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FDCPNet:feature discrimination and context propagation network for 3D shape representation

虚拟现实与智能硬件(中英文) 2025年第1期7卷 83-94页

作者： Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall *** address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh *** FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh *** Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline ***,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.

关键词： 3D shape representation Mesh model MeshNet Feature discrimination Context propagation

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In-service performance of emergency shutdown valves and dependent operational relationships in the offshore oil and gas industry

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Petroleum 2023年第4期9卷 613-620页

作者： Keith Cameron Andrew Lewis Diogo Montalvao Mohammad Reza Herfatmanesh School of Physics Engineering and Computer ScienceUniversity of HertfordshireUnited Kingdom Department of Design and Engineering Faculty of Science and TechnologyBournemouth UniversityUnited Kingdom

Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offshore oil and gas processing plants and have been mandated in the design of such systems from national and international standards and *** paper has used actual ESDV operating data from four mid/late life oil and gas production platforms in the North Sea to research operational relationships that are of interest to those responsible for the technical management and operation of *** first of the two relationships is between the closure time(CT)of the ESDV and the time it remains in the open position,prior to the close *** has been hypothesised that the CT of the ESDV is affected by the length of time that it has been open prior to being closed(Time since the last stroke).In addition to the general analysis of the data series,two sub-categories were created to further investigate this possible relationship for CT and these are“above mean”and“below mean”.The correlations(Pearson's based)resulting from this analysis are in the“weak”and“very weak”*** second relationship investigated was the effect of very frequent closures to assess if this improves the *** operational records for six subjects were analysed to find closures that occurred within a 24 h period of each ***,no discriminating trend was apparent where CT was impacted positively or negatively by the frequent closure *** was concluded that the variance of ESDV closure time cannot be influenced by the technical management of the ESDV in terms of scheduling the operation of the ESDV.

关键词： Emergency Shutdown Valve Safety instrumented systems Closure time IEC-61511

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Home-based Detection and Prediction of Diabetic Foot Ulcers at Early Stage Using Sensor technology and Supervised Learning

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Journal of Harbin Institute of technology(New Series) 2024年第1期31卷 26-37页

作者： Kamasamudram Bhavya Sai Rishi Raghu Sai Surya Varshith Nukala Jayashree Jayaraman Vijayashree Jayaraman School of Computer Science and Engineering Vellore Institute of TechnologyVellore 632014India

For years,foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients’ health-related quality of life(HRQoL). Diabetes foot ulcers impact15% of all diabetic patients at some point in their lives. The facilities and resources used for DFU detection and treatment are only available at hospitals and clinics,which results in the unavailability of feasible and timely detection at an early stage. This necessitates the development of an at-home DFU detection system that enables timely predictions and seamless communication with users,thereby preventing amputations due to neglect and severity. This paper proposes a feasible system consisting of three major modules:an IoT device that works to sense foot nodes to send vibrations onto a foot sole,a machine learning model based on supervised learning which predicts the level of severity of the DFU using four different classification techniques including XGBoost,K-SVM,Random Forest,and Decision tree,and a mobile application that acts as an interface between the sensors and the patient. Based on the severity levels,necessary steps for prevention,treatment,and medications are recommended via the application.

关键词： diabetic foot ulcer podiatry diabetes mellitus healthcare footcare internet of things machine learning human⁃computer interaction

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Enhancing Image Super-Resolution with GANs Featuring Enhanced Deep Super-Resolution and Attention Mechanisms 4

Enhancing Image Super-Resolution with GANs Featuring Enhance...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Kumar, G. Aditya Kativar, Aditi Kannan, A. Vellore Institute of Technology Computer Science and Engineering Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387063

Improving the quality and resolution of low- resolution digital images is an important task with far-reaching implications for a variety of applications, including medical imaging, surveillance, and content retrieval. To solve the high- resolution problem of a single image, this study proposes a new Generative Adversarial Network (GAN) model that includes an Enhanced Deep Super-Resolution (EDSR) generator. Our model includes a sensitivity function and a Squeeze-and- Excitation (SE) block in an EDSR design. These SE blocks directly model channel connections to optimize channel-specific performance responses and improve network capabilities without incurring significant computational costs. After extensive training using complex data processing methods such as rotation, rotation, and rotation, our model shows an impressive ability to enhance image details and traditional culture more accurately. Our strategy outperforms existing models both statistically and qualitatively with better signal-to- noise ratios (PSNRs) on the DIV2K dataset. These results, together with deep learning models, demonstrate the role of attentional processes in improving image resolution and lay the groundwork for further work in this field. © 2024 IEEE.

关键词： Generative adversarial networks

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A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

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computers, Materials & Continua 2023年第11期77卷 1893-1910页

作者： Wei Zhang Ping He Ting Li Fan Yang Ying Liu School of Electronic and Information Engineering Hebei University of TechnologyTianjin300401China School of Artificial Intelligence Hebei University of TechnologyTianjin300401China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly *** limitations can result in the misjudgment of models,leading to a degradation in overall detection *** paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above *** contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained *** memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration *** two modules together effectively alleviate the problem of ***,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature *** a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly *** proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly *** validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and *** results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,*** findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.

关键词： Anomaly detection multivariate time series contrastive learning memory network

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Optimizing deep learning architectures for novel view synthesis: Investigating the impact of NeRF MLP parameters on complex scenes

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International Journal of Information technology (Singapore) 2024年第4期16卷 2295-2305页

作者： Singla, Kanika Nand, Parma Department of Computer Science & Engineering School of Engineering and Technology Sharda University Greater Noida India

Machine learning combined with geometric reasoning is a promising approach for generating new perspectives of a scene using limited image captures, known as neural rendering techniques. Neural radiance fields (NeRF) represent complex 3D scenes by learning the radiance implicit function and have shown impressive performance in various applications. However, the influence of NeRF's parameters on its overall performance remains largely unexplored. In this paper, we conduct an extensive grid search on NeRF's multi-layer perceptron (MLP) parameters to examine their impact on image quality. We analyze the results by varying parameters related to the MLP architecture, such as hidden layer count and neurons per layer. Our findings reveal that the choice of MLP architecture significantly affects image quality. Our proposed approach outperforms NeRF's default settings on challenging scenes, including synthetic and real-world datasets. These results shed light on the impact of different parameters on NeRF's performance, assisting practitioners in selecting suitable settings for their specific applications. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Neural radiance fields (NeRF) Neural rendering Novel view synthesis Peak Signal to Noise Ratio (PSNR)

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Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security 1

Proposed AES-based Obfuscation and Encryption Model for Clou...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 IEEE.

关键词： Cloud platforms

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TransFuseNetS: A new Image Medical Segmentation Method based on TransFuse Architecture 24

TransFuseNetS: A new Image Medical Segmentation Method based...

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Chen, Wei Wang, Zhiheng School of Computer and Information Engineering Heilongjiang University of Science and Technology Heilongjiang China School of Computer and Information Engineering Heilongjiang University of Science and Technology Anhui China

ISBN: (纸本)9798400718199

In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmentation is still a difficult problem. In view of the above problems, this project intends to use TransFuseNetS under the framework of Transfuse for image medical segmentation. TransFuseNetS further optimizes and strengthens the Fusion architecture. First of all, this project intends to design a more complex coding architecture and propose an overall attention strategy at the channel level to improve the feature representation and understandability of the image. Secondly, the linear fusion method of two directions is used to make the multi-level images more comprehensive and rich, so as to improve the identification and segmentation of multi-scale images. Through a large number of experiments, we find that TransFuseNetS can effectively improve the recognition rate of CT images. Compared with the traditional artificial neural network, the average error of IOU and ACC of artificial neural network is reduced by 0.1571, and the average error is reduced by 0.0029. The research results of this project will lay a foundation for the research and application in the fields of medical image processing and medical image processing. © 2024 ACM.

关键词： Image segmentation

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Boost diagnostic performance in retinal disease classification utilizing deep ensemble classifiers based on OCT

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Multimedia Tools and Applications 2024年 1-21页

作者： Khan, Umer Sadiq Khan, Saif Ur Rehman School of Computer and Information Science Hubei Engineering University Xiaogan432000 China School of Computer Science and Information Engineering Hubei University Wuhan430062 China Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China School of Computer Science and Engineering Central South University Yuelu District 932 Lushan S Rd Changsha Hunan China

Damage to the retinal blood vessels is critical in diabetic retinopathy, a progressively emerging health concern that often advances quietly without explicit symptoms. Optical coherence tomography-OCT has emerged as a favored noninvasive imaging technique for diagnosing diabetic retinopathy promptly and accurately. However, timely and precise diagnoses from OCT images are essential in prevention of blindness. Moreover, accurate interpretation of OCT images is challenging. Single model learning debilitates in managing diverse data types and structures, constraining its adaptability to varied environments. Its limitations become apparent in tasks requiring expertise from multiple domains, delaying overall performance. Moreover, learning may exhibit susceptibility to overfitting with large and heterogeneous datasets, resulting in compromised generalization capabilities. In this study, we propose a hybrid learning model for the classification of four distinct classes of retinal diseases in OCT images with improved generalization capabilities. Our hybrid model is constructed upon the well-established architectural foundations of ResNet50 and EfficientNetB0. By pre-training the hybrid model on extensive datasets like ImageNet and then fine-tuning it on publicly available OCT image datasets, we capitalize on the strengths of both architectures. This empowers the hybrid model to excel in discerning intricate image patterns while efficiently extracting hierarchical prediction from various regions within the images. To enhance classification accuracy and mitigate overfitting, we eliminate the fully connected layer from the base model and introduce a concatenate layer to combine two objective learning prediction. A dataset comprising 84,452 OCT images, each expertly graded for illnesses. we conducted training and evaluation of our proposed model, which demonstrated superior performance compared to existing methods, achieving an impressive overall classification accuracy of 97.

关键词： Optical tomography

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Predicting Genetic Disorders: A Link Mining Approach 2

Predicting Genetic Disorders: A Link Mining Approach

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sen, Madhura Rajasekaran, Rajkumar Jayaram Reddy, A. Govinda, K. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350306415

Genetic diseases are conditions caused by a spontaneous alteration or mutation in an individual's DNA. People can inherit genetic disorders from parents, which means they are born with them, even if they are not initially noticeable. On the other hand, some illnesses are not inherited and arise due to disease-causing mutations occurring during cell division. In link mining, link prediction is a crucial task. Based on attribute information and observed existing link information, link prediction predicts if there will be a link between two nodes. Link prediction is applicable not just in the sphere of social networks but also in other fields. This project primarily aims to forecast a person's probability of being diagnosed with a genetic condition. To accomplish this, elements such as the person's family history, the environment in which they live, and a variety of others will be considered. The likelihood of a person being diagnosed with a specific genetic condition can be predicted using the Firefly algorithm. If a person is genetically connected to someone having a given ailment, they are more likely to be diagnosed. Pollution, radioactive radiation, and other environmental factors may also affect the disease's incidence. Considering these factors, we suggest a method for determining the likelihood of a person being diagnosed via link prediction. © 2024 IEEE.

关键词： firefly algorithm genetic disorders healthcare link prediction social network analysis

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