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Hyper Spectral Spatial Image Segmentation Using machine learning Algorithms 1

Hyper Spectral Spatial Image Segmentation Using Machine Lear...

1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Saravanan, G. Suresh, A. Jagannathan, J. Thanga Mariappan, L. Majeeth, Najeem Dheen Abdul Kumar, Nithish Veltech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamilnadu India

ISBN: (纸本)9798350371185

In recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KDB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KDB model. By implementing the KDB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real ROSIS Hyper Spectral Dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy. © 2023 IEEE.

关键词： Image segmentation

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Modified EfficientNetB3 Deep learning Model to Classify Colour Fundus Images of Eye Diseases 5

Modified EfficientNetB3 Deep Learning Model to Classify Colo...

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5th IEEE International Conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Sharma, Riya Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Kumar Gunjan, Vinit Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India Learning Manipal University Jaipur Dept. of Artificial Intelligence & Machine Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes. © 2023 IEEE.

关键词： Color

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A Secure and Efficient Framework for Storing Medical Data using Blockchain and Cloud Servers

A Secure and Efficient Framework for Storing Medical Data us...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M Lakshmanan G.S. Anandha Mala G. Sivakumar V S Divya Sundar Rupa Kesavan A Ajoe Sweetlin Jeena Department of Artificial Intelligence and Machine Learning Dayananda Sagar University Bangalore Department of Computer Science and Engineering Easwari Engineering College Anna Univeristy Chennai Department of Artificial Intelligence and Machine Learning Gnanamani College of Technology Anna University Saveetha Engineering College Anna Univeristy Chennai Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The digitization of Electronic Health Records (EHRs) has brought a revolution in health care delivery and even practices in reporting key organizational HR processes. However, these advancements have their merits and demerits particularly in regard to data security, the speed of data retrieval, and how data transactions are performed. This paper presents a solution to this problem in the medical world. It presents a unique architecture that connects cloud servers with Blockchain technology for better and secure medical databases. Using Merkle trees to protect health information and advanced heuristic algorithms to optimize operations, the system deals with main issues of transaction time, retrieval rate, and data privacy. The method is tested on a realized medical database and shows that the performance indicators increased substantially. It would seem that Blockchain assures that data cannot be altered once uploaded, while cloud servers provide flexibility to users and allow faster data retrieval. Proposed system improves the transaction time by 30 percent and the retrieval rate by 40 percent compared with traditional methods and supports strategic management of EHR systems.

关键词： Data privacy Databases Scalability Heuristic algorithms Medical services Data retrieval Threat assessment Blockchains Servers Electronic medical records

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An Involuntary Liver Cancer Recognition in Abdominal Liver Imageries with Soft Optimization Methods in Biomedical Application

An Involuntary Liver Cancer Recognition in Abdominal Liver I...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumar, M. Vinodh Kumar Patra, Raj Veena, R. Siva Kumar Reddy, A. Sunil Kumar, V. Inventuriz Labs Pvt Ltd Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

A medical image recovery system can be used for a variety of useful purposes, one of which is the classification of medical images. When numerous different types of medical imaging evidence are accessible, it is essential to have accurate categorization models. Because of the CT paradigm in clinical diagnosis, radiologists are able to properly detect and monitor changes in the body's physiological state. The ability to differentiate between different types of tissues in a CT image based on the various grey rates along each line contributes significantly to the accuracy of a medical diagnosis. The standard practice for diagnosing cancer relies on scientific and histological data, both of which have the potential to lead to incorrect or erroneous findings. The liver, which may be found in the upper abdominal region of the external human form, is responsible for filtering the blood and getting rid of any toxins it discovers in the process. The distinction between the two types of cancer is taught through an artificial neural network in this method. A number of different parameters, including accuracy, area, correlation, entropy, homogeneity, contrast, and the similarity index, are employed to conduct an analysis of the impact. After that, researchers tried to enhance the early identification and treatment of liver cancer by segmenting and classifying liver tumors from CT images. This was done in order to gain a better understanding of the disease. The accuracy of the identification was raised to 99.63 percent thanks to the classifier and optimizer that was proposed. © 2023 IEEE.

关键词： Medical imaging

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machine learning in Healthcare: A Review of Current Applications and Future Trends 2

Machine Learning in Healthcare: A Review of Current Applicat...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Yesankar, Prajyot Puri, Chetan Barahate, Aditya Gote, Pradanywant M. Hajbe, Janhavi Pawar, Adesh Department of Computer Science & Design Maharashtra Wardha442001 India Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

machine learning (ML) has been a main force behind important breakthroughs in patient tracking, personalized medicine, medical tests, and operating efficiency in the healthcare business in recent years. machine learning algorithms provide unique insights into early disease identification, picture analysis, and prediction analytics, improving diagnosis accuracy and treatment results. These programs are able to examine big and complex information. By predicting treatment effectiveness and genetic risks, machine learning (ML) allows personalized medicine by allowing customized care through tailored therapy approaches. Furthermore, preventative health management is backed by ML-driven prediction analytics and online patient tracking, especially for chronic illnesses. Operationally, machine learning increases process efficiency and cuts costs, allowing for data- driven decision-making and individualized patient care. However, there are important ethics issues with machine learning acceptance in the healthcare business as well, such as data protection, computer bias, and legal problems. This study offers a full analysis of the state-of-the-art machine learning applications in healthcare, stressing their revolutionary potential while discussing the ethics issues and hurdles that must be addressed to ensure execution that is both responsible and fair. © 2025 IEEE.

关键词： Personalized medicine

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The Age and Gender Detection using Deep Convolution Neural Network

The Age and Gender Detection using Deep Convolution Neural N...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Chauhan, Shweta Kumar, Rahul Kumar, Bibek Madhav Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Madhya Pradesh Gwalior474005 India GLA University ECE Department Uttar Pradesh Mathura281406 India Symbiosis University of Applied Sciences School of Data Science Madhya Pradesh Indore453112 India

ISBN: (纸本)9798350385199

Automatic age and gender classification has become important researcher since the rise of social platforms and social media. The main advancements in face recognition are the ability to determine a person's age and gender. This paper makes a significant contribution by providing a survey of different face recognition methods for determining age and gender. A deep neural network (DNN) known as a convolutional neural network is frequently used for natural language processing and image recognition and processing. The proposed method significantly enhances three elements: face alignment, face recognition, and face recognition accuracy. All weight vectors of people in the same age group within a given database are averaged together. To predict gender and age, picture classification and regression models have been discussed in this paper respectively. Although training the proposed network to make accurate predictions takes a lot of time, it significantly improves age and gender categorization precision. From the experimental results we compared and analyzed the work to the most recent UTK-Face benchmark for estimating age and gender. © 2024 IEEE.

关键词： Convolutional neural networks

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StaR Maps: Unveiling Uncertainty in Geospatial Relations

StaR Maps: Unveiling Uncertainty in Geospatial Relations

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International Conference on Intelligent Transportation

作者： Benedict Flade Simon Kohaut Julian Eggert Devendra Singh Dhami Kristian Kersting Honda Research Institute Europe GmbH Offenbach Germany Department of Computer Science Artificial Intelligence and Machine Learning Lab TU Darmstadt Darmstadt Germany Department of Mathematics and Computer Science Uncertainty in Artificial Intelligence Group TU Eindhoven Eindhoven MB Netherlands Hessian AI Centre for Cognitive Science German Center for Artificial Intelligence (DFKI)

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncertainties, which are commonly underrepresented by the respective geographic information systems. Hence, it is critical to develop a representation that combines a statistical, probabilistic perspective with the relational nature of geospatial data. Further, such a representation should facilitate an honest view of the data's accuracy and provide an environment for high-level reasoning to obtain novel insights from task-dependent queries. Our work addresses this gap in two ways. First, we present Statistical Relational Maps (StaR Maps) as a representation of uncertain, semantic map data. Second, we demonstrate efficient computation of StaR Maps to scale the approach to wide urban spaces. Through experiments on real-world, crowd-sourced data, we underpin the application and utility of StaR Maps in terms of representing uncertain knowledge and reasoning for complex geospatial information.

关键词： Vocabulary Uncertainty Navigation Semantics Urban areas Stars Probabilistic logic Cognition Geospatial analysis Computational efficiency

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SARA: Structural and Adversarial Representation Alignment for Training-efficient Diffusion Models

arXiv

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arXiv 2025年

作者： Chen, Hesen Wang, Junyan Tan, Zhiyu Li, Hao Fudan University China Shanghai Academy of Artificial Intelligence for Science China Australian Institute for Machine Learning The University of Adelaide Australia

Modern diffusion models encounter a fundamental trade-off between training efficiency and generation quality. While existing representation alignment methods, such as REPA, accelerate convergence through patch-wise alignment, they often fail to capture structural relationships within visual representations and ensure global distribution consistency between pretrained encoders and denoising networks. To address these limitations, we introduce SARA, a hierarchical alignment framework that enforces multi-level representation constraints: (1) patch-wise alignment to preserve local semantic details, (2) autocorrelation matrix alignment to maintain structural consistency within representations, and (3) adversarial distribution alignment to mitigate global representation discrepancies. Unlike previous approaches, SARA explicitly models both intrarepresentation correlations via self-similarity matrices and inter-distribution coherence via adversarial alignment, enabling comprehensive alignment across local and global scales. Experiments on ImageNet-256 show that SARA achieves an FID of 1.36 while converging twice as fast as REPA, surpassing recent state-of-the-art image generation methods. This work establishes a systematic paradigm for optimizing diffusion training through hierarchical representation alignment. © 2025, CC BY.

关键词： Generative adversarial networks

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Harmonizing Precision Agriculture: Augmented Insights into Plant Disease Detection Using Deep learning

Harmonizing Precision Agriculture: Augmented Insights into P...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Chauhan, Shweta Kumar, Rahul Kumar, Bibek MITS College of Engineering Department of Artificial Intelligence and Machine Learning Gwalior474005 India GLA University ECE Department Uttar Pradesh Mathura281406 India School of Data Science Symbiosis University of Applied Sciences Madhya Pradesh Indore453112 India

ISBN: (纸本)9798350364088

This research addresses the critical issue of accurate plant disease identification, recognizing its paramount significance in preserving agricultural yields and safeguarding natural ecosystems. Leveraging the power of deep learning, specifically Convolutional Neural Networks (CNNs), our study focuses on developing a robust image classification model capable of precisely detecting various plant diseases. However, the challenge of limited training data necessitated the implementation of diverse data augmentation techniques to enrich the dataset. The research involves a meticulous exploration of different augmentation strategies, including rotation, flipping, zooming, translation, shearing, and cropping. The acquired dataset comprises 25,000 images categorized into 38 classes, with 20,000 designated for training and 5,000 for validation. The CNN model, with convolutional, pooling, dense, dropout, and flatten layers, exhibits a remarkable accuracy of 93.5% and 95% on the valid and training dataset, reflecting its adeptness in learning intricate patterns. Furthermore, the study reviews recent advancements in plant disease identification, including innovative models like PPLC-Net, Deep CNN, SG2-ADA, and HQIA. These breakthroughs underscore the continuous evolution of techniques to address challenges such as imbalanced datasets, computational demands, and overfitting. The research concludes with recommendations for future exploration, emphasizing the potential refinement of data augmentation techniques, hyperparameter tuning, and broader disease dataset acquisition. The outcomes presented here contribute to the growing body of knowledge in precision agriculture and pave the way for further advancements in plant disease detection methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Makes Better: Privacy-Preserving Medical Image Classification Based on Federated learning

Hybrid Makes Better: Privacy-Preserving Medical Image Classi...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Zhi Chi, Haotian Li, Yonggang Zhang, Yuxuan Jiang, Shunrong Du, Xiaojiang Guizani, Mohsen China University of Mining and Technology School of Computer Science Xuzhou221116 China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351255

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients' private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients' privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget. © 2024 IEEE.

关键词： Federated learning

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