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Optimized Image Compression Using Multiple Compressed Sensing Techniques

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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Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in Cloud Environment

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 217-221页

作者： Gupta, Rishabh Saxena, Deepika Gupta, Ishu Singh, Ashutosh Kumar National Institute of Technology Kurukshetra Department of Computer Applications Kurukshetra136119 India National Sun Yat-sen University Cloud Computing Research Center Department of Computer Science and Engineering Kaohsiung804 Taiwan

This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model's privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively. © 2019 IEEE.

关键词： Deep neural networks

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MangoSpot: A Hybrid CNN-SVM Model for Multi-Classification of Mango Leaf Spot Disease Based on Seriousness Levels 3

MangoSpot: A Hybrid CNN-SVM Model for Multi-Classification o...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Baresary, Devdutt Saini, Anuradha Sharma, Ravi Kumar Bordoloi, Dibyahash Sharma, Rishabh Kukreja, Vinay Lovely Professional University Depatment of Computer Science and Engineering Punjab Phagwara India Chandigarh Group of Colleges Department of Computer Applications Landran India M.M Institute of Computer Technology & Business Management Mullana Haryana Ambala India Graphic Era Hill University Computer Science and Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350338607

The devastating crop losses and economic damage caused by mango leaf spot disease pose a danger to the global mango industry. To classify mango leaf spot disease seriousness across six categories, we present a hybrid deep learning (DL) model that combines Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The model is trained and tested using a dataset of 20,000 photos of diseased mango leaves gathered from a variety of sources, including internet repositories and field surveys in mango plantations. In the suggested hybrid model, a CNN is utilized for the feature extraction process, while an SVM is utilized for the classification process. High-level features from the input pictures are retrieved by the CNN component, and the SVM component is then trained to classify the images based on those features. On the assessment set, the proposed model demonstrated its generalizability and robustness by attaining an overall accuracy of 95.68%. These findings validate the efficacy of the proposed model in detecting and categorizing photos of mango leaves affected by various diseases. High accuracy was also achieved on an independent dataset, demonstrating the model's generalizability and resilience. The suggested approach may help farmers and agronomists detect and track the seriousness of mango leaf spot disease, which would lessen crop losses and boost crop output and quality. This research makes a difference in agriculture and plant pathology by giving a reliable method for keeping mango crops healthy. The suggested hybrid DL model provides a framework for creating dependable and practical tools for disease diagnosis and management in agriculture. It shows promise for the multi-classification of mango leaf spot disease based on six degrees of seriousness. © 2023 IEEE.

关键词： Convolutional neural networks

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Supply Chain Strategies Supported by Blockchain Technology: Open Problems and Challenges

Supply Chain Strategies Supported by Blockchain Technology: ...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Rana, Sumit Kumar Rana, Arun Kumar Naz, Sardar M N Islam Singh, Ashutosh Kumar Bhushan, Bharat Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Panipat India Galgotia College of Engineering Knowledge Park I Department of Computer Science Engineering Uttar Pradesh Greater Noida201310 India ISILC Victoria University Melbourne Australia Kurukshetra University NIT Department of Computer Applications Kurukshetra India Sharda Universi Knowledge Park III Department of Computer Science Engineering Greater Noida201310 India

ISBN: (纸本)9798350396485

The most prevalent issue in the supply chain ecosystem is introducing transparency to the system. Another issue is fostering trust among various stakeholders to support successful process execution. Depending on the domain and the stakeholders, the supply chain process may change. Thus, solving the issue is challenging. Blockchain technology, sometimes referred to as distributed ledger technology, is beneficial in many ways in a variety of industries, including finance and healthcare. Blockchain based model can resolve the issues of traditional supply chain ecosystem. It creates a decentralized and transparent system where every decision is taken with the help of consensus of the associated stakeholders. This supports an immutable ledger so unauthorized modification is not supported. In this article, we address the issues that the supply chain ecosystem faces as well as the benefits of blockchain technology in resolving these issues. As well as some of the remaining challenges and opportunities, other aspects of blockchain technology are explored that will enable it flourish in future supply chains. © 2023 IEEE.

关键词： Supply chains

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Cloud-based Data Analytics for Automated Coastal Cleanup Robots with Convolutional Neural Network 8

Cloud-based Data Analytics for Automated Coastal Cleanup Rob...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Babu, A. Ramesh Basavaraddi, Chethan Chandra S. Ramesh, S. Mouleeswaran, S.K. Sahoo, Santosh Kumar Solainayagi, P. Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India R&D Don Bosco Institute of Technology Bangalore Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Chennai India Vit University Department of Engineering and Technology Madhya Pradesh Bhopal India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Bangalore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

The growing danger of coastal pollution requires novel, automated remediation methods. This paper proposes combining autonomous robots with sophisticated sensor technologies and a Convolutional Neural Network (CNN)-based data analytics system to clean up coastal areas. The robots autonomously cruise coastal settings, discover and categories marine waste using onboard sensors, and send data to a cloud-based infrastructure for analysis. The proposed method relies on CNN, a deep learning model known for image recognition. For reliable identification and classification, CNN is trained on a broad collection of marine waste images. The cloud-based data analytics platform receives, processes, and stores cleaning robot data. It handles massive datasets effectively using cloud computing scalability and processing power. The technology uses real-time cloud image processing to identify garbage and enable robots to adjust their cleaning tactics. The data analytics platform generates detailed reports and visualizations to analyze cleaning efficiency and environmental effect. It advances automated coastal cleaning technology by integrating advanced robotics, sensor technologies, and deep learning. The proposed approach improves coastal cleaning speed, accuracy, and flexibility to protect marine habitats and biodiversity. © 2024 IEEE.

关键词： Cloud-based Infrastructure Environmental Monitoring Image Recognition Real-time Processing Sensor Technologies

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AI-Driven Threat Detection: Implementing Multi-Layer Security Networks in Cloud Environments

AI-Driven Threat Detection: Implementing Multi-Layer Securit...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Surya, S. Santhakumar, D. Tyagi, Anuj Onapakala, Kiran Thurai Raaj, V.B. Krishna Kumar, N. Chennai600116 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India RingCentral Inc Sr. Site Reliability Eng United States PacificSource Health Plans PortlandOR United States Madanapalle Institute of Technology & Science Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Guru Nanak University University Institute of Computer Science and Applications Department of Csa Telangana Hyderabad501506 India

ISBN: (纸本)9798331508685

AI-driven threat detection has become a vital tool for security maintenance as cloud environments become more and more integrated into digital infrastructures. In order to identify and reduce risks in cloud-based systems, this study investigates the deployment of multi-layer security networks utilising cutting-edge machine learning (ML) and deep learning (DL) models. We created a hybrid training strategy that blends historical and synthetic data for improved relevance in contemporary cloud environments by utilising datasets like KDD Cup 99 and CSECIC-IDS2018 in addition to simulated cloud network interactions. ML models such as Random Forest and Logistic Regression were used to create baseline threat detection, while Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks were used to accomplish deeper pattern recognition. The effectiveness of DL models in proactively protecting cloud infrastructures against changing cyber threats was demonstrated by thorough performance evaluations based on accuracy, precision, recall, F1 Score, and AUC-ROC. These evaluations also demonstrated the models' better flexibility in dynamic cloud environments. © 2025 IEEE.

关键词： Cloud security

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Conditional Generative Adversarial Networks for SAR-based Ocean Feature Monitoring 2

Conditional Generative Adversarial Networks for SAR-based Oc...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Gajjar, Pranshav Mehta, Naishadh Patel, Malay Shah, Pooja Das, Anup College of Engineering and Computing George Mason University United States Department of Computer Science and Engineering Institute of Technology Nirma University India Pandit Deendayal Energy University India Space Applications Centre Indian Space Research Organisation India

Ocean surface monitoring, detection of oil spills, and accurate maritime vehicle detection is an important set of problem statements associated with ocean pollution monitoring. This paper proposes a novel study on Pix2Pix or generative adversarial networks for the aforementioned task. A comparative analysis is presented on various convolutional network-based encoders for semantic segmentation. A publicly available Synthetic Aperture Radar (SAR) dataset from the Sentinel-1 C-band sensor is used, constituting a total of 1112 images for the task of multiclass segmentation. Each integrant image pixel is classified into five classes, Oil Spills, Ships, Look-Alikes, Land Surface, and Ocean Surface. Deep architectures like U-Nets, SegNets, LinkNets, and FCNs are thoroughly assessed to obtain a practical analysis of this approach. Through a systematic exploration of hyperparameters, we identified the best-performing encoder configuration, achieving impressive results with a weighted Mean IoU of 0.898 and an F1-score of 0.931. These findings hold great promise for the advancement of solutions in ocean pollution monitoring, contributing to more accurate and efficient detection and mitigation efforts. respectively. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Enhancing Road Safety Through computer Vision: A Comprehensive Approach Using Associative Rule Mining

Enhancing Road Safety Through Computer Vision: A Comprehensi...

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2024 International Conference on computer, Electronics, Electrical engineering and their applications, IC2E3 2024

作者： Shoran, Preety Yadav, Meenakshi Indu Saxena, Esha Gandhi, Parul School of Sciences Christ University Delhi NCR India Galgotia College of Engineering and Technology Department of Information Technology Gr. Noida India Guru Tegh Bahadur Institute of Technology Department of Computer Science and Engineering Delhi India Manav Rachna International Institute of Research and Studies Department of Computer Applications Faridabad India

ISBN: (纸本)9798350388534

Ensuring road safety is of utmost importance in contemporary urban settings, requiring creative strategies to prevent accidents and efficiently manage traffic. In this paper, we present a solution that utilizes computer vision techniques, with a specific emphasis on using associative rule mining. Our approach combines face tracking and vehicle detection using associative rule mining (ARM) to gain a comprehensive picture of traffic dynamics and improve road safety. The face tracking component enables the detection of distracted drivers or pedestrians, providing valuable information about potential safety risks. Our research adds to the expanding knowledge in the field of intelligent transportation systems by introducing a new framework that tackles the complex difficulties of road safety. This framework combines face tracking, vehicle identification, and associative rule mining to achieve its objectives. The proposed method shows potential for developing road networks that are both safer and more efficient in the age of smart cities. © 2024 IEEE.

关键词： Intelligent systems

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Quantum-Inspired Neural Network For Enhanced Image Recognition

Quantum-Inspired Neural Network For Enhanced Image Recogniti...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Michael, Raj. T.F. Rmesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. Sujatha, D. Sclas Simats Department of Computer Applications Tamilnadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India St.Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350394580

The field of quantum computing has seen significant progress in recent times, leading to the emergence of innovative methodologies across several areas, particularly in machine learning and neural networks. This research study introduces a quantum-inspired neural network (QINN) that has been particularly developed to improve image recognition capabilities. In contrast to conventional neural networks, the Quantum-inspired Neural Network (QINN) utilizes concepts derived from quantum mechanics, including superposition and entanglement, to process and encode information effectively. The paradigm we propose incorporates quantum bits (qubits) as computing units instead of traditional binary nodes, simultaneously enabling the representation of many states. Moreover, the use of the intrinsic parallelism of quantum mechanics is harnessed to provide expedited and more effective data processing, particularly within the concealed levels of the network. The researchers ran benchmark tests using well-recognized image datasets, including CIFAR-10 and ImageNet. The study's findings indicated that QINN displayed superior accuracy rates compared to traditional methods and revealed faster convergence throughout the training process. Additionally, our quantum-inspired methodology demonstrated significant efficacy in analyzing pictures containing nuanced characteristics, highlighting its potential in practical scenarios where precise identification is of utmost importance. In summary, integrating quantum principles with neural network topologies has the potential for groundbreaking advancement in image recognition skills. QINN is a groundbreaking advancement in this domain, emphasizing the unexplored capacity to use quantum phenomena for sophisticated machine learning objectives. Potential areas for future study are the expansion of network scalability to accommodate bigger datasets, exploration of security measures that are resilient against quantum threats, and investigating hybrid mo

关键词： image recognition neural network Quantum-inspired Qubits

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Objective Full-Reference Image Assessment Metrics for Estimating the Quality of Remote Sensing Images 2

Objective Full-Reference Image Assessment Metrics for Estima...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Maruthi, R. Anusha, P. Sankar, P. Thiyagaragan, K. Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India Periyar Maniammai Institute of Science and Technology Department of Software Engineering Thanjavur India Hindustan Institute of Technology and Science Department of Electronics and Communication Engg Tamil Nadu Chennai India

ISBN: (纸本)9798350397635

Image Quality (IQ) assessment is a very complex task and it is extremely important to evaluate the images with the metrics. The metrics applied can be a full reference, partial reference or no-reference metric and it depends on the application and availability of the ground truth. Most of the IQ metrics are developed by considering the Visual System (VS) of humans. The assessment methods studied in this paper focuses on some of the Full-Reference (FR) measures and it is used to estimate the remote sensing noisy images. The effectiveness of the measures demonstrates a considerable outcome and demonstrates how well the noisy remote sensing images are being quantified. © 2023 IEEE.

关键词： Image quality

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