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IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

Intelligent Automation & Soft Computing 2023年第1期35卷 567-580页

作者： M.P.Karthikeyan E.A.Mary Anita Department of Computer Science and Engineering R.M.K.Engineering CollegeChennai601206India Department of Computer Science and Engineering School of Engineering and TechnologyChrist UniversityBengaluru560029India

In recent years,there has been a significant increase in the number of people suffering from eye illnesses,which should be treated as soon as possible in order to avoid *** Fundus images are employed for this purpose,as well as for analysing eye abnormalities and diagnosing eye *** can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic *** that in mind,the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis(IM-EDRD)with multi-level *** model uses Support Vector Machine(SVM)-based classification to separate normal and abnormal fundus images at thefirst *** input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix(GLCM).Furthermore,the presence of Exudate and Diabetic Retinopathy(DR)in fundus images is detected using the Adaptive Neuro Fuzzy Inference System(ANFIS)classifier at the second level of *** detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable ***,the second level processing comprises Morphological Component Analysis(MCA)based image enhancement and object segmentation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose ***,thefindings reveal that the proposed model surpasses existing models in terms of accuracy,time efficiency,and precision rate with the lowest possible error rate.

关键词： Retinal fundus images exudate diabetic retinopathy SVM ANFIS morphological component analysis

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Maximizing Learning Outcomes while Minimizing: Costs: A Cost-Effective Approach to M-Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第11期18卷 4-14页

作者： Suresh Kumar, B. Hemawathi, M.S. Dhanasekaran, P. Kaliappan, Vishnu Kumar Sivaganesan, D. Department of Electronics and Communication Engineering The Kavery Engineering College Tamil Nadu Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibil- ity offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and per- sonalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without com- promising the quality of educational delivery. Ultimately, the findings highlight the transfor- mative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education. © 2024 by the authors of this article.

关键词： Contrastive Learning

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Efficient COVID-19 Prediction by Merging Various Deep Learning Architectures

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Informatica (Slovenia) 2024年第5期48卷 55-62页

作者： Oraibi, Zakariya A. Albasri, Safaa Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Electrical Engineering Department College of Engineering Mustansiriyah University Baghdad Iraq

In late 2019, COVID-19 virus emerged as a dangerous disease that led to millions of fatalities and changed how human beings interact with each other and forced people to wear masks with mandatory lockdown. The ability to diagnose and detect this novel disease can help in isolating the infected patients and curb the spread of the virus. Artificial intelligence techniques including machine learning and deep learning showed huge potential in accurately classifying COVID-19 chest X-ray images. In this paper, we propose to combine the feature maps of multiple powerful CNN models (Xception, VGG-16, VGG-19) using the rule of sum. Each of these models is trained from scratch and tested on the given test images. The dataset was collected from a large public repository of COVID images with three classes: COVID, Normal, and Pneumonia. During experiments, data augmentation is also applied to provide more training samples. Experimental results show that combining multiple models improve the classification accuracy and achieve better performance than standalone models. An accuracy of 97.91% was achieved using a combination of three models which outperforms state-of-the-art techniques. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Coronavirus

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and Technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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Point cloud completion via structured feature maps using a feedback network

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Computational Visual Media 2023年第1期9卷 71-85页

作者： Zejia Su Haibin Huang Chongyang Ma Hui Huang Ruizhen Hu College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Kuaishou Technology China

In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature *** key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric *** accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local *** then integrate FSNet into a coarse-to-fine pipeline for point cloud ***,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point ***,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate *** efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point *** have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.

关键词： 3D point clouds shape completion geometry processing deep learning

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IoMT & ML Model to Detect Stress in Human 2

IoMT & ML Model to Detect Stress in Human

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Nadh Singh, Bhaludra R Ghanta, Sindhura Saishivani, Thokala Vaishnavi, Basingala Sai Sravya, Polishetty Shifa, Shaik Bhoj Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Hyderabad India

ISBN: (纸本)9798350364699

Today's fast-moving surroundings cause stress for heaps. Real-time stress administration may be troublesome, regardless of the allure of negative effects on tangible and insane fitness. ML, IoMT, and AI abilities change how we live and work and solve this question. "A Smart Biomedical Healthcare System to Detect Stress Utilizing IoT, Machine Learning and Artificial Intelligence,"intends to utilize physiological sensors like soul rate, hotness, dampness (sweat) sensors and first verbalizations to detect stress. The submitted approach can path and categories folk into "difficult"and "non-tense"circumstances. Our classification results manifest the advantage of our approach in certain-occasion stress identification. The main aim of the original search out supports a novel procedure of identifying early stress something did surroundings, knowledge atmospheres, and administrative settings to support laborers' and scholars' welfare and someday increase productivity. © 2024 IEEE.

关键词： mHealth

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Privacy preserving using joint 2 K-means clustering and coati optimization algorithm for online social networks

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2715-2724页

作者： Gowda, Nithish Ranjan Venkatesh Venugopal, K.R. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India University Visvesvaraya College of Engineering Bangalore University Bangalore India

Social networks, that have grown so prevalent nowadays, enable users to exchange information and save a significant amount of personal data about them. Although the information that is stored can be useful for enhancing the quality of services provided to users, it also poses a risk to their privacy. This is because social networks contain private information about users. As a result, members of social networks seek to protect the confidentiality of their shared data. The most popular method for protecting confidentiality is anonymizing data, which involves modifying or eliminating some information while trying to preserve as much of the original data as possible. The high level of data loss, similarities attacks, and protection from attribute or link disclosure is problem with existing anonymity approaches. The study proposes a hybrid approach based on K-member k-means clustering and coati optimization algorithm (2KMCOA) as a successful solution for balanced clustering and anonymizing in social networks in order to get over these shortcomings. A K-member K-means clustering algorithm is used to divide the different users into C clusters and each cluster has at least K users, as part of the proposed anonymization procedure. Following clustering, an initial solution is generated that produces the modified data table which should satisfy objective functions and three constraints. The coati optimization algorithm (COA) is then utilized to optimize the primary clusters even more to anonymize the data as well as network graph. The efficiency of the proposed 2KMCOA is compared with other existing anonymity techniques K-means clustering with COA (KMCOA) and K- member K-means clustering without COA (2K) in terms of clustering error, balancing error, distortion rate, objective function, cost function and CPU running time. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anonymization Clusters COA Cost function K-anonymity K-means clustering L-diversity T-closeness

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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Enhancing aquaculture water quality forecasting using novel adaptive multi-channel spatial-temporal graph convolutional network

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International Journal of Agricultural and Biological engineering 2025年第1期18卷 279-291页

作者： Tianqi Xiang Xiangyun Guo Junjie Chi Juan Gao Luwei Zhang College of Computer Science Beijing Information Science&Technology UniversityBeijing 102206China College of Management Science and Engineering Beijing Information Science&Technology UniversityBeijing 102206China College of Engineering China Agricultural UniversityBeijing 100083China

In recent years,aquaculture has developed rapidly,especially in coastal and open ocean *** practice,water quality prediction is of critical ***,traditional water quality prediction models face limitations in handling complex spatiotemporal *** address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality *** aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the *** results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,*** indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.

关键词： water quality prediction aquaculture spatial-temporal graph convolutional network multi-channel adaptive graph construction

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Fruit and Vegetable Classification and Freshness Detection using Machine learning

Fruit and Vegetable Classification and Freshness Detection u...

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MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： Akshi Poorvi Varshney Sandhya Avasthi Kadambri Agarwal Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of CSE ABES Engineering College Ghaziabad India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

Freshness is a key factor in determining a fruit or vegetable’s quality, and it directly influences the physical health and coping provocation of consumers. It ascertains the nutritional value of the specified fruit or vegetable. This paper proposes a well-organized and precise fruit and vegetable classification and freshness detection method. The proposed method employs state-of-the-art deep learning models, specifically convolutional neural networks (CNNs), to analyze images of fruits and vegetables captured through high-resolution cameras. The dataset used for training and evaluation is extensive and diverse, encompassing a wide variety of fruits and vegetables in various conditions. The freshness of a fruit or vegetable can be ascertained by looking at a variety of features, including color, texture, shape, and size. Fresh produce, for instance, is colorful and free of mold or brown spots. Traditional methods for assessing the quality of fruits and vegetables are both time-consuming and error-prone. These methods include inspection and sorting. It is possible to reduce these issues by utilizing automatic detection techniques. In light of this, we proposed an automated fruit-vegetable freshness detection approach that first recognizes whether the image is of a fruit or vegetable, after which it classifies it into one of three freshness categories: rotten, fresh, or mixed. To identify and categorize fruits and vegetables, two deep learning models are employed: You Only Look Once (YOLO) and Visual Geometry Group (VGG-16). The suggested method’s qualitative analysis indicates superior performance on the fruit-vegetable dataset.

关键词： YOLO Deep learning Training Visualization Accuracy Shape Inspection

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