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Deep Retinal Image Analysis and Classification Using Deer Hunting Optimization-Based Tandem Pulse Coupled Neural Network

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Journal of The Institution of Engineers (India): Series B 2022年第6期103卷 1909-1916页

作者： Vinayaki, V. Desika Kalaiselvi, R. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Gummidipoondi India

Retinal eye diseases lead to vision loss and visual deficiency. Various kinds of human eye diseases are arteriosclerosis, diabetic retinopathy, hypertension, and glaucoma. Diabetic retinopathy is the form of the injured retina which is occurred by diabetes. The improper treatment without proper observations leads to permanent blindness. Therefore, it is necessary to detect the disease at an earlier stage to protect their vision. The main aim of this research is to detect diabetic retinopathy early from the fundus images. The proposed approach detects diabetic retinopathy through four major stages, namely pre-processing, segmentation, feature extraction, and classification. The proposed classifier uses the deep neural network for classifying the diabetic retinopathy (DR) infected images and normal images. Here, the proposed approach utilizes features such as coherence, edge features, shape features, local binary pattern, and Gray Level Co-occurrence Matrix (GLCM) features from the segmented output. The performance measures like sensitivity, specificity, accuracy, precision, F1-measure and processing time are utilized for the estimation of the proposed classifier and compared to other approaches. From the performance evaluation, it is noted that the proposed diabetic retinopathy detection has obtained an accuracy of 99.42%, a sensitivity of 98.84%, and a specificity of 96.49% that outperforming other state-of-art methods. © 2022, The Institution of Engineers (India).

关键词： Local binary pattern

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Comparison of Tribological Behavior of Hard Faced Fe-Based Metallic Composite Coatings Sprayed Using HVOF

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-18页

作者： Soni, Piyush Kumar Prasad, C. Durga Manjunath, H.R. Tiwari, Amit Raju, Vadali Pitchi Athithan, Senthil Deshmukh, Khemraj Kumar, Anjani Department of Mechanical Engineering New Horizon College of Engineering Karnataka Bengaluru India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka Bengaluru India Department of Mechanical Engineering Ramaiah Institute of Technology Karnataka Bengaluru560054 India Department of Mechanical Engineering Suresh Gyan Vihar University Jaipur India Mechanical Engineering Department & amp Principal Indur Institute of Engineering and Technology Siddipet India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Parul University Gujarat Vadodara India Mechanical Engineering Department Government Engineering College Arwal India

Fe-based alloys have recently become well-known for their ability to resist wear and corrosion. Iron, the primary component of alloys, is a cheaper option than alloys made from Co and Ni. There is a need for affordable and easily obtainable coatings based on iron. Coatings in the Fe–Ni–Cr system have demonstrated superior wear resistance compared to other commonly used Fe-based metallic coatings. The Fe-12Ni17Cr2.5Mo2.3Si0.03C based (Metco 41C) metallic coatings were applied onto boiler steel substrates using the High Velocity Oxy-Fuel (HVOF) spraying method. The coatings were examined using X-ray diffraction (XRD), a scanning electron microscope (SEM), and a microhardness tester to determine their density, porosity, microstructure, hardness, and surface roughness. The sliding wear behavior of the substrate and coatings was studied using the pin-on-disk tribometer. Figuring out the wear loss and coefficient of friction. Comparison was made of wear results between the substrate, coatings, and composite coatings. By combining these carbide powders with 30% SiC and WC-12Co particles, the coating’s wear resistance is enhanced in comparison to the Metco 41C coating. In comparison to boiler steel, the Metco 41C + SiC and Metco 41C + WC–Co coatings exhibited a roughly 70% and 40% reduction in volume loss compared with boiler steel respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： HVOF thermal spraying

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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

作者： Muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Machine Learning Techniques to Identify the High-Resolution Radar Image by Supervised Trained Virtual Data 2

Machine Learning Techniques to Identify the High-Resolution ...

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

作者： Devi, Chegondi Durga Ramakrishna, B. Sreeramamurthy, K. Manikanta, R.V. Raju, N Tulasi Polamuri, Subba Rao Swarnandhra College of Engineering & Technology India Koneru Lakshmaiah Education Foundation India Swarnandhra College of Engineering and Technology India Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem India

ISBN: (纸本)9798350364699

Satellite imaging has the potential to aid archaeologists in their work by allowing for the rapid identification of buildings across huge areas of surfaces. While there are a few approaches that have been suggested to attack this problem so far, they aren't very effective in heavily forested regions. Satellite photos acquired with SAS Planet are compatible with the approach suggested in this article. It enhances the discovery of ancient buildings in heavily vegetated environments by combining supervised machine learning with two colour spaces and filters. Accuracy, precision, recall, and F1 score are all averaged out by the approach. In comparison to more conventional methods of manual or semi-automatic structure detection, the proposed solution greatly improves efficiency in the identification of archaeological sites. © 2024 IEEE.

关键词： Archaeological Sites Conventional Methods SAS Planet Satellite Imaging Supervised Machine Learning

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Multi-agent Task Assignment in Unmanned Aerial Vehicle Edge Computing based on Deep Learning Approach 3

Multi-agent Task Assignment in Unmanned Aerial Vehicle Edge ...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Samuel, Amalorpava Mary Rajee Yamuna Devi, M.M. Madhusudhanan, S. Dept. of Electrical and Information Engineering University of Witwatersrand Johannesburg South Africa Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundatio Vijayawada India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331532420

Unmanned aerial vehicles (UAVs) are used as supportive edge computing for sparsely located user equipment on a large scale. In this work, we propose and address a collaborative edge computing system involving multiple UAVs as agents in deep reinforcement learning (DRL) approach due to the restricted computation and energy capabilities of UAVs. The challenge of task offloading is being tackled to reduce the total delays in execution and energy usage by simultaneously planning the paths, assigning computation tasks, and managing communication resources of UAVs. Additionally, Lyapunov optimization is incorporated for the system stability of mobile edge computing assisted by multiple UAVs. Exploring a multi-agent deep reinforcement learning framework to achieve a combined strategy to manage task allocation, and power management. The sum rate results of the evaluation show that our method for task offloading using multi-UAV and multi-EC achieves superior performance with 25 % increase when compared to other optimization methods and ability to reduce cost and delay. © 2024 IEEE.

关键词： Energy utilization

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Enhancing Team Sports Strategy and Performance Using Machine Learning: A Case Study on Soccer 3

Enhancing Team Sports Strategy and Performance Using Machine...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Narayana, K.Venkata Kumari, Nalli Vinaya Ghantasala, G.S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Gupta, Deepika Mlr Institute of Technology Department of Physical Education Hyderabad India Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India Hpsebl Solan India

ISBN: (纸本)9798331518592

This study aims to improve performance in team sports, with a specific focus on football, using machine learning techniques. The research explores different machine learning methods, such as Random Forest, Decision Tree, and Support Vector Machine (SVM), by analyzing a dataset that includes player statistics, match results, and in-game events. The Random Forest model performed the best, with 88% accuracy in predicting match outcomes and identifying key performance indicators. The study highlights how machine learning can help coaches and analysts gain useful insights to enhance team performance and strategy. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimized Image Compression Using Multiple Compressed Sensing Techniques

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SN computer Science 2025年第4期6卷 1-18页

作者： Puttegowda, Kiran Mohan, B.A. Veeraprathap, V. Vijay, C.P. Sudheesh, K.V. Kumar, D. S. Sunil Department of ECE Vidyavardhaka College of Engineering Karnataka Mysuru India Department of ISE B.M.S. Institute of Technology and Management Yelahanka Karnataka Bengaluru India Department of ECE ATME college of Engineering Karnataka Mysuru India Department of Computer Science Engineering (AI & ML) Vidyavardhaka College of Engineering Mysuru Karnataka India Department of Computer Applications East Point College of Higher Education Banglore India

In the digital era, the demand for efficient image compression techniques is ever-increasing due to the exponential growth of visual data. Compressed sensing (CS), a groundbreaking theory in signal processing, offers a promising solution by exploiting the inherent sparsity of images. The proposed work introduces a novel approach to image compression, leveraging compressed sensing techniques to achieve optimized results. Our method works by expressing image data in a higher-order non-raw and non-dense format hence dramatically decreasing the size of the entire data. This significantly reduces storage requirements while preserving image quality. By combining advanced signal processing with innovative image analysis techniques, our approach maintains low computational complexity, enabling real-time applications without sacrificing the fidelity of reconstructed images, even after compression. This makes our approach especially valuable for applications where storage space is limited but image integrity remains crucial. We present experimental results demonstrating the effectiveness of our technique across various image types and compression ratios. Our findings underscore the potential of compressed sensing as a transformative paradigm in the realm of image compression, paving the way for more efficient storage and transmission of visual content in diverse applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Compressed Sensing Compression Compression ratio Images Reconstruction

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A novel workload forecasting model for cloud computing using ALAA-DBN algorithm

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Multimedia Tools and Applications 2025年第13期84卷 11383-11407页

作者： Karimunnisa, Syed Gopu, Arunkumar Rao, T. Prabhakara Ayyadurai, M. Kumar, ESaravana Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP VaddeswaramGuntur-522501 India School of Computer Science and Engineering VIT - AP University Amaravati522237 India Department of CSE Godavari Institute Of Engineering And Technology AP Rajahmundry533296 India Department of ECE SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of CSE The Oxford College of Engineering Bengaluru India

Cloud Computing (CC) generally exhibits varying workload patterns. This autoscaling feature of CC has been extensively managed through predictive cloud resource management approaches. For this reason, a solitary forecasting model is not sufficient to forecast various workload patterns of CC web applications. With the intention of developing a proficient CC model, this paper implements a novel cloud workload forecasting model with the efficiency of DL algorithms like Deep Belief Network (DBN) and Lion Algorithm (LA). Here, the previous workload models are assessed and trained via DBN, and the prediction efficacy is enhanced by optimizing the DBN's hidden neurons through LA. The proposed workload forecast model for the cloud is accomplished by tuning the significant parameters via the Adaptive Lion Algorithm Assisted-DBN (ALAA-DBN)model. Besides, a well-organized resource provisioning pattern is followed to manage the under/over provisioning issues. The proposed CC model also considers providing better Quality of Service (QoS) to the users without violating the Service Level Agreement (SLA). Eventually, the significance of the proposed ALAA-DBN is verified through a comparative analysis with state-of-the-art models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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A Review of Genome to Chaos: Exploring DNA Dynamics in Security

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Multimedia Tools and Applications 2024年 1-28页

作者： Aashiq Banu, S. Rao, L. Koteswara Priya, P. Shanmuga Thanikaiselvan Hemalatha, M. Dhivya, R. Rengarajan, Amirtharajan Department of Systems Engineering Military Technological College Muscat Oman Department of ECE Koneru Lakshmaiah Education Foundation Telangana Hyderabad500043 India Department of Electronics and Communication Engineering Rajalakshmi Engineering College ThandalamChennai602105 India Vellore632 014 India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India School of Electrical & amp Electronics Engineering SASTRA Deemed University Thanjavur613401 India

One of the largest and fastest-growing industries globally is e-healthcare. Digital medical imaging constantly evolves, advancing from standard computed tomography to powerful multi-dimensional imaging. This medical imaging forms patients’ real-time electronic health records (EHR), making it a prime target for hackers. To counter cipher-attacks, it is necessary to secure medical imaging. This research thoroughly analyses the properties of DNA chaos-based digital medical image encryption and reviews various algorithms. Researchers have performed several statistical and differential metrics, conducting simulation experiments to determine which methodology provides the best security outcome. The paper concludes by analysing significant advances in medical image encryption and emphasising future challenges. This paper discusses the importance of securing medical images, the potential breaches, and the security needs, providing a comprehensive review of DNA-based chaos cryptography techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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