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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Nivaashini, M. Mithun, M. Shanmugam, Pabitha Mohammed Rafi, H. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331534950

In the present society where computers and technology are considered as almost indispensable, data theft is a serious concern for organizations since information is often subject to being threatened by hackers. It has however been realized that conventional approaches to guard data by encryption is not fully capable of protecting the information from complex attacks of today. To address these issues, this research provides a modern multi-layer encryption model to enhance security measures in its various layers. The above proposed system primarily identifies the occurrence of such sensitive data within the current databases and then encodes the same data, which further goes through a two-stage encryption process. The first operation to be done is encryption followed by data partition where the data is split into two sections and the two partitions encrypted using two different keys. This means that recombined data is secured, and the problem is that the data can only be decrypted using both keys. That is useful not only in increasing the level of data protection but also in creating an extra barrier in entering into the system. The model employs some of the most superior encryption methods across the range and also has enhanced keys management. Combined with this approach the system addresses the risks components of constructive security and provides a practical solution for real time protection of the data. Besides, this research offers a new technique of data encryption that will rectify the shortcoming of the existing methods of data encryption to ensure the data remains secure irrespective of the evolving and transforming threats in the world wide web. © 2024 IEEE.

关键词： Cybersecurity

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Integrating Machine Learning in Air Quality Prediction Systems 2

Integrating Machine Learning in Air Quality Prediction Syste...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Keerthika, S. Krishnasamy, Lalitha Priyanka, S. Sanjai, R. Sathyaa, S. Pooja, K. Kongu Engineering College Department of Artificial Intelligence TamilNadu Perundurai India Nandha Engineering College Department of Artificial Intelligence and Data Science TamilNadu Perundurai India Department of Artificial Intelligence Kongu Engineering College TamilNadu Perundurai India

ISBN: (纸本)9798331541217

Air pollution is described as the contains of dangerous substances in atmosphere that are emitted by natural sources or human activities and can harm humans, animals, and the environment. Sulfurous anhydride, carbon monoxide, Dioxide of nitrogen, trioxygen, carbon monoxide, and volatile organic compounds are some of the most common air contaminants. The development of tools for tracking and reducing air pollution is greatly supported by this analysis, which is important for both public health and environmental sustainability. To improve model performance, particular attention is paid to feature engineering and data pre-treatment methods. The study employs machine learning models, including Decision Tree Classifier, Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, and SVM, to ascertain the most effective approach for accurate air quality forecasting. The key contribution of this work lies in its ability to provide a practical decision-making framework that can guide authorities in implementing focused interventions and optimizing resource allocation for pollution control. The hybrid approach utilized in this study surpasses traditional methods, offering a more precise tool for air quality forecasting. This model not only addresses the need for improved predictive performance but also acts as a useful tool for public health and environmental agencies seeking to minimize the harmful effects of air pollution. © 2024 IEEE.

关键词： Logistic regression

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Enhancing Disease Diagnosis with Machine Learning Based Symtom Prediction

Enhancing Disease Diagnosis with Machine Learning Based Symt...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Priyanka, S. Keerthika, S. Santhiya, S. Malar, S. Jenesha Malavika, A. Babu, T. Dinesh Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India Kongu Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350376470

Effective symptom-based disease prediction is essential for prompt patient care and management. Investigating the use of machine learning and data mining in the analysis of the rapidly expanding medical data. The new method makes use of decision tree for trait-based subgrouping, Random Forest for increased precision, support vector machine for managing complicated datasets, K-nearest neighbor for similarity-based predictions, and logistic regression for binary categorization. These techniques provide important insights for precise forecasting by revealing hidden patterns in vast amounts of disease-related data. The dataset: symptom and severity shows promise for preventative healthcare by effectively predicting diseases by taking into account a variety of symptoms. © 2024 IEEE.

关键词： Adversarial machine learning

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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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CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

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Computer Modeling in engineering & Sciences 2024年第6期139卷 3209-3231页

作者： Weiya Shi Shaowen Zhang Shiqiang Zhang College of Artificial Intelligence and Big Data Henan University of TechnologyZhengzhou450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural *** these efforts,the detection of small objects in remote sensing remains a formidable *** deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep ***,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection ***,the sensitivity of small objects to the bounding box perturbation further increases the detection *** this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote *** address feature loss in deep layers,we have devised a cross-layer attention fusion *** noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid ***,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)*** efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available *** experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

关键词： Small object detection attention mechanism cross-layer fusion discrete cosine transform

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Improved Brain Tumour Prognosis Using Deep Neural Networks 4

Improved Brain Tumour Prognosis Using Deep Neural Networks

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Vasavi, D. Durgasathvika I, Vindhya VijayChandu, K. Shalini, K. Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Vardhaman College of Engineering Department of Artificial Intelligence and Data science Engineering Hyderabad India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350352931

The segmentation of brain tumor in MRI is an essential step to specify the most likely location, stage and type of a lesion being diagnosed by physician before any medical treatment planning with full-confidence for better prognosis. Traditional manual annotation is expensive, labor-intensive and inconsistent. Deep-learning-oriented image analysis has recently brought new impetus to this quest, as for example in the form of convolutional neural network (CNN) based models which offer some advantages over traditional methods, but may have difficulty in capturing more complex spatial relations. This paper proposes a novel framework for brain tumor segmentation using Dynamic Graph Neural Networks (DGNNs) and Dense Micro-block Difference Feature (DMDF) extraction. Here, DGNNs build and perform operations on graphs which enables them to learn complex spatial as well as contextual features. The DMDF improves feature representation by amplifying regional/micro-block differences on which accurate tumor boundary definition crucially depends. The proposed model integrates DGNNs with a fully convolutional neural network (FCNN), processing multi-modal MRI scans to generate precise segmentation masks. The training pipeline includes robust data preprocessing, normalization, and augmentation techniques for improved generalization. Evaluation on the Brain MRI Images for Brain Tumor Detection dataset demonstrates the effectiveness of this approach, achieving high Dice similarity coefficients and accurate segmentation performance. This method significantly enhances the accuracy and efficiency of brain tumor diagnosis and treatment planning. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Wavelet Convolution Stochastic Analysis Graph Neural Network for Remaining Useful Life Prediction

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IEEE Sensors Journal 2025年第11期25卷 19428-19441页

作者： Hu, Qidi Su, Binyi Kong, Jinzhen Liu, Weipeng Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin300130 China Hebei University of Technology School of Mechanical Engineering Tianjin300130 China

The prediction of the Remaining Useful Life (RUL) of stochastically degrading devices is of critical importance for Prognostics and Health Management (PHM). However, most of the existing approaches overlook the inherent coupling of trends and fluctuations in the degradation process. Meanwhile, these methods are struggling to effectively model the random fluctuations component in the degradation process and may lead to inaccurate prediction. To address these limitations, we propose a modified RUL prediction framework WTSAGNN. The framework extends the spatiotemporal dependencies of multi-sensor signals into the frequency domain. By leveraging the proposed adaptive orthogonal wavelet convolution to more effectively decouple the trend and fluctuation components of the degradation process. Furthermore, we introduce a stochastic differential network (SDN) to replace the traditional multilayer perceptron (MLP). The proposed method enhances the robustness of neural networks against stochastic variations in degradation processes through explicit stochastic modeling. Experimental results demonstrate that WTSAGNN outperforms existing state-of-the-art(SOTA) methods on benchmark datasets, achieving up to a 9.1% improvement in prediction accuracy with only a limited increase in computational overhead. In addition, the proposed modules are plug-and-play, allowing seamless integration with existing data-driven prediction frameworks. © 2025 IEEE.

关键词： Graph neural networks

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Assistance for Visually Impaired individuals using artificial intelligence and Natural Language Processing

Assistance for Visually Impaired individuals using Artificia...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sathvig, Sri K. Bharathi.ln, Priyanka Santhosh.m Ramya, J. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331529833

People with Visual Impairments are vulnerable to various challenges in their everyday lives ranging from Access to Information, Travelling to places, Recognizing their surroundings, Isolated Lifestyle and much more. The Proposed solution aims to address these socially inclusive problems through the use of artificial intelligence, Natural Language Processing, Machine Learning and Computer Vision. The feature index involves using Object Detection models like Yolo v5 for Obstacle avoidance, Integration of Google Map API for Seamless Navigation and NLP based Optical Character Recognition (OCR) for Text Recognition. An average accuracy of 86 percentage was achieved across all models. This solution aims to provide Better Lifestyle, Increased accessibility and Reduced Dependability for the Community of Visually Impaired Individuals. © 2025 IEEE.

关键词： artificial intelligence Computer Vision Machine Learning Natural Language Processing

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Multimodal Content Generation: A Project for AI Creativity 2

Multimodal Content Generation: A Project for AI Creativity

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Geetha, M.P. Hariharan, S. Jeevanprasanth, T. Sanjay, L. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

21st-century interactions involve the consumption of content, whether in the form of text, audio, images, or any other medium. This paper presents a multimodal content generation (MCG) system that seeks to ease the content production process by providing integrated solutions for video, image, audio and text content production among others. Large language models (LLMs) have shown a great promise in enhancing the creativity process in various fields such as media and education where mediums of communication interweave. The system takes into account the current data, situation, user needs, and composes an appropriate and suitable content for them. At the same time, conventional AI models, such as transformers, are combined with multimodal models enhanced with optimization strategies such as transfer learning and fine-tuning. This strategy greatly augments the content generation potential and scalability of the system and thus, is of great importance in the design of systems for the content production process. The quantum- inspired elements of the hybrid model are the ones targeted towards making changes in decision and feature selection within the creative processes. The effectiveness of MCG in this research was compared to other content creation systems, and the results of the research revealed that MCG is more creative, efficient, and satisfying for the users than the existing systems;therefore, it has a great potential for changing the practices of producing content in a multimedia environment. © 2024 IEEE.

关键词： Distribution transformers

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