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检索条件"任意字段=International Conference on Computer Engineering and Technology"

共 440832 条记录，以下是511-520 订阅

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 IEEE.

关键词： Peer to peer networks

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Optimizing Diabetic Kidney Disease Diagnosis with Machine Learning: A Novel Approach 8

Optimizing Diabetic Kidney Disease Diagnosis with Machine Le...

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8th international conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Vinitha, G. Rambabu, Bandi Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chengalpattu India CVR College of Engineering Department of Computer Science and Engineering Hyderabad India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331520762

Diabetic kidney disease (DKD) is a diabetic condition in which elevated blood sugar levels harm the kidney's filtering units, leading to kidney damage and potentially, kidney failure. About 700 million people effected by Diabetic Kidney Disease of whom approximately four million patients require kidney replacement therapy (KRT). The existing methods for predicting Diabetic Kidney Disease (DKD) have several drawbacks that limit their effectiveness and accuracy. Current models often rely on traditional biomarkers, such as blood glucose and urine albumin levels, which can be insufficient for early detection as they typically indicate kidney damage only after it has progressed. Proposed a novel technique by utilizing machine learning algorithm such as navie Bayes to predict the DKD in early stage leads to decrease the mortality rate. Proposed invention enhances the accuracy in disease diagnosis, which result in the score of 96.03, 94.03, 95.08 and 95.09 with precision, accuracy recall and F1-Score respectively. © 2025 IEEE.

关键词： Transplants

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Structural Health Monitoring Data Analysis Using Deep Learning Techniques 24

Structural Health Monitoring Data Analysis Using Deep Learni...

引用

8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Yang, Linfei Huang, Enzo Faculty of Science and Technology University of Macao China Faculty of Business Administration University of Macao China

ISBN: (纸本)9798400710094

With the rapid development of deep learning technology, its application in the field of Structural Health Monitoring (SHM) represents a significant leap from traditional detection methods. This paper aims to explore how deep learning algorithms enhance the assessment of civil infrastructure integrity by analyzing large datasets from sensors. We first introduce the background of SHM, highlighting the shortcomings of traditional methods in terms of accuracy and efficiency. Next, we employ deep learning models for data analysis, investigating their applications in structural damage detection and prediction. Through a comprehensive analysis of existing literature, we find that deep learning not only overcomes the limitations of manual inspections but also significantly improves the overall performance of SHM by enhancing real-time monitoring capabilities. Furthermore, this paper discusses hybrid systems that combine data-driven models with physical understanding, as well as the distributed intelligence brought about by edge computing. These factors collectively provide more reliable and flexible solutions for future infrastructure management. Our key findings indicate that the application of deep learning can significantly improve the accuracy and efficiency of SHM, enabling a quicker response to the needs of dynamic communities. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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An Evaluation Method based on Fuzzy Model for the Autonomous and Controllable of the Spacecraft Component Production 24

An Evaluation Method based on Fuzzy Model for the Autonomous...

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8th international conference on computer Science and Application engineering, CSAE 2024

作者： Hongxing, Hao Ronghuan, Yu Jing, Guo Huaquan, Hu Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Huairou Beijing China

ISBN: (纸本)9798400718090

The Autonomous and controllable of the spacecraft component is of great importance for the development of our space industry. The key step of the spacecraft component is the production. Many researchers focus on the estimation of the autonomous and controllable of the spacecraft component production. This paper model the evaluation method including the construction of the evaluation index system and the estimation method. The proposed estimation method is based on the fuzzy model to synthesize the qualitative, quantitative indicators and the according weights. Compared to mapping the qualitative value to a fixed number, the proposed method avoids the effect of the subjectivity. The experimental results show that the proposed method tend to think highly of the quantitative values when the qualitative values are not very too much so as to avoid the effect of the subjectivity for the qualitative indicators in the evaluation. © 2024 Copyright held by the owner/author(s).

关键词： Aerospace industry

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A hybrid CNN-LSTM approach for detecting intracerebral hemorrhage in CT images

A hybrid CNN-LSTM approach for detecting intracerebral hemor...

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Dash, Pratyusa Sabut, Sukanta Kumar Mishra, Ruby Mishra, B.S.P. Heritage Institute of Technology Department of Cse Westbengal Kolkata India Kalinga Institute of Industrial Technology School of Electronics Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology School of Mechanical Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331513894

A Stroke is a life-risk condition caused due to lack in blood flow to the part of the brain. Accurate diagnosis using computed tomographic (CT) scans is crucial for immediate care in the emergency department. A multidisciplinary approach is essential to manage this health challenge. Medical imaging using computed tomography (CT) and MRI data from stroke patients plays a crucial role in supporting clinical decision-making. Locating lesions in image is a challenging and time-consuming task for radiologists, hence automated techniques powered by Artificial Intelligence (AI) is vital for detecting and quantifying Intracerebral Hemorrhage (ICH). In this study, we proposed deep learning approach based on CNN along with long short-term memory (CNN-LSTM) for accurate prediction of ICH on CT scans. Non-contrast whole-head CT scan images were collected from 72 hemorrhagic stroke patients at KIMS, Bhubaneswar. A total of 2,394 head CT images were used to train the models with both CNN and CNN-LSTM architectures with a split of 80:20. The performance of the two models was then compared to evaluate their effectiveness. The CNN alone achieved an accuracy of 0.95, a recall of 0.89, and an F1-score of 0.94, whereas the hybrid CNN-LSTM model outperformed it with an accuracy of 0.97, a recall of 0.97, and an F1-score of 0.97. The findings indicate that the hybrid CNN-LSTM model performs better in detecting ICH lesions in CT images. Further research is essential to enhance the detection of hemorrhagic stroke in clinical settings for effective management of stroke. © 2025 IEEE.

关键词： computerized tomography

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Predicting Public Transport Passenger Using Machine Learning Algorithms 10

Predicting Public Transport Passenger Using Machine Learning...

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10th international conference on Digital Arts, Media and technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Meza, Angelo Acuna, Annsherly Santisteban, Jose School of Systems and Computer Engineering Lima Peru

ISBN: (纸本)9798331543273

This study compares five predictive models for passenger demand at the Lima Metro's Line 1 stations: Prophet, Linear Regression, Random Forest, Decision Tree, and AutoRegressive Integrated Moving Average (ARIMA). The analysis utilizes a dataset extracted from the OSITRAN website, which includes information on the number of passengers who used the metro at various stations during specific times. Data was collected from July 1 to July 7, 2024, between 5 a.m. and 11 p.m., covering all 26 metro stations. The evaluation metrics used in this study are Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The results indicate that Prophet achieved the highest scores across all three metrics, establishing it as a viable solution compared to the other models analyzed. © 2025 IEEE.

关键词： Random forests

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Image Contrast Enhancement using Fuzzy Logic with Metaheuristics 24

Image Contrast Enhancement using Fuzzy Logic with Metaheuris...

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11th international conference on Networking, Systems and Security, NSysS 2024

作者： Kabir, Mohimenul Mobin, Jaiaid Hassanat, Ahmad Sohel Rahman, M. Department of Computer Science Bangladesh University of Engineering and Technology Dhaka Bangladesh Rochester Institute of Technology Rochester United States Faculty of Information Technology Mutah University Karak Mutah Jordan Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400711589

In this work, we present a method to enhance image contrast using a fuzzy logic-based transformation function. Our goal is to develop an image-specific transformation by tuning the fuzzy system. We employ Hill Climbing and Genetic Algorithms in various configurations to evolve the fuzzy system, conducting multiple experiments. Different variants of our method are tested on several images, and the two variants with the highest fitness scores are selected. Additionally, we compare against existing contrast enhancement technique and conduct a survey to evaluate the visual improvements made by these two variants. The survey results indicate that one method effectively enhances image contrast visually. © 2024 Copyright held by the owner/author(s).

关键词： computer circuits

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE international conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Regression analysis based on driving schools in Haikou 24

Regression analysis based on driving schools in Haikou

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8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Yang, Darun Yang, Daqing Yang, Dashuai Yang, Dacai College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China Longjie Town Tianqiao Primary School Guizhou Bijie China College of Humanities and Technology Guizhou University for Nationalities Guizhou Guiyang China Guiyang University of Information Technology Guizhou Guiyang China

ISBN: (纸本)9798400710094

With the development of society and the growth of population in Haikou city, the driving school market in Haikou faces a series of challenges such as unreasonable teaching methods, cumbersome registration processes, non-standard training sites, overly long learning periods, delayed handling of student complaints, and lack of supervision. Using univariate regression and ridge regression to analyze the relationship between the number of exam takers and the number of passers, predicting the number of passers with a certain number of exam takers, providing reference for Haikou local undergraduate students and social individuals when registering for driving schools. Also, it aims to provide scientific decision-making basis for driving school managers, decision-makers, and practitioners, and predict future trends. © 2024 Copyright held by the owner/author(s).

关键词： Students

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