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Devices for Integrated Circuit (DevIC)

作者： Tamal Deb Jyotsna Kumar Mandal Deeptanu Sen Dept. of Computer Science Barasat College Kolkata India Dept. of Computer Science and Engineering University of Kalyani Kalyani India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

Quantum key distribution protocols printed a prominent mark in the old path of digital communication by making it even more secure. Unlike its classical counterpart, quantum key distribution protocols exploit the key principles of quantum elements, such as superposition and entanglement, to expose the intrusion in an efficient way. This paper presents a prepare and measure quantum key distribution protocol that detects the eavesdropper more efficiently by inflating bit mismatch between the sender and receiver. The proposed work achieves bit mismatch up to 62.5 percent while Eve is present in the quantum communication channel. The improvement is remarkable in comparison with the classical BB84 protocol, which has a bit mismatch up to 37.5 percent in presence of Eve.

关键词： Integrated circuits Protocols Quantum entanglement Receivers Digital communication Quantum key distribution Quantum communication

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： deep learning DenseNet-201 Kellgren-Lawrence grading Knee osteoarthritis knowledge distillation.

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A Lightweight CNN for Real-Time Pre-Impact Fall Detection

A Lightweight CNN for Real-Time Pre-Impact Fall Detection

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Design, Automation and Test in Europe Conference and Exhibition

作者： Cristian Turetta Muhammad Toqeer Ali Florenc Demrozi Graziano Pravadelli Dept. of Computer Science University of Verona Italy Dept. of Electrical Engineering and Computer Science University of Stavanger Norway Dept. of Engineering for Innovation Medicine University of Verona Italy

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Falls can have significant and far-reaching effects on various groups, particularly the elderly, workers, and the general population. These effects can impact both physical and psychological well-being, leading to long-term health problems, reduced productivity, and a decreased quality of life. Numerous fall detection systems have been developed to prompt first aid in the event of a fall and reduce its impact on people's lives. However, detecting a fall after it has occurred is insufficient to mitigate its consequences, such as trauma. These effects can be further minimized by activating safety systems (e.g., wearable airbags) during the fall itself—specifically in the pre-impact phase—to reduce the severity of the impact when hitting the ground. Achieving this, however, requires recognizing the fall early enough to provide the necessary time for the safety system to become fully operational before impact. To address this challenge, this paper introduces a novel lightweight convolutional neural network (CNN) designed to detect pre-impact falls. The proposed model overcomes the limitations of current solutions regarding deployability on resource-constrained embedded devices, specifically for controlling the inflation of an airbag jacket. We extensively tested and compared our model, deployed on an STM32F722 microcontroller, against state-of-the-art approaches using two different datasets.

关键词： Productivity Safety devices Microcontrollers Atmospheric modeling Psychology Data models Real-time systems Convolutional neural networks Fall detection Older adults

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An Advanced Hybrid Deep Neural Network for Precise Multiclass Classification and Detection of Cancerous and Lymphatic Node States

An Advanced Hybrid Deep Neural Network for Precise Multiclas...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Mahede Hasan Md. Saiful Islam Md. Jubayeed Pathan Dept. of Computer Science & Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science & Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Metastatic cancer, defined by the spread of cancer cells from their original site to distant body regions, presents considerable diagnostic hurdles. Precise identification in histopathological images is essential for optimal patient care. This research introduces an innovative hybrid framework integrating ResNet50, self-attention modules, and Gated Recurrent Units (GRUs) to enhance binary classification precision in metastatic cancer detection. In opposite to CNN-GRU, CNN-LSTM, and AlexNet-GRU models, our approach showcased remarkable performance across two datasets. For the Histopathologic Cancer Detection BreakHis dataset, the model achieved 96.00% accuracy, 95.32% precision, 96.15% sensitivity, and 95.32% specificity. On the BACH dataset, it attained 98.00% accuracy, 98.44% precision, 98.44% sensitivity, and 98.44% specificity. In multi-class classification tasks, the model achieved an impeccable score of 1.00 on both the BreakHis and BACH datasets. Those outcomes highlight the model's capacity to significantly decrease diagnostic errors and improve pathologists' diagnostic accuracy surpassing other approaches in the field.

关键词： Body regions Accuracy Sensitivity Computational modeling Cancer detection Convolutional neural networks Biological cells Residual neural networks Cancer

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201

Knee Osteoarthritis detection and classification using a cus...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： N. Shirisha K. Shilpa CH. Meghana Mohammed Abdul Rahman Dept. of Computer Science and Engineering MLR Institute of Technology Hyderabad India Dept. of Computer Science and Engineering MLR Institute of Technology Nalgonda India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Training Deep learning Accuracy Computational modeling Magnetic resonance imaging Computed tomography X-rays Feature extraction Osteoarthritis Testing

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Comparing Deep Reinforcement Learning Methods for HVAC System Optimization with Forecast Parameters Over Various Time Periods

Comparing Deep Reinforcement Learning Methods for HVAC Syste...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Prema Arokia Mary G Hema M S S Bama Nageswara Guptha Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru India Dept. of Computer Science and Engineering RV Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Buildings are significant contributors to global energy consumption. Maintaining comfortable indoor temperatures while reducing energy consumption are conflicting objectives. Deep Reinforcement Learning (DRL) is a promising area of research for building Heating, Ventilation and Air Conditioning (HVAC) system optimization. In this study an open-source framework Building Optimization Testing Framework (BOPTEST), which is a virtual testbed that help comparison different control strategies for evaluation of DRL control methods is used. A Proportional-Integral (PI) controller is used to benchmark the DRL methods. A single zone residential building of 192 m 2 with a radial heating system and a heat pump in a climate zone with high heating requirement with dynamic electricity prices with prices varying every 15 min based on demand is chosen for implementing different control strategies. On comparing Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Twin Delay DDPG (TD3) based DRL controllers and the baseline controller, the DDPG based controller reduced energy consumption by 97.3 % and operating cost by 17.7 % during the peak heating period with reference to baseline method. Then on analyzing the impact of inclusion of forecast parameters occupancy, solar irradiance, and electricity prices over the period 3, 6 and 12 hours in DDPG based controller. The prediction for 3 hours gave the greatest reduction in thermal discomfort of 99.7 % and prediction for 12 hours gave maximum reduction in cost by 30.4 % but resulted in only 82% reduction in thermal comfort when compared with baseline method indicating that longer prediction horizon is not necessarily results in better performance.

关键词： Energy consumption HVAC Costs PI control Heat pumps Electricity Buildings Deep reinforcement learning Optimization Resistance heating

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Identification of Candidate Genes for Colorectal Cancer Using Machine Learning and Bioinformatics Approaches

Identification of Candidate Genes for Colorectal Cancer Usin...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Humayan Kabir Rana Boshir Ahmed Dept. of Computer Science and Engineering Rajshahi University of Engineering & Technology (RUET) Rajshahi Bangladesh Dept. of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Colorectal cancer (CRC) stands as the second most prevalent cause of cancer deaths, and its incidence is rising over time. Identifying key genes is essential for diagnosing and developing effective therapeutic strategies for cancer. Several studies were conducted to determine the candidate genes of CRC but this is still not sufficient and further research is needed in this area. Thus, we aimed to identify the key candidate genes of CRC using The Cancer Genome Atlas (TCGA) dataset, applying the Kruskal-Wallis test and Bonferroni correction machine learning techniques. We successfully identified 9 candidate genes, including CALB2, GRP, KRAS, MLH1, NPR3, PPFIA4, SOX11, STAC2, and TRPA1, from 20518 genes of CRC using our model. In addition, we used bioinformatics frameworks to identify signaling pathways, gene ontological pathways, and PPI networks that reflect the functions of these candidate genes. We found 9 significant signaling pathways, 12 ontological pathways, and 3 hub genes for CRC. The diagnostic effectiveness of the candidate genes was evaluated through the receiver operating characteristic (ROC) analysis, and all candidate genes showed good performance according to area under the curve (AUC) values. Notably, the gene MLH1 demonstrated the highest AUC of .91. In fine, the findings of this study may play a role in disease management and offer a foundation for further laboratory investigations to uncover potential therapeutic targets for CRC treatment.

关键词： Proteins Electric potential Biological system modeling Genomics Machine learning Colorectal cancer Receivers Bioinformatics

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Chasing puppies on orthogonal straight-line plane graphs

arXiv

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arXiv 2025年

作者： Ockenfels, Johanna Okamoto, Yoshio Schnider, Patrick Dept. of Computer Science ETH Zürich Switzerland Dept. of Computer and Network Engineering The University of Electro-Communications Japan Dept. of Mathematics and Computer Science University of Basel Dept. of Computer Science ETH Zürich Switzerland

Assume that you have lost your puppy on an embedded graph. You can walk around on the graph and the puppy will run towards you at infinite speed, always locally minimizing the distance to your current position. Is it always possible for you to reunite with the puppy? We show that if the embedded graph is an orthogonal straight-line embedding the answer is yes. © 2025, CC BY.

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Crowd Digital Twins: High-Level Requirements and Architecture 22

Crowd Digital Twins: High-Level Requirements and Architectur...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Casadei, Roberto Delnevo, Giovanni Girau, Roberto Mirri, Silvia University of Bologna Dept. of Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread. © 2025 IEEE.

关键词： computational collective intelligence crowd digital twin crowd management crowd safety human crowds human-machine networks

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