (FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree (MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms. © 2024

关键词： Thunderstorms

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A Novel Deep Learning Framework for Contraband Items Recognition in Smart City Applications 2nd

A Novel Deep Learning Framework for Contraband Items Recogni...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khalid, Shehzad Ahmed, Zubair Noor, Ayesha Sedik, Ahmed Asif, Waqar Department of Computer Engineering Bahria University Islamabad Pakistan Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Computer Science and Cyber Security University of West London London United Kingdom

ISBN: (纸本)9789819783441

Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas

关键词： Deep learning

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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

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computer Modeling in engineering & Sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 IEEE.

关键词： Vehicle safety

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PlantCare AI: An AI based Solution for Plant Disease Diagnosis and Assistance 6

PlantCare AI: An AI based Solution for Plant Disease Diagnos...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ghogare, Snehal Dhupe, Shivkanya Kulkarni, Abhijit Shingane, Tanish Gound, Renuka Department of Computer Science and Business Systems JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune India

ISBN: (纸本)9798331522667

The advancement of Artificial Intelligence (AI) and machine learning (ML) has introduced trans- formative solutions in plant care. This review investigates the integration of deep learning and image recognition technologies for identifying and managing plant diseases. The proposed project focuses on developing a user-centric web application designed to detect plant diseases automatically, provide treatment recommendations, suggest nearby nurseries for obtaining necessary supplies, and offer chatbot assistance for user inquiries. Leveraging advanced image recognition techniques, this system aims to make plant care more efficient and accessible. The integration of Google Maps enhances navigation to local nurseries, further simplifying access to resources and promoting better plant care practices. This study outlines current methodologies, explores challenges and opportunities, and proposes a structured framework for creating an intelligent plant care platform to meet the evolving demands of farmers and horticulture professionals. © 2025 IEEE.

关键词： Convolutional neural networks

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