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Stationary wavelet transform and SpinalNet trained light spectrum Tasmanian devil optimization enabled DR detection using fundus images

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Multimedia Tools and Applications 2025年第4期84卷 1597-1626页

作者： Tamboli, Mubin Shoukat Dhumane, Amol Prasad, Rajesh Prasad, Jayashree Rajesh Ranjan, Nihar M. Computer Engineering Pimpri Chinchwad College of Engineering Pradhikaran Maharashtra Pune411044 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Diabetes is a common disease that causes complications in the eyes known as Diabetic Retinopathy (DR). It aids in discovering the DR and tends to be the main cause behind people’s blindness amidst the previous decades. For prevent the complications of DR and to control the level of blood Glucose, the timely and crucial diagnosis is essential. A model is provided for detecting the DR. Here, Light Spectrum Tasmanian Devil Optimization (LSTDO) is utilized with SpinalNet for DR detection. The method undergoes an adaptive median filter and Region of interest mining process to generate a pre-processed image. The Sparse Fuzzy C-Means (FCM) is adapted for segmenting the optic disks followed by feature extraction. Then, the Hough line transformation with Tyler Coye algorithm (HLTCA) is used for blood vessel segmentation. Concurrently, the Stationary Wavelet Transform (SWT) is adapted to input image to overwhelm the deficiency of translation-invariance issue and then the feature mining is done. The feature vector is generated by unifying the outputs of the three-feature extraction phases and is employed to detect DR. The accuracy, sensitivity, and specificity obtained by the LSTDO-based SpinalNet are 91.6%, 89.9% and 92.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Comprehensive Review on Data Visualization Techniques for Real-Time Information 7

A Comprehensive Review on Data Visualization Techniques for ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Choudhary, Vishal Kumar Kumar, Gulshan Suman, Saurabh Kumar, Abhishek Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India

ISBN: (纸本)9798350359299

Research on real-time data visualization methods is necessary to achieve the most accurate and clear representation of information. Creating specific boards and modifying current platforms are two key tasks in performing an appropriate information visualization. An overview of the studies on dashboard platforms, data visualization methods, and accessible strategies is given in this article. To evaluate the effects of the various data visualization approaches on the finished dashboard, a generic and dynamic dashboard based on real-time data was created. Consequently, a preliminary collection of cues, charts, tables, and reports will allow users to interact with the data. produced by the Dashboard itself. Making informed decisions across a variety of businesses requires the capacity to acquire and comprehend real-time information in today's data-driven environment. Real-time data visualization techniques are now essential for turning complex data into insights that can be put to use. This study examines the value of real-time data visualization and offers a summary of strategies and equipment that enable businesses to efficiently use real-time data. © 2024 IEEE.

关键词： Data visualization

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Sufficient learning: mining denser high-quality pixel-level labels for edge detection

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Neural Computing and Applications 2025年第14期37卷 8245-8260页

作者： Chen, Xiaodiao Yang, Wenya Wu, Wen Tao, Xiuting Mao, Xiaoyang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Media and Design Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science and Engineering University of Yamanashi Kofu400-8511 Japan

The success of deep neural networks can largely be attributed to large-scale datasets with accurate annotations. In many practical applications, labels are annotated by multiple annotators, resulting in ambiguous labels. To mitigate the impact of such labels, existing deep edge detectors tend only to exploit sparsely reliable samples with high-confident labels by simple voting mechanism. However, lacking sufficient pixel-level learnable samples would severely degrade detection performance. To combat this issue, this work designs a sufficient learning framework for edge detection (SLED) to sufficiently explore reliable samples for model learning, which consists of three stages: sample selection, label refinement, and retraining. Firstly, we propose a sample selection scheme founded on history prediction, which aims to identify more reliable samples and locate unreliable regions containing potentially reliable samples. Secondly, we design an efficient label refinement strategy based on a graph convolutional network (GCN) to propagate reliable label information to unreliable nodes. This strategy can generate "new training targets" with denser, reliable labels for all images. Finally, we retrain the network with these new targets, obtaining a more robust model. Experimental results on three widely used benchmarks show that the proposed detector outperforms all compared SOTA methods. Our code will be released at https://***/wenya1994/SLED. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Pixels

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Smoke detection in foggy surveillance environment using parallel vision transformer network

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Neural Computing and Applications 2024年第31期36卷 19499-19514页

作者： Chaturvedi, Shubhangi Thakur, Poornima Singh Khanna, Pritee Ojha, Aparajita Computer Science and Engineering Department PDPM Indian Institute of Information Technology Design and Manufacturing Dumna Road Madhya Pradesh Jabalpur482005 India

Recent years have seen an unprecedented increase in fire incidents, resulting in severe damage to forest regions, loss of human and animal lives, and unwarranted displacement of people. Owing to these issues, artificial intelligence-based fire detection systems have been developed. These systems mostly work with camera-based inputs for fire detection and alarm generation. As fire-smoke can be seen from a distance, methods for smoke detection have also been deployed for identifying fire incidents. But detecting smoke in an outdoor environment can be challenging due to weather conditions such as haze, fog, or clouds. These challenges have led to advancements in the field, and various deep learning architectures have been suggested over the years for fire-smoke detection in normal and foggy or cloudy weather conditions. Many existing methods are either computationally expensive and demand high memory, or if they are lightweight, they suffer from lower smoke detection accuracy and high false alarms. In the present paper, a dual-channel vision transformer model ‘SmokeViT’ is proposed for detecting fire-smoke in an outdoor environment. These channels are interlaced with a convolutional neural network to improve the feature learning capability of the model. ‘SmokeViT’ shows a remarkable accuracy of more than 99% and false alarm rate below 0.20% on two publicly available smoke datasets, outperforming six state-of-the-art methods. The model architecture has only 0.4 million parameters and 0.17 giga floating-point operations, making it suitable for deployment on resource-constrained surveillance devices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smoke

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Adaptive Decentralized Federated Learning in LEO Satellite Networks 24

Adaptive Decentralized Federated Learning in LEO Satellite N...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Li, Dong Yan, Zhigang Hao, Yuyu Niyato, Dusit Quek, Tony Q. S. Macau University of Science and Technology School of Computer Science and Engineering Macau China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

ISBN: (纸本)9798350363760

In Federated Learning (FL) for the low Earth orbit (LEO) satellite networks with parameters aggregated by a ground station, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the latency within the FL for the satellite-ground communication, Decentralized Federated Learning (DFL) has appeared as an effective solution. However, considering the heterogeneity of satellites, the problem on how to efficiently leverage the limited resources available to enhance the model performance of DFL has drawn little attention, which motivates this work. Specifically, the proposed solution involves optimizing the number of local training rounds across diverse satellites with varying resource budgets to enhance the DFL performance by utilizing the resource more efficiently. Simulation results show that, the proposed scheme achieves a much better performance than the conventional schemes with fixed rounds of local training. © 2024 IEEE.

关键词： Federated learning

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Prediction of Student Performance in Online Classes During COVID-19 Pandemic 5th

Prediction of Student Performance in Online Classes During C...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Sudharshana, N. Naren, J. Sumathi, D. Chennai India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (纸本)9789819747108

Despite global disruptions caused by the COVID-19 pandemic, education sector adapted and continued to function online. Undergraduate courses happened online. Variations in students’ performance emerged due to differing approaches in incorporating online education into routine life amidst challenging conditions at home and in society during the pandemic. It was necessary to find out whether online education aided academics. A solution based on a machine learning is proposed and implemented in the paper to forecast an individual’s performance using data gathered from a user survey among university students regarding their perceptions of online education. The user survey comprised questions about factors that could affect academic performance. A diverse range of algorithms are meticulously evaluated to identify one that delivers precise predictions of student performance, leading to an effective restructure of educational landscapes to replicate the immersive and engaging experience of in-person classes, fostering a conducive environment for students to wholeheartedly focus on academic pursuits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Leveraging ML Algorithms to Predict IT Job Opportunities and Inform Programme Approval 5th

Leveraging ML Algorithms to Predict IT Job Opportunities and...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Sudharshana, N. Naren, J. Sumathi, D. Chennai India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (纸本)9789819747108

Unemployment is a serious concern around the world. Every year, millions of graduates are without a job. Students enrol in graduate schools without understanding what the job market expects. Upon graduation, the necessary skills to secure employment are often lacking. In such instances, it can be inferred that degree programmes fail to prepare students for job applications and interviews. A framework must be in place to educate graduates about available employment. Hence a job recommender system comes into the picture. Its main objective is to recommend jobs to students based on technical skillset, logical quotient rating, self-learning capability and a variety of other criteria. The system can also serve as feedback for programmes as it gauges the employability of the student passing out. It can also be used by regulatory councils (government organizations like AICTE which regulate and approve technical programmes) to decide whether the programme should be continued based on the number of job-ready graduates produced. The proposed work has employed classifier algorithms (machine learning), for categorizing student placement data based on features like ‘Academic Percentage in Operating Systems’, ‘Percentage in computer Networks’, ‘Hours working per day’, etc. The performance of various classifiers is also analyzed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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ResNet50-Based Deep Learning Model for Lung Disease Detection

ResNet50-Based Deep Learning Model for Lung Disease Detectio...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Hajbe, Janhavi R. Patle, Sneha K. Talvekar, Shreya V. Khonde, Tanvi W. Yesankar, Prajyot R. Faculty of Engineering and Technology Department of Computer Science & Design Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509828

By enabling a highly accurate examination of the chest x-ray, deep learning, for example, is changing the methods of recognizing lung disorders. In order to classify lung diseases, such as bacterial pneumonia, viral pneumonia, TB, and COVID-19, this work describes the use of ResNet50, a kind of CNN and a deep learning technique. We prove the potential of deep learning to discern these diseases from the two conventional approaches and most importantly, the patterns one could implicitly observe. The article also discusses the challenges that deep learning may pose on to the healthcare industry including data privacy, availability of data and the issue of overfitting. The paper links with this part by looking at the future of this area of specialization, which includes;multimodal fusion, operational AI, real-time identification, personalized care, data issues, and concerns, and ethics. But if deep learning is successful in addressing all the above-discussed challenges then it can bring the situation to the real world where the identification of lung diseases happens in a previous phase which in turn results in better treatment procedures and higher patient outcomes. © 2025 IEEE.

关键词： Lung cancer

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A top-down character segmentation approach for Assamese and Telugu handwritten documents

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3275-3287页

作者： Dutta, Prarthana Muppalaneni, Naresh Babu Department of Computer Science and Engineering National Institute of Technology Assam Silchar786003 India Department of Computer Science and Engineering The Assam Kaziranga University Assam Jorhat785006 India Indian Institute of Information Technology Design and Manufacturing Kurnool Andhra Pradesh Kurnool India

Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend, as they primarily understand alphanumeric text. This transformation is achieved through Optical Character Recognition (OCR), a technology that converts scanned image documents into a format that machines can process. A novel top-down character segmentation approach has been proposed in this work, involving multiple stages. Our approach began by isolating lines from handwritten documents and using these lines to segment words and characters. To further enhance the character segmentation, a Raster Scanning object detection technique is employed to isolate individual characters within words. Thus, the character segmentation results are integrated from the results of the vertical projection and raster scanning. Recognizing the significance of advancing digitization of handwritten documents, we have chosen to focus on the regional languages of Assam and Andhra Pradesh due to their historical and cultural importance in India’s linguistic diversity. So, we have collected datasets of handwritten texts in Assamese and Telugu languages due to their unavailability in the desired form. Our approach achieved an average segmentation accuracy of 93.61%, 85.96%, and 88.74% for lines, words, and characters for both languages. The key motivation behind opting for a top-down approach is two-fold: firstly, it enhances the accuracy of character recognition, and secondly, it holds the potential for future use in language/script identification through the utilization of segmented lines and words. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object detection

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Effect of Pesticides on Crop Health Monitoring Based on NDVI Using Machine Learning 3

Effect of Pesticides on Crop Health Monitoring Based on NDVI...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Pawar, Suvarna Badure, Aman Bade, Adikrishna Kolhe, Sai Takwane, Prasad Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518592

The aim of this research paper is to evaluate the crucial impact of pesticides. This control measure is an important agricultural input for improving crop health. However, due to the nature of agriculture, we need developments in machine learning which are urgently required to analyze it. The utilization of pesticides is important in preventing severe damage from pest and disease outbreaks. This work suggests the application of remote sensing finding devices like drones with multispectral imaging, which can be used to obtain Normalized Difference Vegetation Index (NDVI), with the aim of measuring the status of the crops' health as a function of the chlorophyll content of the plants. The model being presented is based on the use of IoT sensors, drone images, and machine learning algorithms to supervise the energy status of the plants in the field. Preliminary imaging data based on NDVI will highlight the images of drones conditions. However, in this research offers solutions to the problem of diverse resolution images that need to be registered in order to create a mosaic. In the end, the results will illustrate how pesticides impact the efficiency of crops, which will help in developing sustainable agriculture solutions that are environmentally friendly. © 2024 IEEE.

关键词： crop health machine learning NDVI pesticides Precision agriculture

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