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arXiv 2023年

作者： Yang, Chen Wang, Kailing Wang, Yuehao Yang, Xiaokang Shen, Wei MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Reconstructing deformable tissues from endoscopic stereo videos in robotic surgery is crucial for various clinical applications. However, existing methods relying only on implicit representations are computationally expensive and require dozens of hours, which limits further practical applications. To address this challenge, we introduce LerPlane, a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting. LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields, leading to a compact memory footprint and significantly accelerated optimization. The efficient factorization is accomplished by fusing features obtained through linear interpolation of each plane and enables using lightweight neural networks to model surgical scenes. Besides, LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling. We also propose a novel sample scheme to boost optimization and improve performance in regions with tool occlusion and large motions. Experiments on DaVinci robotic surgery videos demonstrate that LerPlane accelerates optimization by over 100× while maintaining high quality across various non-rigid deformations, showing significant promise for future intraoperative surgery applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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An enzyme-free interdigitated electrode for ketamine detection

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Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2023年第5期46卷 441-452页

作者： Phan, Thien Luan Van Hieu, Nguyen Lin, Jing-Jenn Wu, Hsing-Ju Chu, Pei-Yi Hsiao, Chin-Sung Yao, Fiona Yan-Dong Lin, Yung-Kai Ching, Congo Tak Shing Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung Taiwan Viet Nam Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Taiwan Research Assistant Center Show Chwan Memorial Hospital Taiwan Department of Medical Research Chang Bing Show Chwan Memorial Hospital Taiwan Department of Pathology Show Chwan Memorial Hospital Taiwan School of Medicine College of Medicine Fu Jen Catholic University Taiwan Computer Science and Information Engineering Asia University Taiwan Division of Science Engineering and Health Studies College of Professional and Continuing Education The Hong Kong Polytechnic University Hong Kong Department of Food Science National Taiwan Ocean University Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan

Ketamine is an anesthetic, which when used wrongly would affect judgment and behavior, increasing risks to individual such as unwanted or risky sexual behavior. This study reports the development of an admittance- and capacitance-spectroscopy-based sensor to detect ketamine in solution, such as whiskey, a popular alcoholic drink. The result confirms the proposed method is capable of detecting the presence of ketamine in whiskey, in concentration as low as 6.25 ng/μL, by using admittance and capacitance measured at 6.75 MHz, as well as quantifying the measured admittance (sensitivity: 300 μΩ−1/Log (ng/μL);limit of detection (LOD): 0.16 ng/μL) or capacitance data (sensitivity: 2 nF/Log (ng/μL);LOD: 0.73 ng/μL) at 18 kHz and 7.69 MHz, respectively. © 2023 The Chinese Institute of Engineers.

关键词： Capacitance

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Wide-Deep Neural Networks with Randomized Sparse Backpropagation Simplify Demand-Side Load Event Detection

Wide-Deep Neural Networks with Randomized Sparse Backpropaga...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Adusumilli, Manasa Ramesh, G.P. Katkam, Vijayrani Bhargavi Pareek, Piyush Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India St. Peter's Institute for Higher Education and Research Department of Electronics and Communication Engineering Chennai India Ballari Institute of Technology and Management Department of e and Ce Bellary India Hyderabad Institute of Technology and Management Department of Cse Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

Identifying demand-side load events is a critical step in lowering energy use in a number of situations. Using wide-deep neural networks and randomised sparse backpropagation algorithms, this study presents a unique way to identify demand-side load events. To detect subtleties in the input data, we employ randomised sparse backpropagation and both shallow and deep network topologies. We also use the ReLU and sigmoid activation functions to improve the performance of our model. We use the Adam optimizer to train the proposed model, using a learning rate of 0.001 per batch and a batch size of 64. The results were compared to those of other popular technologies, such as support vector machines. According to the findings of our studies, the technique we utilised is more precise and efficient than any other option we investigated. The proposed approach combines wide-deep neural networks with randomised sparse backpropagation to simplify demand-side load event identification. © 2023 IEEE.

关键词： Energy efficiency

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Analyzing the behaviors of pedestrians and cyclists in interactions with autonomous systems using controlled experiments: A literature review

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Transportation Research Part F: Traffic Psychology and Behaviour 2025年 114卷 270-307页

作者： Danya Li Wencan Mao Francisco C. Pereira Yu Xiao Xiang Su Rico Krueger Department of Technology Management and Economics Technical University of Denmark (DTU) Denmark Information Systems Architecture Science Research Division National Institute of Informatics (NII) Japan Department of Information and Communications Engineering Aalto University Finland Department of Computer Science and Department of Agricultural Sciences University of Helsinki Finland

Urban transportation is set to undergo a profound transformation with the advent of autonomous systems such as autonomous vehicles, automated buses, and sidewalk delivery robots. To promote safe, sustainable, and inclusive urban mobility, understanding and predicting the behaviors of pedestrians and cyclists, including their intentions, decisions, and movements, when they interact with autonomous systems becomes crucial. Gaining a thorough understanding of these complex interactions can not only improve the safety, efficiency, and acceptance of autonomous systems but also enhance the design and implementation of these technologies. Through a comprehensive review of the literature spanning the years 2014 to 2023, we identify 99 articles that empirically investigate the interactions of humans and autonomous systems. Based on our overview of progress and challenges within this field, we further identify five research gaps that future research should address to enhance human-autonomous system interactions, including: (1) scaling up experimental scenarios to multi-user and multi-modal setups to better represent real-world challenges, (2) emphasizing safety-critical scenarios that are difficult to achieve in real-world environments by applying virtual reality, (3) incorporating diverse behavioral data from the human perspective to deepen the understanding of vulnerable road user behavior and decisions, (4) embracing continuous and real-time interaction to better predict dynamic future environments, and (5) enhancing the generalization ability to ensure realism and broad applicability. This review article offers valuable insights for the growing human-autonomous system research community, specifically those interested in leveraging controlled experiments to enhance the understanding and prediction of pedestrians' and cyclists' behaviors in future urban environments.

关键词： Human-autonomous system interaction Human behavioral modeling Autonomous vehicles Virtual reality Controlled experiment

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Smart System for Early Detection of Agricultural Plant Diseases in the Vegetation Period

Smart System for Early Detection of Agricultural Plant Disea...

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Artificial Intelligence in Education and Industry 4.0 (IDICaiEI), DMIHER International Conference on

作者： A. Satya Mallesh Nagaraju Pamarthi P T Satyanarayana Murty Pokkuluri Kiran Sree T Daniya Balajee Maram Shri Vishnu Engineering College for Women Bhimavaram Department of CSE (AI & ML) GMR Institute of technology Rajam School of Computer Science and Artificial Intelligence SR University Warangal Telangana

The abstract introduces a smart system designed to detect agricultural plant diseases during the vegetation period. The system aims to enable early detection and intervention, contributing to enhanced crop health and yield. Leveraging advanced technologies such as remote sensing, machine learning, and data analysis, the system monitors key indicators of plant health, including leaf color, texture, and growth patterns. It compares real-time data with predefined disease profiles to identify potential infections at an early stage. Upon detection, the system alerts farmers through a user-friendly interface, facilitating timely and targeted responses such as precision spraying or quarantine measures. By harnessing the power of data-driven insights, the smart system holds promise for revolutionizing disease management in agriculture, minimizing losses, reducing pesticide usage, and ultimately ensuring sustainable food production.

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Integrating Inception v3 and Retinex Enhancement for Accurate Diagnosis of Age-Related Macular Degeneration

Integrating Inception v3 and Retinex Enhancement for Accurat...

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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Senthil K Sivagami S Vijaya Lakshmi R Sree Dedeepya B Department of Computer Science and Engineering Saveetha School of Engineering Saveetha UniversitySIMATS Chennai Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha UniversitySIMATS Chennai Tamil Nadu Department of AI&DS Saveetha School of Engineering Saveetha UniversitySIMATS Chennai Tamil Nadu

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

Proactive identification of eye conditions remains essential to stop conditions from advancing and to enable proper medical treatment of retina-related disturbances. Over the recent past, scientists have dedicated significant attention to diagnosing eye diseases. Visual impairments create enormous obstacles that reduce patients' overall life satisfaction because they affect their ability to complete daily duties. Age-related macular Degeneration (AMD) is one of the prevalent reasons behind permanent central vision loss in both eyes that affects people progressively without causing discomfort. The proposed model architecture applies pre-processing to fundus images, which are then integrated into a Retinex algorithm before combining them with InceptionV3 to obtain AMD diagnosis results. The algorithm manifests its capability to enhance contrast and noise reduction, and its procedures necessary imaging parameters for drusen diagnosis. The InceptionV3 architecture principles are integrated to boost the diagnostic accuracy of the research outcomes. The proposed model receives training through an Open Access Series of Images Reading database consisting of 5,000 images that contain various types of eye diseases. The accuracy achieved during 10-fold cross-validation measures 95.67%. The automated method for AMD detection requires consideration for its diagnostic value, which should help professionals initiate timely medical assistance.

关键词： Macular degeneration Deep learning Wireless communication Training Accuracy Signal processing Feature extraction Spatial resolution Medical diagnostic imaging Testing

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A Robust Protocol for Smart eHealthcare based on Elliptic Curve Cryptography and Fuzzy logic in IoT

A Robust Protocol for Smart eHealthcare based on Elliptic Cu...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Abdaoui, Abderrazak Erbad, aiman Al-Ali, Abdulla Mohamed, Amr Guizani, Mohsen Qatar University Computer Science and Engineering Department College of Engineering Doha Qatar College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Division of Information and Computing Technology Doha Qatar

ISBN: (纸本)9781665423908

Emerging technologies change the qualities of modern healthcare by employing smart systems for patient monitoring. To well use the data surrounding the patient, tiny sensing devices and smart gateways are involved. These sensing systems have been used to collect and analyze the real-time data remotely in Internet of Medical Thinks (IoM). Since the patient sensed information is so sensitive, the security and privacy of medical data are becoming challenging problem in IoM. It is then important to ensure the security, privacy and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for the IoM. In this paper, in order to improve the authentication and communications in health care applications, we present a novel secure and anonymous authentication scheme. We will use elliptic curve cryptography (ECC) with random numbers generated by fuzzy logic. We simulate IoM scheme using network simulator 3 (NS3) and we employ optimized link state routing protocol (OLSR) algorithm and ECC at each node of the network. We apply some attack algorithms such as Pollard's ρ and Baby-step Giant-step to evaluate the vulnerability of the proposed scheme. © 2021 IEEE.

关键词： Fuzzy logic

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BERT-Driven Deep Learning Approach for Depression Detection in Social Media Posts

BERT-Driven Deep Learning Approach for Depression Detection ...

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Optimization Techniques for Learning (ICOTL), International Conference on

作者： Prashant Karna Sonu Kumar Keshari Ashis Kumar Mandal Basabi Chakraborty Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science University of Saskatchewan Saskatoon Canada Research and Regional Cooperation Division Iwate Prefectural University Takizawa Japan School of Computing Madanapalle Institute of Technology and Science Madanapalle India

Depression constitutes a worldwide health emergency, impacting countless individuals daily. However, timely diagnosis and treatment for depression are infrequent. Recent research has revealed affirmative links between the use of social media and the presence of depression. The use of learning techniques has become increasingly popular in detecting signs of depression in social media posts. This study introduces a method that utilizes transformer based models like BERT and RoBERTa. During the methodology phase we carefully preprocess the data followed by word embedding procedures. We then employ a combination of deep learning architectures such as LSTM, CNN with BiLSTM, RNN, BERT with Dense and RoBERTa with Dense to thoroughly evaluate and validate our models performance. Interestingly our results consistently show that the BERT with Dense and RoBERTa with Dense approaches outperform methods achieving an accuracy rate of 98%. This refined approach allows for accurate identification of signals in social media posts offering promising potential to make significant contributions, to the field of mental health advancement.

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Advanced Detection of Diabetic Foot Ulcers Using Hamiltonian Quantum GANs and Cell Attention Networks, with Genghis Khan Shark Optimization

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Biomedical Materials and Devices 2025年 1-17页

作者： Bapu, B. R. Tapas Sivamuni, Kalaimagal Samiappan, Dhandapani Sathiya, R.R. Department of Electronics and Communication Engineering S.A. Engineering College Poonamallee-Avadi Main Road Thiruverkadu Tamil Nadu Chennai 600077 India Department of AI & DS Panimalar Engineering College Varadharajapuram Poonamallee Tamil Nadu Chennai 6000123 India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai 602105 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Diabetic Foot Ulcers (DFUs) are a severe difficulties of diabetes, often resulting from peripheral vascular disease, neuropathy, and poor glycemic control. If left undetected, DFUs can lead to severe infections, amputations, and increased mortality rates. Despite advancements in deep learning for medical image analysis, existing DFU detection methods suffer from low accuracy, high false-positive rates, and limited generalizability, making them unsuitable for real-world clinical deployment. To point out this challenge, this study proposes a novel DFU detection framework Cell Attention Networks with Genghis Khan Shark Optimization (CANet-GKSOpt) which integrates multiple cutting-edge ai techniques to enhance diagnostic precision. The proposed method utilizes Block-Batching Domain-Guided Filtering (2B-DGF) for noise reduction and image enhancement, followed by feature extraction using Hamiltonian Quantum Generative Adversarial Networks (HQGANet) to capture complex ulcer patterns. Cell Attention Networks (CANet) are then employed for refined classification by focusing on critical image regions. To further optimize accuracy and computational efficiency, the Genghis Khan Shark Optimization (GKSO) algorithm is applied for parameter tuning. The model is evaluated on the DFU2020 dataset and achieves a satisfying accuracy around 99% and 99.8% of sensitivity, significantly outperforming existing state-of-the-art methods. These results demonstrate the robustness of CANet-GKSOpt in accurately detecting DFUs, reducing false positives, and improving classification efficiency. The proposed approach contributes to the evolution of a highly effective ai-assisted diagnostic tool, which, with further validation on multi-center datasets and real-world clinical settings, has the potential to revolutionize DFU detection and management. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Block-batching domain-guided filtering cell attention networks DFUs detection Genghis Khan shark optimization Hamiltonian quantum generative adversarial networks

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Lane Detection Using Deep Learning Approach

Lane Detection Using Deep Learning Approach

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International Conference on Computational science and Technology (ICCST)

作者： Shyam Immanuel A Kaladevi R Hariharan Shanmugasundaram Bhanu prasad A Karthikeyan R Mohammad Bilal J Department of Computer Science and Engineering Saveetha Engineering College India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of CSE (AI&ML) Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9781665476560

The recent advancements in vehicle detection and self-driving capability of autonomous vehicles has become a challenging trend in recent years. Though humans are prone to errors while they drive the car, these automated vehicles are more critical in identifying and ensuring safety. The autonomous detection capability is quiet an interesting and more importantly be an demanding topic of research. The study on detection of appropriate LANE for identifying the road layouts directs for proper route identification. In the study presented here, Convolutional Neural Network was used for effective lane detection. Convolutional Neural Network (CNN) deals with images looking for deep pixel level analysis. For the study presented here, image frames were extracted from running length videos of the driving vehicles which of reasonable length (several minutes of less that 5 in number) from which image frames were extracted and processed. The procedure involves dealing with OpenCV functions, camera calibration and perspective transformation. The results obtained is quiet promising with accuracy level 87.5% as compared to existing algorithms and leads to proper detection of LANE using the deep learning algorithmic procedure used in the study.

关键词： Deep learning Training Lane detection Convolution Roads Vehicle detection Transforms

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