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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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Fog Restoration in Hazy Images using Deep Transfer Learning

Fog Restoration in Hazy Images using Deep Transfer Learning

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Sharma, Viboo Singh, Pranita Sachi, Savya Jain, Arpit Alam, Md Mohtab GLA University Department of Computer Engineering and Applications U.P. Mathura India Hindustan College of Science and Technology Department of IT U.P. Mathura India Roorkee Institute of Technology Department of Computer Science and Engineering U.K. Roorkee India L N Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Eklavya University Department of Computer Science & Engineering M.P. Damoh India

ISBN: (纸本)9798350394412

Issues like low vision and the distortion of spectral and spatial information in photographs can be brought on by unfavorable atmospheric circumstances including smog, rain, overcast sky, fog, and smoke. This piece looks at images taken in a hazy environment. While de-smogging requires veil and transmission map data, optical imaging systems are limited to capturing smoggy images. To restore blurry photographs, effective optical information prediction is necessary. Research has shown that optical information anticipated by the dark channel prior technique frequently produces subpar images in photographs with areas of higher brightness, noticeable pollution gradients, or textured information. In order to estimate optical information, this study makes use of Deep Transfer Learning (DTL) and Optimized Gradient Prior Prediction (OGPP). We gathered benchmark images of both smoggy and smog-free environments in order to train the DTL model. The OGPP technique uses the smog gradient, which the DTL model is taught to anticipate, to recover pictures free of pollution. A comparison analysis shows that when it comes to recovering hazy photos, the OGPP restoration model performs better than other approaches. © 2024 IEEE.

关键词： Restoration

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Innovations in Mechanical Drawing and computer Graphics: Enhancing Precision and Visualization in engineering Design 4

Innovations in Mechanical Drawing and Computer Graphics: Enh...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Goyal, Himanshu Rai Hussein, Layth Bansal, Saloni Sharma, Vishal Harikrishna, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

This study examines the significant advancements in computer graphics and mechanical drawing, with an emphasis on how these developments have revolutionised engineering design precision and visualisation. For engineers and designers, integrating cutting-edge computer-aided design (CAD) tools and visualisation technologies has become crucial as sectors continue to adapt to issues that are ever more complex. The study focusses on significant developments that improve design accuracy and enable detailed visual representations of engineering concepts, including augmented reality (AR), simulation methods, and 3D modelling. Professionals can streamline the engineering workflow by using CAD software to produce complex drawings that are both exact and easily customisable. Before physical prototyping takes place, decision-making based on examination of the product's performance under different conditions is made possible by the integration of simulation modelling. Furthermore, by offering immersive experiences that let stakeholders see and interact with 3D models in actual settings, augmented reality applications transform design assessments. The research emphasises the importance of these advances in enhancing engineering teams' cooperation, efficiency, and communication through a thorough examination of them. The ultimate goal of this research is to pinpoint future paths for the integration of computer graphics and mechanical drawing, opening the door for improved engineering design and development processes. © 2024 IEEE.

关键词： Precision engineering

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A Novel Ant Colony Optimization Algorithm for Dynamic Routing in Communication Networks

A Novel Ant Colony Optimization Algorithm for Dynamic Routin...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Al-Farouni, Mohammed Bansal, Saloni Michaelson, Jacob Kumar, Suresh Veena, C.H. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study on the evolving routing terrain of communication networks centers around improving ACO algorithms. A new method, Modified Ant Colony Optimization (MACO) is developed using dynamic pheromone updates according to current network states. The GA, PSO, and traditional ACO narrations on comparative analysis provide further evidence of MACO's superior adaptability of routing decisions in scenarios where the network conditions change quickly. We validate the proposed MACO algorithm in multiple folds using a typical dataset. Output results show a noticeable enhancement in network throughput, reaching extremely high values that are very low in terms of latency. Packet delivery ratios present better results that further point to the efficiency of MACO in providing successful information transfer. These results make MACO a strong candidate to solve the issues of unstable routing in communication networks. © 2024 IEEE.

关键词： Ant colony optimization

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Artificial Intelligence in Cybersecurity: Enhancing Automated Defense Mechanisms to Combat Sophisticated Cyber Threats and Guarantee Digital Resilience 5

Artificial Intelligence in Cybersecurity: Enhancing Automate...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prakash, V.S. Murugesan, P. Poongothai, P. Shivakumar, B.N. Vijayakumar, P. Shriidhar, P.J. Department of Computer Science Bengaluru India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Cmr Institute of Technology Department of Mathematics Bengaluru India Kongu Engineering College Department of Computer Applications Perundurai Erode India Department of Cse Salem India

ISBN: (纸本)9798350376685

The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity approaches, with a growing emphasis on leveraging artificial intelligence (AI) to bolster automated defense mechanisms. This research delves into the integration of AI technologies within cybersecurity frameworks, exploring their potential to enhance digital resilience against sophisticated cyber threats. By examining the current landscape of cyber attacks and the limitations of traditional defense strategies, the study underscores the need for intelligent, adaptive systems capable of learning from vast datasets and responding in real-time to emerging threats. The research focuses on the development and evaluation of AI-driven defense mechanisms, including machine learning algorithms for anomaly detection, predictive analytics for threat forecasting, and natural language processing for cyber threat intelligence. Through a series of controlled experiments and case studies, the effectiveness of these AI-enhanced systems is assessed in terms of their accuracy, speed, and adaptability to new attack vectors. Findings indicate that AI-augmented cybersecurity solutions can significantly improve the detection and mitigation of advanced persistent threats, zero-day exploits, and other complex cyber attacks. Moreover, the research highlights the importance of ethical considerations and the responsible use of AI in cybersecurity to prevent the misuse of powerful AI tools and ensure privacy and fairness. The study concludes with recommendations for the strategic implementation of AI in cybersecurity, emphasizing the need for a holistic approach that combines human expertise with AI capabilities to achieve robust digital resilience. By fostering a collaborative ecosystem between cybersecurity professionals, AI developers, and policymakers, this research aims to contribute to the ongoing effort to safeguard digital infrastructure against the ever-evolving landscape of cyber threats. © 2024 IEE

关键词： Adversarial machine learning

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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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 robust and consistent stack generalized ensemble-learning framework for image segmentation

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Journal of engineering and Applied science 2023年第1期70卷 74页

作者： Faska, Zahra Khrissi, Lahbib Haddouch, Khalid El Akkad, Nabil Laboratory of Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco LIIAN Department of Computer Science FSDM of Fez Sidi Mohamed Ben Abdellah University Fez Morocco

In the present study, we aim to propose an effective and robust ensemble-learning approach with stacked generalization for image segmentation. Initially, the input images are processed for feature extraction and edge detection using the Gabor filter and the Canny algorithms, respectively;our main goal is to determine the most feature descriptions. Subsequently, we applied the stacking generalization technique, which is generally built with two main learning levels. The first level is composed of two algorithms that give good results in the literature, namely: LightGBM (Light Gradient Boosting Machine) and SVM (support vector machine). The second level is the meta-model in which we use a predictor model that takes the base-level predictions to improve the accuracy of the final prediction. In the stacked generalization process, we use the Extreme Gradient Boosting (XGBoost);it takes as input the sub-models’ outputs to better classify each pixel of the image to give the final prediction. Today, several research works exist in the literature using different machine learning algorithms;in fact, instead of trying to find a single efficient and optimal learner, ensemble-based techniques take the advantage of each basic model;they integrate their outputs to obtain a more consistent and reliable learner. The result obtained from the models of individuals and our proposed approach is compared using a set of evaluation measures for image quality such as IoU, DSC, CC, SSIM, SAM, and UQI. The evaluation and a comparison of the results obtained showed more consistent predictions for the proposed model. Thus, we have made a comparison with some recent deep learning-based unsupervised segmentation methods. The evaluation and a comparison of the results obtained showed more coherent predictions for our stacked generalization in terms of precision, robustness, and consistency. © 2023, The Author(s).

关键词： Support vector machines

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Comparative Analysis of Multi Optimization Techniques under Load Variations in Optimal Reactive Power Dispatch 4

Comparative Analysis of Multi Optimization Techniques under ...

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4th IEEE International Conference in Power engineering applications, ICPEA 2024

作者： Salimin, Rahmatul Hidayah Musirin, Ismail Hamid, Zulkiffli Abdul Aminuddin, Norziana Zakaria, Fathiah Senthil Kumar, A.V. Universiti Teknologi MARA School of Electrical Engineering College of Engineering Shah Alam40450 Malaysia Hindusthan College of Arts and Science Department of Research and PG in Computer Applications Coimbatore India

ISBN: (纸本)9798350344578

Power System Compensation Schemes encompass a range of strategies, including the integration of distributed generation (DG), flexible AC transmission system (FACTs) devices, power scheduling, capacitor bank placement, and optimal reactive power dispatch (ORPD). ORPD, a current trend, also involves optimizing transformer tap changer settings (TTCS). However, the efficacy of TTCS optimization is questioned due to its potential impact on transmission line parameters. To avoid over-compensation and under-compensation, these schemes necessitate optimization. Evolutionary Programming (EP) and Artificial Immune System (AIS) are widely employed in power system optimization, yet their susceptibility to getting stuck at local optima limits their effectiveness. This hybrid, termed Integrated Accelerated Clonal Evolutionary Programming (ICAEP), exhibits promising results in optimizing ORPD under load variations. The paper introduces the implementation of the accelerated mutation technique in ICAEP, showcasing exceptional performance compared to standalone EP and AIS methods. ICAEP can be adapted for various optimization problems, marking a significant advancement in addressing challenges associated with power system compensation, particularly in optimizing ORPD under varying loads. © 2024 IEEE.

关键词： Electric load dispatching

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Detecting Objects in Surveillance Videos with Deep Neural Networks for Crime Scene Analysis

Detecting Objects in Surveillance Videos with Deep Neural Ne...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sudha, I. Kirubasri, G. Deivakani, M. Singh, Vijendra Pratap Kumar, T.Rajesh Niranjanamurthy, M. Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications U.P Varanasi India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Department of Ai and Ml Karnataka Bangalore India

ISBN: (纸本)9798350348910

Surveillance systems are critical components of modern security and forensic investigation, and their efficacy is strongly reliant on precise object detection in video recordings. This study looks into the use of deep neural networks to improve object detection in surveillance films for crime scene analysis. This study investigates the capabilities of cutting-edge deep learning architectures in recognizing and classifying objects in various surveillance contexts. The study's findings are extensive, including detection accuracy metrics for numerous item classes such as 'Person,' 'Vehicle,' and 'Suspicious Item.' Precision values range from 0.78 to 0.92, recall values range from 0.82 to 0.90, and F1 scores range from 0.80 to 0.90, demonstrating the models' ability to recognize objects accurately, but with variances among item categories. This study also looks into computational performance, offering information about inference times and GPU utilization. Inference times for ResNet-50 and YOLOv3 are 15 ms and 20 ms, respectively, with GPU use percentages of 75% and 90%. These findings provide useful information for picking models that fulfill real-time processing needs while optimizing computational resources. The research also examines the connection between video resolution, detection speed (up to 30 frames per second), and average detection accuracy. Lower resolutions allow for faster processing, but at the expense of accuracy, whilst higher resolutions provide finer details at the expense of larger computational needs. These trade-offs are critical considerations when building surveillance systems to meet certain operational requirements. © 2023 IEEE.

关键词： Object detection

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