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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis Sun: fundamental parameters techniques: image processing

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Secure Photo Sharing Across Platforms Using Blockchain Technology 4

Secure Photo Sharing Across Platforms Using Blockchain Techn...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nivetha, K. Harini, M. Harshinni, V. Nivetha, N. Tamilselvan, P. M Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India M Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

With the rapid expansion of online social networks (OSNs), photo sharing has become increasingly prevalent, raising significant privacy and security concern. Existing approaches, like centralized access control mechanisms and encryption-based privacy solutions, suffer from inconsistent policy enforcement, unauthorized access risk, and lack of transparency. Moreover, traditional OSN platforms afford limited control over the dissemination of content with respect to counteracting illegal reproductions. In order to address these challenges, Photo Chain is designed. The relevance of this name derives from its concept of using a blockchain mechanism to enable photo sharing in social networks securely and in a controlled manner. The system has smart contracts for dynamic access control, PreHash for photo integrity verification, and Gaussian Blur for the protection of sensitive regions. Dissemination control also provides various ways to prevent unauthorized sharing of photos. Ethereum-based smart contracts maintain an immutable log of transactions to guarantee privacy and transparency through user-defined access policies. A functional prototype has also been developed and tested for security, privacy, and controlled dissemination of pictures through OSNs. This prototype has demonstrated the usefulness of Photo Chain. © 2025 IEEE.

关键词： Differential privacy

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Enhancing Career Development: An AI-Driven Mentorship Platform for Student-Mentor Connections

Enhancing Career Development: An AI-Driven Mentorship Platfo...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Nidhish, M. Prajesh, U.R. Saktheeswar, K. Pillai, Sanjana S. Sri Eshwar College of Engineering Intelligence and Machine Learning Department of Artificial Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331512965

Thunai is an AI-powered mentorship platform that connects users with personalized mentors for career guidance. Key features include automated calendar booking, video communication with privacy-enhancing dynamic emojis, and AI-driven mentor matching aligned with mentee goals. The AI assistant, GURU, supports task management, resource recommendations, and LinkedIn-based evaluations to foster growth. Thunai offers subscription plans and partnerships with educational and mental health organizations, enhancing engagement, leadership development, and networking. Leveraging AI technologies like sentiment analysis and feedback, Thunai improves mentor matching, user satisfaction, and mentee engagement for career and emotional well-being. © 2025 IEEE.

关键词： Professional aspects

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Certain Investigations on Cognitive based Movie Recommendation system using Pairwise Cosine Similarity 9

Certain Investigations on Cognitive based Movie Recommendati...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Sumathi, S. Madhumitha, S. Sushma, R. Suriya, M. Subitsha, M. Swetha, S. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350397376

The Internet serves as a major source in various sectors to communicate and share data. It is being used by every individual across the world for several purposes. The main objective of the recommender system is to provide users with suitable products and information. The movie recommendation system also helps in providing customized decisions to the user, thus improving the experience of customer or user. Content-based filtering manipulates item attributes to suggest other items that are relevant to user likes, this suggestion is made using past actions of customers or users. Cosine Similarity is an evaluation metric that helps in finding how similar the items are regardless of their dimensions. The proposed study, furnish users with suggestions that rest on the prevalence and category of a movie. Content based recommendation system is built using cosine similarity. The movie recommendation system has its separate algorithms for making suggestions by examining users' past data. Many attributes are considered like rating of each movie, name of the director and genre of the film. The system make recommendation by considering one attribute or mixture of attributes. For the proposed system, two different datasets used namely, the credits dataset and the movie dataset. The analysis of data is done with help of Python. © 2023 IEEE.

关键词： Python

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Emergency Medical System using ML 9

Emergency Medical System using ML

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Suriya, M. Dhaanus, K. Farhan Ahamed, Khan A Naveen, V. Ajay, A. Vishnu, M. Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350397376

We all know that automobile industries have achieved their success in creating autonomous driving systems and we can see them exist in our daily life. Have you ever wondered how the safety features would be? So, what will happen if your health goes down to critical condition while travelling in an autonomous vehicle? You will be moving to the fixed destination with the critical condition you are facing. This problem can be solved by our method of approaching the system on how autonomous cars work. In the existing scenario autonomous cars are considered to be the safest way of transportation. But it has its own limitations. Despite all the hype, fully autonomous vehicles are not yet offered to the general public on a commercial basis. In specific geographic locations, people can be travelling in these automobiles on occasion. Nevertheless, mastery these car's technology has proven to be very expensive, and it took a lot longer than intended to go out to more people. © 2023 IEEE.

关键词： Autonomous vehicles

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SAR Ship Detection Based on Multi-Scale Feature Cross Fusion

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Progress in Electromagnetics Research B 2025年 110卷 149-161页

作者： Ren, Xiaozhen Zhou, Peiyuan Liu, Gang School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

Synthetic aperture radar (SAR) ship detection plays a significant role in ocean monitoring. However, the current SAR ship detection methods face limitations in detecting small and dense ships. To address these issues, a novel SAR ship detection method based on multi-scale feature cross-fusion (MFCNet) is proposed in this paper. In the proposed model, a feature extraction network with a spatial fusion attention mechanism (FESNet) is designed to improve the capability of the backbone network in feature extraction. A multiintersection spatial pyramid pooling (MISPP) module is proposed to expand the receptive field and enhance the semantic information. Furthermore, a feature cross-fusion network (FCFNet) is designed to comprehensively integrate features of different scales for enhancing SAR ship detection performance. Experimental results demonstrate that the proposed model achieves high detection performance on the SSDD and HRSID datasets, providing more reliable technical support for ship detection in maritime environments. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Synthetic aperture radar

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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Runways Detection from Sentinel Images using Enhanced Yolov8 Model 1

Runways Detection from Sentinel Images using Enhanced Yolov8...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Vasavi, S. Ippili, Pavani Chirumamilla, Vyshnavi Durga Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350352931

Automation is what future is looking for. Aircrafts and autonomous vision-based vehicles are facing challenging consequences during take-off and landing due to the inefficient detection of runways. The ability to accurately detect airport- runways is critical for enhancing the safety and efficiency of airport-operations. Traditional methods for runway-detection are based on edge-detection or thresholding methods that segment the image by converting pixel intensity thresholds to binary values. Such methods often struggle during undesirable weather conditions and obstructions. The integration of YOLOv8, has shown significant promise in overcoming these challenges. This work enlightens the usage of enhanced YOLOv8 model with better preprocessing techniques for accurate detection of airport- runways. This automated way of detecting the runways will be beneficial during natural calamities, when manual verification is impossible. The proposed model is tested on 880 images and gave the accuracy of 93.2 and false positive rate of 6.32 ensuring the reliability of the model. © 2024 IEEE.

关键词： deep learning region of interest (ROI) runway detection satellite images you only look once (YOLO)

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Spectral Reflectance of Indian Ocean Surface to Detect Blue Whales Using VHRS Images.

Spectral Reflectance of Indian Ocean Surface to Detect Blue ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sanikommu, Vasavi Bandaru, Prudhvi Narayana Sigreddy, Balasai Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798331512965

Protecting blue whales is a core principle of the One-earth Initiative. Accurate detection and classification in oceanic environments are crucial for marine conservation, particularly to avoid mis-classification with other marine objects. Building on our earlier work using Transformer-based models for Detection and tracking of blue whales in the Indian Ocean, this study refines classification accuracy through the application of spectral indices. High-resolution data, obtained from NSIL Bangalore(ISRO) and *** with resolutions of 1.6m and 3m, respectively, is utilized to tackle the complex task of distinguishing blue whales from other marine objects. Statistical Machine learning model are used to validate the detection output accurately by means of whale reflectance data. Mean square error(MSE) is used to check the performance of the prediction. The combined use of these technologies outperformed traditional methods, offering an accuracy of 0.85 and reliability for real-time whale monitoring. © 2025 IEEE.

关键词： Misclassification Spectral Bands Spectral Indices Spectral Reflectance Transformer Models

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Logo Detection Using Deep Learning 5

Logo Detection Using Deep Learning

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kandula, Ashok Reddy Mohammad, Mazid Pokuri, Venkata Siva Sai Akhilesh Sadhu, Madhava Rao Talla, Krishna Raju Seshadri Rao Gudlavalleru Engineering College Department of Artificial Intelligence & Data Science Gudlvalleru India

ISBN: (纸本)9798331530013

Logo detection plays a pivotal role in various applications like brand monitoring, image retrieval, and counterfeit detection. This paper presents a comprehensive comparative study among three state-of-the-art methods: YOLOv9, YOLOv8, and CNN-VGG19. YOLOv9 and YOLOv8 are CNN-based object detection frameworks known for real-time capabilities, while CNN-VGG19 excels at extracting intricate features from images. We evaluated and compared their performance on a diverse logo dataset encompassing varying environmental conditions and logo types. Metrics such as detection accuracy, speed, and robustness to environmental variations were analyzed. Our experimental results demonstrate that all three methods achieved strong capabilities for logo detection tasks, each with distinct advantages. Notably, we achieved an impressive overall accuracy of 94.20%. However, CNN-VGG19 stood out in detecting logos under challenging conditions and extracting fine-grained features. This makes it preferable for applications demanding precise logo recognition, such as counterfeit detection and brand monitoring in cluttered scenes. The findings of this study, including the benchmark of 94.20% accuracy, provide valuable insights for selecting the most suitable logo detection method based on specific application requirements. © 2024 IEEE.

关键词： Deep learning

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