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SegAN for recognition of caries from 2D-panoramic X-ray images

IEEE Access 2025年 13卷 100419-100432页

作者： Naga Srinivasu, Parvathaneni Aruna Kumari, Gorli L. Jaya Kumari, D. Barsocchi, Paolo Kumar Bhoi, Akash Amrita Vishwa Vidyapeetham Amrita School of Computing Andhra Pradesh Amaravati 522503 India GITAM University Department of Computer Science and Engineering GST Andhra Pradesh Visakhapatnam 530045 India Department of Computer Science and Engineering Sri Vasavi Engineering College Andhra Pradesh Tadepalligudem India National Research Council Institute of Information Science and Technologies Pisa 56124 Italy Sikkim Manipal University Sikkim Manipal Institute of Technology Sikkim Gangtok 737102 India

Accurate recognition and segmentation of dental caries in 2D panoramic X-ray images are crucial for timely diagnosis and strategic treatment planning. The current study uses a Generative Adversarial Network (GAN) model named SegAN to segment the X-ray samples to recognize the caries. The SegAN model works in an adversarial architecture in which the generator focuses on creating precise segmentation maps of caries from 2D panoramic X-ray images. On the other hand, the discriminator ensures that the output matches realistic segmentation patterns. The SegAN model efficiently handles the local and global contextual information for precise segmentation by considering Pixel-Wise and structural loss measures that assist in better segmentation of complex structures. Moreover, the SegAN model efficiently deals with noisy data and effectively handles class imbalances. Data augmentation, like histogram equalization and affine transforms, is performed on the input images for precise segmentation of the samples. The model was evaluated on both raw and preprocessed dental X-ray images using standard quantitative metrics. SegAN demonstrated superior performance compared to traditional segmentation approaches, achieving an accuracy of 98.5%, and a dice coefficient of 0.936 in caries detection. © 2013 IEEE.

关键词： data augmentation deep learning dental imaging generative adversarial network Image segmentation X-ray images

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DCGG: A Dynamically Adaptive and Hardware-Software Coordinated Runtime System for GNN Acceleration on GPUs

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IEEE Transactions on computers 2025年第7期74卷 2293-2305页

作者： Xiao, Guoqing Xia, Li Chen, Yuedan Chen, Hongyang Yang, Wangdong Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University Shenzhen Institute Shenzhen518063 China Central South University Big Data Institute Changsha410083 China Zhejiang Lab Research Center for Graph Computing Hangzhou311121 China

Graph neural networks (GNNs) are a prominent trend in graph-based deep learning, known for their capacity to produce high-quality node embeddings. However, the existing GNN framework design is only implemented from the algorithm level, and the hardware architecture of the GPU is not fully utilized. To this end, we propose DCGG, a dynamic runtime adaptive framework, which can accelerate various GNN workloads on GPU platforms. DCGG has carried out deeper optimization work mainly in terms of load balancing and software and hardware matching. Accordingly, three optimization strategies are proposed. First, we propose dynamic 2D workload management methods and perform customized optimization based on it, effectively reducing additional memory operations. Second, a new slicing strategy is adopted, combined with hardware features, to effectively improve the efficiency of data reuse. Third, DCGG uses the Quantitative Dimension Parallel Strategy to optimize dimensions and parallel methods, greatly improving load balance and data locality. Extensive experiments demonstrate that DCGG outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph Library (up to 3.10 × faster) and GNNAdvisor (up to 2.80× faster), on mainstream GNN architectures across various datasets. © 1968-2012 IEEE.

关键词： Graph neural networks

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Towards Depth-Continuous Scene Representation With a Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年 27卷 3637-3649页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

Light field (LF) captures both spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods have typically model surface depth only, while ignoring the continuous nature of depth in 3D scenes. In this paper, we use displacement field (DF) to describe this continuous property, and propose a novel depthcontinuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speed. Specifically, inspired by signed distance field in 3D object description, we aim to exploit the intrinsic depth-continuous property of 3D scenes using DF, and define a novel depth-continuous scene representation. Then, we introduce a simple yet general learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-ofthe-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D Light Field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multi-view stereo depth inference, LF super-resolution, and LF salient object detection. Experiments demonstrate improved performance when the continuous scene representation is applied, suggesting that our framework can potentially bring insights to more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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Advancing Mars Exploration: Development of a Large-Scale Gravity Compensation System for Landers during Descent

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6697-6710页

作者： Sun, Haining Tang, Xiaoqiang Ge, Shuzhi Sam Agency for Science Technology and Research Singapore Institute of Manufacturing Technology 138634 Singapore Tsinghua University Department of Mechanical Engineering State Key Laboratory of Tribology Beijing100084 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

As space exploration advances, an increasing number of planets are becoming targets for landing missions. Before officially launching a lander, it is essential to conduct landing tests on Earth to verify the functionality of the lander. One key challenge is to simulate the target planet's gravity environment during the lander's descent. In this study, a large-scale gravity compensation system is developed to physically simulate the Martian gravity for the landing tests of a Mars lander. The system integrates a constant force mechanism and electronic control systems, working closely to ensure smooth transitions between working states during the landing tests. Based on the principle of minimum potential energy, an expression for the output force of the constant force mechanism is established, and the influence of factors such as mechanical parameters on the output force is analyzed. Experimental results show that the system can provide gravity compensation for a 720-kg Mars lander. It maintains a nearly constant output force even when the lander touches the ground and during the subsequent period, demonstrating its adaptability to transient collisions and sudden state changes. © 1965-2011 IEEE.

关键词： Space research

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Simulation and Analysis of a MEMS based Micromirror System for Deployment in Optical Beam Steering Systems Used in Experimental Quantum computing 4

Simulation and Analysis of a MEMS based Micromirror System f...

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4th International Conference for Emerging Technology, INCET 2023

作者： Abani, Rita Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798350335750

The aim of this study is to highlight the utility and applicability of Microelectromechanical systems (MEMS) technology in addressing the existing challenges of precision control of beams in performing manipulation of two-dimensional atomic systems or states of a qubit in Quantum Optics setups. This study specifically focuses on the utility of a controllable MEMS based Micromirror that maneuvers a laser source to access multiple positions of a qubit in a two-dimensional trapped lattice;an application of pertinent interest to Experimental Quantum Computation. This paper focuses on simulating a MEMS based micromirror system proposed by (Knoernschild et al. [1]) in COMSOL and analyzing its architecture. © 2023 IEEE.

关键词： MEMS

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Automating Machine Learning Model Development: An OperationalML Approach with PyCARET and Streamlit

Automating Machine Learning Model Development: An Operationa...

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2023 Innovations in Power and Advanced computing Technologies, i-PACT 2023

作者： Annamalai, R. Deena, S. Venkatakrishnan, R. Shankar, Harrieni Sree Harshitha, Y. Harini, K. Nikhil Reddy, M. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Porur Chennai India

ISBN: (纸本)9798350325188

OperationalML (Automatic Machine Learning)refers to tools and techniques that automate various machine-learning model development aspects, including data preparation, feature engineering, model selection, hyperparameter optimiza-tion, and evaluation. OperationalML can significantly reduce the time and expertise required for building high-quality machine- learning models while making the process more transparent and scalable. In this study, we present an implementation of OperationalML using two open-source libraries: PyCARET and Streamlit. Our work focuses on evaluating the performance and usability of our implementation across classification tasks from publicly available datasets. Results show that our OperationalML system outperforms manual approaches, especially in speed and ease of use. Additionally, we conduct sensitivity analysis to assess the effectiveness of each component within the system and offer insights into future improvements that could enhance the overall quality of the generated models. This work contributes to the growing body of literature on Machine learning in Production, serving as a valuable resource for practitioners, researchers, and educators who want to quickly develop effective predictive models without spending extensive time on training machine learning. © 2023 IEEE.

关键词： Automatic Machine Learn- ing Data Preparation Feature engineering Hyperparameter Optimization Model Selection PyCaret Streamlit

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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