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Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

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computers, Materials & Continua 2021年第2期66卷 1613-1629页

作者： G.Arun Sampaul Thomas Y.Harold Robinson E.Golden Julie Vimal Shanmuganathan Seungmin Rho Yunyoung Nam CSE Department J.B.Institute of Engineering and TechnologyHyderabad500075India School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Anna University Regional CampusTirunelveli627007India Department of Information Technology National Engineering CollegeKovilpatti628503India Department of Software Sejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision *** of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked *** this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate *** CNN model is trained by different images of eyes that have retinopathy and those which do not have *** fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent *** feature loss factor increases the label value to identify the patterns with the kernel-based *** performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and *** results show that the proposed CNN performs better.

关键词： Convolutional neural networks dental diagnosis image recognition diabetic retinopathy detection

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Simulation and analysis of a tomographic imaging system for cloud field measurement in rectangular icing wind tunnels

Simulation and analysis of a tomographic imaging system for ...

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2024 International Conference on Optoelectronic Materials and Devices, ICOMD 2024

作者： Zhang, Quan Qing, Chao Zuo, Chenglin Hu, Jiahui Li, Yan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China Low Speed Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan Mianyang621000 China Key Laboratory of Icing and Anti/De-Icing China Aerodynamics Research and Development Center Mianyang621000 China Intelligent Oil and Gas Laboratory Southwest Petroleum University Chengdu610500 China

ISBN: (数字)9781510689046

ISBN: (纸本)9781510689039

The uniformity of cloud distribution is a critical metric for evaluating the performance of icing wind tunnels, directly affecting the accuracy of experimental results. To address the challenge of measuring cloud distribution in wind tunnels, this paper proposes a solution based on extinction tomography. A rectangular cross-sectional imaging system was developed specifically for rectangular wind tunnel structures, overcoming the limitations of traditional ring-shaped systems. To resolve the issue of insufficient projections, an iterative reconstruction algorithm combined with regularization techniques was employed for accurate image reconstruction. Additionally, the BM3D denoising algorithm was applied to enhance image quality. The results of simulation experiments demonstrate that the system effectively improves the PSNR and SSIM of reconstructed images under both Gaussian and speckle noise conditions, significantly enhancing the accuracy of cloud distribution measurements. © 2025 SPIE.

关键词： Image reconstruction

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QoS Optimization via Computation Offloading in Metaverse Environment

QoS Optimization via Computation Offloading in Metaverse Env...

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IEEE International Conference on Web Services (ICWS)

作者： Zhiyuan Ge Pengcheng Zhang Huiying Jin Hai Dong Shunhui Ji Jiajia Li Qi Wang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

The emergence of the metaverse signifies a paradigm shift in Internet technology, offering a comprehensive virtual social platform spanning various domains such as social interaction, gaming, healthcare, and tourism. This new era of the metaverse is facilitated by advancements in next-generation digital technologies including edge computing, artificial intelligence, virtual reality, augmented reality, and blockchain. In the metaverse, the quantity and variety of services requested by users may surpass those in other environments, and existing work cannot be applied to metaverse QoS (Quality of Service) optimization. To address this problem, this paper proposes Meta-PPO, an optimization method for enhancing the QoS of metaverse services using reinforcement learning. Firstly, metaverse services are categorized into virtual scene services and meta-services, providing a comprehensive framework for analysis. Secondly, Meta-PPO, based on the proximal policy optimization algorithm, is introduced to optimize the QoS of metaverse services. This method effectively balances the objectives of minimizing average delay and maximizing resource utilization of mobile devices by making informed offloading decisions for the identified service categories. Simulation results demonstrate the superiority of the proposed method over existing techniques, showcasing its suitability and effectiveness for enhancing the QoS of metaverse service.

关键词： Metaverse Web services Simulation Optimization methods Quality of service Reinforcement learning Mobile handsets Delays Resource management Object recognition

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Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations

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Heliyon 2025年第1期11卷 e41199页

作者： Alkalifah, Badriah Shaheen, Muhammad Tariq Alotibi, Johrah Alsubait, Tahani Alhakami, Hosam Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah Saudi Arabia School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

Middle-Aged and Elderly people today face a variety of health problems as a result of their modern lifestyle, which includes increased work stress, less physical activity, and altered food habits. Because of Complications arising, diabetes has become one of the most frequent, severe, and fatal illnesses around the world. Therefore, inaccurate measurements of blood glucose levels can seriously damage vital organs. Several strategies for long-term glucose prediction have been proposed in the literature. Unfortunately, these methods require the patient to identify their daily activities, which can be error-prone, such as meal intake, insulin injection, and emotional aspects. This paper suggests using continuous glucose monitoring (CGM) of 14733 patients, with three assistance factors to predict blood glucose levels independently of other parameters, hence reducing the burden on the patients. To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. The result indicated that The highest classification accuracy of (92.58%) has been achieved by BDT followed by BSTE (92.04%) and GPR (88.59%). The obtained average of root means square error (MSE) was 1.64, 1.67, 1.69, mg/dL for prediction horizon (PH) respectively to GPR, BSTE, and ANN. © 2024 The Author(s)

关键词： Continuous glucose monitoring (CGM) Diabetic Hyperglycemia Hypoglycemia Machine learning (ML) Regression

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Unleashing Collaborative Potentials: Multifaceted Collaboration among Agents in Multitask Internet of Things Networks

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IEEE Internet of Things Journal 2025年

作者： Liu, Jiadi Li, Ting Wang, Quyuan Wang, Ying Guo, Zhiwei Yu, Keping Chongqing Technology and Business University Chongqing Key Laboratory of Intelligent Perception and Blockchain Technology Chongqing400067 China Southwest University College of Computer and Information Science College of Software Chongqing400710 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

The rapid advancement of Internet of Things (IoT) and multi-agent systems has transformed how complex IoT tasks are managed across domains. While individual edge agents demonstrate proficiency in specialized tasks such as data collection and edge learning, they encounter substantial challenges when confronting complex IoT scenarios that demand diverse skill sets. This paper introduces a novel group formation framework facilitating effective IoT agent collaboration in complex task environments, including smart manufacturing, intelligent transportation, and smart cities. We propose a hybrid competition mechanism that optimizes initial multi-agent cooperation strategies by integrating task requirements, agent capabilities, and system-wide performance metrics. Our approach combines intra-task and inter-task competition to achieve optimal agent-task matching and resource allocation in large-scale IoT networks. Through comprehensive simulations across various IoT scenarios, we demonstrate that our framework substantially enhances task completion efficiency and system performance compared to existing methods. The results confirm our approach’s effectiveness in resource-constrained environments, achieving minimal agent grouping time costs while increasing total task revenue by 30%-42% and resource utilization by 38% compared to baseline heuristic methods. © 2014 IEEE.

关键词： Multi agent systems

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A Stochastic Proximal WMMSE for Ergodic Sum Rate Maximization

A Stochastic Proximal WMMSE for Ergodic Sum Rate Maximizatio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaotong Zhao Xi Wang Juncheng Wang Qingjiang Shi School of Software Engineering Tongji University Shanghai China Department of Computer Science Hong Kong Baptist University Hong Kong China Shenzhen Research Institute of Big Data Shenzhen China

We consider ergodic weighted sum rate (WSR) maximization in a massive multi-user multiple-input multiple-output system. Existing solutions iteratively minimize the average WSR based on all the historical information, and use bisection search to satisfy the power constraint at each iteration, resulting in both high storage burden and high computational complexity. In contrast, we propose an efficient stochastic proximal weighted minimum mean-square error (SPWMMSE) algorithm, which updates the precoder only based on the current single channel realization, without checking the power constraint at each iteration. Furthermore, we propose a novel proximal term to incorporate all the previous channel and surrogate function information in precoder updates. Our analysis shows that SPWMMSE converges to the stationary point of the original ergodic WSR maximization problem almost surely. Simulation results demonstrate the effectiveness of SPWMMSE over the current best alternatives.

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SXSFormer: Spectral Squeeze and Expansion Swin Transformer Network for Hyperspectral Image Classification

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IEEE Transactions on Consumer Electronics 2025年

作者： Ullah, Farhan Ullah, Irfan Khan, Khalil Wang, Quan Algamdi, Shabbab Ali Aldossary, Haya Pau, Giovanni Wuxi University School of Internet of Things Engineering Wuxi 214105 China Wuxi University School of Internet of Things Engineering Foreign Expert Lab Jiangsu Foreign Expert Lab China Chengdu University of Technology School of Computer Science Sichuan China Nazarbayev University Department of Computer Science School of Engineering and Digital Sciences Kazakhstan Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Imam Abdulrahman Bin Faisal University College of Science and Humanities Computer Science Department Jubail 31961 Saudi Arabia Kore University of Enna Italy

Hyperspectral images (HSIs) are highly complex, containing an enhanced spectral dimension compared to conventional images. Deep learning methods are increasingly being applied to process this three-dimensional data for hyperspectral image classification (HSIC). Existing convolutional and transformer-based methods often struggle with capturing fine-grained spectral-spatial dependencies, achieving high accuracy while balancing computational complexity. In order to solve these problems, we propose a novel SXSFormer network for HSIC. This novel approach integrates Squeeze and Expansion (SX) blocks into the Swin Transformer architecture to enhance feature extraction and attention mechanisms in the model. At its core, the novel SX Block recalibrates channel-wise features by temporarily expanding and then compressing channel dimensionality, allowing the model to focus on informative spectral bands and capture complex interdependencies. The SXSFormer, equipped with window-based multi-head self-attention, efficiently captures long-range dependencies with reduced computational complexity by partitioning input data into non-overlapping windows. Additionally, the SX block’s integration within the Swin Transformer enables improved global contextual understanding by selectively weighting the feature maps. We further refine the architecture using various components, such as patch extraction and embedding layers, and a patch merging strategy, ensuring efficient multi-scale feature extraction. Extensive experiments on four benchmark HSI datasets (SA, IP, PU, and KSC) demonstrate that the proposed model achieves remarkable test accuracies of 99.97%, 98.15%, 99.63%, and 98.14%, respectively, outperforming existing state-of-the-art methods. Our proposed approach also shows good generalization ability when applied to new datasets. Overall, our proposed approach represents a promising direction for HSIC. © 1975-2011 IEEE.

关键词： Attention Hyperspectral image classification Squeeze and expansion Swin Transformer SXSFormer

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Deep Learning for 3D Point Cloud Enhancement: A Survey

arXiv

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arXiv 2024年

作者： Quan, Siwen Yu, Junhao Nie, Ziming Wang, Muze Feng, Sijia An, Pei Yang, Jiaqi School of Electronics and Control Engineering Chang’an University Xi’an710064 China School of Software Engineering Northwestern Polytechnical University Xi’an710064 China School of Computer Science Northwestern Polytechnical University Xi’an710064 China School of Electronic Information and Communications Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics Wuhan430074 China

Point cloud data now are popular data representations in a number of three-dimensional (3D) vision research realms. However, due to the limited performance of sensors and sensing noise, the raw data usually suffer from sparsity, noise, and incompleteness. This poses great challenges to down-stream point cloud processing tasks. In recent years, deep-learning-based point cloud enhancement methods, which aim to achieve dense, clean, and complete point clouds from low-quality raw point clouds using deep neural networks, are gaining tremendous research attention. This paper, for the first time to our knowledge, presents a comprehensive survey for deep-learning-based point cloud enhancement methods. It covers three main perspectives for point cloud enhancement, i.e., (1) denoising to achieve clean data;(2) completion to recover unseen data;(3) upsampling to obtain dense data. Our survey presents a new taxonomy for recent state-of-the-art methods and systematic experimental results on standard benchmarks. In addition, we share our insightful observations, thoughts, and inspiring future research directions for point cloud enhancement with deep learning. Copyright © 2024, The Authors. All rights reserved.

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Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC Systems

Denoising Diffusion Model-Based Channel Estimation in IRS-As...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Lan Yi Jiansong Miao Tao Zhang Zhenhui Yuan Jiawen Kang Dusit Niyato Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Engineering University of Warwick UK School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition.

关键词： Wireless communication System performance Noise reduction Channel estimation Signal processing algorithms Estimation Artificial neural networks Integrated sensing and communication Prediction algorithms Diffusion models

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Systematic Literature Review: An Investigation Towards Finding Constructs For Performance Prediction of Students in an Online engineering Course

Systematic Literature Review: An Investigation Towards Findi...

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World engineering Education Forum - Global engineering Deans Council (WEEF-GEDC)

作者： Yamini Joshi Kaushik Mallibhat Vijayalakshmi M. School of Computer Science& Engineering KLE Technological University Hubballi India School of Electronics & Communication Engineering KLE Technological University Hubballi India Centre for Engineering Education Research KLE Technological University Hubballi India

ISBN: (数字)9781665475280

ISBN: (纸本)9781665475297

The use of technology in the field of engineering education has been the most common intervention, especially in the post-pandemic era. Most universities have plans to continue the blended approach to education in the future. This decision has to be an evaluated decision and the analysis of a student's performance serves as an input to take the decision. The other advantage of analysis include early prediction of student performance which can help the instructors to provide timely interventions and help the students to improve their performance. Thus identification of constructs that reflect student engagement and performance in a course delivered in online mode is very essential. This literature review attempts to bring forward the constructs used by various researchers that reflect student engagement and performance. The review is situated in the context of engineering education delivered in online mode. The identification of constructs is significant and helps to build machine learning models for predicting the performance of the students. Standard Systematic Literature Review(SLR) methods defined in literature including citation searching and hand searching were carried out to identify the constructs that have been in use. A list of constructs used by researchers in the literature, that capture students' attention and performance are identified and presented in this review. The identified constructs include students' interaction with content, students' interaction with peers, demographic factors, and the academic records of the student. These validated constructs are proposed to integrate with the Learning Management System(LMS) and use the feature for early prediction of student failures.

关键词： Support vector machines Systematics Statistical analysis Bibliographies Classification algorithms Decision trees engineering education

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