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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Archana, M. Joseph, Ashwin Antony Ajith, Alex V. Koshy, Alan N. Saritha, S. Department of Computer Science Engineering Rajagiri School of Engineering and Technology Kochi India

ISBN: (纸本)9789819765805

Advent of GAN networks has enabled several tasks such as text to face generation easier. It helps in synthesizing several instances of data from the actual data. It gives an idea on new possibilities for existing dataset. The current scenario in literature sees a parade of GAN architectures. The performance of each GAN architecture varies in a wide range of outputs. Hence, there always exists a confusion of the GAN architecture to be chosen for the purpose of the task in question. This paper attempts to make a comparative evaluation of two famous GAN architectures, namely, StyleGAN and DCGAN. The architectures are implemented and the images generated are compared on the basis of the quality of images. It is seen that StyleGAN responds more accurately to features like smile, age, and gender. From the experiments, it is seen that the FID score of StyleGAN has a lower value as compared to that of DCGAN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network architecture

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Petoria-Your Pet's Ultimate Companion

Petoria-Your Pet's Ultimate Companion

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2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Jiju, Anargh Ajayakumar, Adharsh P Paul, Edwin Shajan, John Sukumaran, Sruthy Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350362879

Pet owners often face challenges in ensuring their pet's well-being, such as receiving timely advice on health and behavior and managing complex tasks like medication schedules and grooming appointments. The lack of personalized guidance and inconsistency in sourcing high-quality pet products can contribute to suboptimal choices. Our comprehensive solution aims to simplify and enhance the pet ownership experience by bridging the gaps in advice, management, and product sourcing. By providing personalized guidance, expert advice, and a curated selection of high-quality pet products, our solution creates a more rewarding and stress-free journey for pets and their devoted owners. © 2024 IEEE.

关键词： Artificial intelligence E-commerce Pet care management

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Encryption of medical data based on blockchain and multi-chaotic maps

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Multimedia Tools and Applications 2025年第17期84卷 17871-17892页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In this work, the SHA-256 mapper of the blockchain has been utilized to secure medical data from brute-force attacks. The uniform distribution and lower correlation of the encrypted data are achieved using the multi-chaotic in the encryption approach. Also, the circular shift method is employed in the approach to avoid differential attacks. The encryption approach is applied to secure the patient information, medical signals (ECG and EEG), and medical images (Grayscale, RGB, and Hyperspectral). The performance of the proposed approach has been analyzed for twenty signals and fifty medical images. Performance metrics such as MSE, SNR, PSNR, entropy, histogram, variance, chi-square, NPCR, UACI, and SSIM have been considered to check the resilience of the proposed approach against various types of attacks. The proposed approach satisfies the requirements of performance metrics by achieving higher MSE values with lower SNR and PSNR values for the encrypted data. Furthermore, the proposed methodology attains an entropy approaching 8, signifying robust randomness and resistance against entropy attacks. The SSIM values for encrypted images are close to 0, indicating significant dissimilarity from the original images. The NPCR values surpassing 99.60% and UACI exceeding 33.40% demonstrate resilience against differential attacks in the proposed approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Hybrid blockchain based medical data sharing with the optimized CP-ABE for e-Health systems

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International Journal of Information Technology (Singapore) 2024年第1期16卷 121-130页

作者： Mishra, Anil Kumar Mohapatra, Yogomaya School of Computer Science and Engineering Trident Academy of Technology Odisha Bhubaneswar India Department of Computer Science and Engineering C.V. Raman Global University Odisha Bhubaneswar India

While e-Health systems must prioritise security and privacy, sharing medical data can help improve diagnostic accuracy. Because of its immutability, Blockchain (BC) is now being suggested as an exciting solution for sharing Personal Health Information (PHI). However, sharing PHI must improve patient privacy and security. As a result, this paper describes a secure medical data sharing system. Private BC, Consortium BC (CBC), and optimized cryptography are all included in the proposed system. To ensure patient privacy, PHI is encrypted using optimised ciphertext policy attribute based encryption (CP-ABE). In particular, replicated attributes amendment (RAA) is proposed to improve CP-ABE performance. For each replicated attribute, this RAA achieves a single share value. PHI ciphertexts are stored as blocks in the hospital’s private BC. Furthermore, the PHI keyword is saved to the CBC. By validating the keyword and getting access to the private BC, the doctor may retrieve all patient’s PHI. The article’s findings show that the proposed system achieves lower storage overhead, encryption time, and decryption time. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain (BC) CBC e-Health system Replicated attributes amendment

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Integrating Machine Learning (ML) and Deep Learning (DL) Methods to Produce Reliable Wind Power Predictions 2

Integrating Machine Learning (ML) and Deep Learning (DL) Met...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Birudaraju, Hyma Sailaja, K. Sreedevi, Vaitla Viswanathan, R. Vandaarkuzhali, S. Guru Nanak Institutions Technical Campus Department Computer Science and Engineering Hyderabad India Gurunanak Institutional Technical Campus Department Computer Science and Engineering Hyderabad India Department Computer Science and Engineering Secunderabad India Kongu Engineering College Department of Mathematics Erode India Mailam Engineering College Department of Mechanical Engineering Mailam India

ISBN: (纸本)9798350364699

Wind energy forecasting is crucial for effective implementation due to weather instability and unpredictability. Seamless incorporation into contemporary electricity systems. Various methodologies and strategies have been used and utilized to achieve precise wind power prediction over a wide range of timeframes. Every approach has advantages and disadvantages. This study first examines the current uses of Machine Learning and Deep Learning methods in wind power and wind speed predictions. An extensive examination and classification of the most significant forecasting techniques has been conducted. Simultaneously, thought-provoking conversations are offered to facilitate the examination of all the topics presented. Two algorithms, one from machine learning (ML) and the other from deep learning (DL), have been chosen and used to anticipate wind power. The findings have shown the strong capacity of both algorithms to accurately capture the characteristics of the produced power. Additional analyses of the outcomes about their correctness and prospects are being contemplated. © 2024 IEEE.

关键词： Wind forecasting

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A Brain Tumor Detection and Classification Model on Roboflow dataset Using Inception RestNet50

A Brain Tumor Detection and Classification Model on Roboflow...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Mandloi, Saurabh Zuber, Mohd Gupta, Rajeev Kumar Madhyanchal Professional University Department of Computer Science & Engineering Bhopal462044 India Pandit Deendayal Energy University Department of Computer Science & Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798331508432

The tenth biggest cause of death for both men and women is brain cancer, which claims thousands of lives every day. An early diagnosis and effective treatment of brain tumors may save their life. To identify malignancies of brain tumors, the medical community needs a method that is rapid, automated, effective, and trustworthy. The ideal standard for cancer detection is a biopsy. However, the biopsy procedure is extremely risky and time-consuming. The conventional methods of diagnosing brain cancers, such as "computed tomography (CT") and "magnetic resonance imaging (MRI)", have yielded helpful information regarding the localization and features of tumors. However, due to brain tumor intricacy and variety, medical practitioners find it difficult and time-consuming to evaluate these images. Using the power of machine learning (ML) algorithms, healthcare professionals and researchers have devised automated brain tumor detection systems to overcome these limitations. Recent years have seen a tremendous increase in the success of approaches that rely on Artificial technique (AI) for identifying and classifying tumors. This study develops an ensemble technique for the use of brain MRI to identify and classify brain tumors and their phases (MRI). Tumor detection and classification from an MRI image are performed using a modified Inception ResNet50 pre-trained model and dense layer. The proposed model classifies the input MRI image into four classes namely "glioma tunor (GT), meningioma tumor (MT), no tumor (NT) and pituitary tumor (TP)". As shows in the experiment result that the proposed models for tumor identification and classification achieve accuracies of 99% during training and 98% during testing. © 2024 IEEE.

关键词： Biopsy

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Legal Assistance Redefined: Transforming Legal Access with AI-Powered LegalLink

Legal Assistance Redefined: Transforming Legal Access with A...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Jacob, Susan Jacob, Prisly Mary Cheriyan, Jo Ismail, Safad Saintgits College of Engineering Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350376135

In today's scenario, securing prompt and accurate legal aid remains tough for many due to India's complex legal system, a scarcity of lawyers, high fees, and widespread legal unawareness. To address these issues, we plan to create a comprehensive chatbot designed to provide easy access to valuable legal information for the general public. The LegalLink chatbot leverages the Nomic AI text encoder for encoding user queries, followed by the Retrieval-Augmented Generation (RAG) model for retrieving and integrating external knowledge sources into prompt templates. Additionally, it utilizes the Mixtral-8x7B-Instruct-v0.1 model for generating contextually rich and accurate responses tailored to legal inquiries. This paper outlines the chatbot's architecture, various ML models used, and the methodology adopted. Leveraging cutting-edge artificial intelligence, LegalLink revolutionizes the way people access legal guidance, offering timely, accurate, and user-friendly assistance. © 2024 IEEE.

关键词： Natural language processing systems

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Enhancing Automatic Image Captioning: A Systematic Analysis of Models, Datasets, and Evaluation Metrics 4

Enhancing Automatic Image Captioning: A Systematic Analysis ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ganesan, Kothai Amutha, S. Saketh, G. Venkata Reddy, G. Vishnu Vardhan Chowdary, G. Harinath Sai, J. Ajay Kpr institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331529635

The fusion of computer vision and natural language processing (NLP) has given rise to the interdisciplinary field of automatic image captioning, which aims to generate descriptive text for images without human intervention. This area of research has attracted considerable interest due to its potential uses in improving accessibility, managing content, and enhancing search engine optimization. Over the past years, there has been a drastic increase in the accuracy and detail of captions thanks to recent advances in deep learning, especially with convolutional neural networks (CNNs), long short-term memory (LSTMs), and recurrent neural networks (RNNs). This research examines cutting-edge approaches to automatic image captioning, including attention mechanisms, models based on transformers, and techniques utilizing reinforcement learning. The effectiveness of these methods was evaluated using benchmark datasets, and their advantages and limitations were assessed. Additionally, the obstacles faced in producing contextually appropriate and linguistically precise captions were explored, such as dealing with intricate scenes, uncommon objects, and abstract ideas. The paper concludes by highlighting potential areas for future research, including learning across multiple modalities, captioning across languages, and incorporating common-sense knowledge to improve caption generation. © 2024 IEEE.

关键词： Deep reinforcement learning

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Efficient and Lightweight Framework for Confidential Medical Image Search using Edge Computing 2

Efficient and Lightweight Framework for Confidential Medical...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Rajan, Arun Amaithi Vetriselvi, V. Aishwarya, R. Rajan, Anitha Amaithi Security Research Laboratory Department of Computer Science and Engineering College of Engineering Guindy Anna University Tamil Nadu Chennai600025 India Department of Computer Science and Business Systems Francis Xavier Engineering College Tamil Nadu Tirunelveli627011 India

ISBN: (纸本)9798331504106

In healthcare, medical images are playing a major role in accurate diagnosis. Cloud storage for medical images creates security risks and latency due to some critical vulnerabilities and their distance from data sources. By bringing computation closer to the source, edge computing improves efficiency, still edge servers cannot be trusted blindly, requiring robust security measures. In this article, an efficient and lightweight framework for secure medical image search and retrieval that leverages edge computing is proposed. Here, Medical images are encrypted and similarity-preserving hashcodes are generated locally at the source, which are sent to a trusted master edge server. Before being stored, searchable hashcodes undergo homomorphic encryption, allowing for secure computations on encrypted data. The master edge server generates n shares of the encrypted images by applying a Learning With Errors (LWE) based image sharing scheme and distributing them across nearby edge devices. During retrieval, to ensure resilience against device compromise, m out of n shares are used to reconstruct the image. The master edge server retrieves and reconstructs the top k relevant images in response to the query image. This framework enhances security and efficiency by reducing reliance on cloud storage and enabling secure processing on edge servers, making it ideal for modern healthcare environments. The efficiency and security of the proposed framework are proved theoretically and experimentally. Hence, it proved that the framework is performing better than existing state-of-the-art models. ©2024 IEEE.

关键词： Electronic health record

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Bidirectional Sign Language Translation System 8

Bidirectional Sign Language Translation System

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Mahjan, Aditya Hendre, Pradnya Mankhair, Gauri Satpute, Shubhangi Chaudhari, Deepti A. Nutan College of Engineering and Research Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350391770

Sign language is an essential form of communication for the deaf and hard-of-hearing community. The creation of Bidirectional Sign Language Translation Systems (BSLTS) has drawn a lot of attention as technology has advanced. The first section of the article gives a general review of the difficulties in translating and recognising sign language, highlighting the urgent need for precise and timely solutions. After that, it explores the fundamental ideas of CNN and Sign language, emphasising their advantages. The survey discusses different architectures and techniques used for feature extraction and classification, and it thoroughly evaluates the body of research on CNN's use in sign language recognition. Additionally, the study investigates how the cutting-edge object detection technology YOLO3 improves the spatial comprehension of sign language gestures. This investigation investigates how real-time object identification capabilities of YOLO3 can enhance the accuracy and efficiency of BSLTS, enabling smooth communication between sign language users and non-signers. Sign language is a communication method used by people with speech and hearing impairments to interact with others who are not handicapped. Normal people don't use sign language very often, and it takes a lot of time and effort to understand signs. People with hearing impairments are cut off from the outside world and seldom engage in social interactions due to this communication gap. A system for converting text to sign and sign to text is required to tackle this issue and facilitate communication. For system training, a variety of methods have been employed, including Sign language and Convolutional Neural Networks (CNN). © 2024 IEEE.

关键词： Convolutional neural networks

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