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International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Swetha, M.S. Muneshwara, M.S. Madihalli, Amurtha T. Bhardwaj, Anand Ananya Solanki, Vijender Kumar Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Department of Computer Science and Engineering Stanley College of Engineering & Technology for Women Telangana Hyderabad India

ISBN: (纸本)9789819755035

Blockchain technology has garnered significant attention across industries, offering advantages such as enhanced security, privacy, and decentralization. The healthcare sector, with its stringent requirements for data security and privacy, stands to benefit from blockchain’s transformative potential. This paper introduces a novel framework that utilizes Ethereum’s blockchain and IPFS for secure and decentralized management of Electronic Health Records (EHRs). Ethereum provides a trustless platform for executing smart contracts, while IPFS ensures secure data storage with cryptographic identifiers. The framework, called SecureMed, incorporates key components like Ethereum’s programmable blockchain, smart contracts written in Solidity, and IPFS for off-chain scaling. Its architecture consists of user, blockchain, and IPFS layers, granting patients and doctors varying levels of access. Smart contracts govern EHR operations, emphasizing roles and access control. Performance evaluation demonstrates promising results in terms of execution time, throughput, and latency, affirming the system’s suitability for decentralized EHR management. SecureMed addresses security, privacy, and scalability concerns, aligning with the evolving needs of the healthcare industry. As blockchain technology continues to mature, our framework serves as a practical guide for implementing a secure and scalable blockchain-based EHR system, presenting a pathway towards a resilient and efficient healthcare information management infrastructure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain Decentralized Electronic Health Record Smart contract

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Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

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Chinese Journal of Electronics 2024年第6期33卷 1478-1486页

作者： Feiyan JI Heng ZHANG Xiuqiong XING Wenjun LU Lei ZHU Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1and TE9/5,1within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%（i.e., 2.06–5.44 GHz）, in which the stable gain bandwidth in the backfire,-x-direction（θ = 90°, φ = 180°） with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3%（i.e., 3.74–5.44 GHz）. It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

关键词： Fluctuations Azimuth Prototypes Metals Resonance Frequency measurement Broadband antennas Impedance Gain Wideband

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Integration of Explainable Artificial Intelligence (XAI) in the Development of Disease Prediction and Medicine Recommendation System 2

Integration of Explainable Artificial Intelligence (XAI) in ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mathew, Joel Chitra, R. Stephen, Caleb Koshy, Richie Suresh Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350360523

The contemporary healthcare landscape necessitates innovative solutions to improve transparency and understanding in medical decision-making. This paper proposes an advanced medicine recommendation system and a robust disease prediction model integrating explainable AI (XAI). Recognizing the prevalence of misinformation and the urgent need for user-friendly applications, the system empowers users to manage their health proactively. It can be extended beyond common diseases, encompassing rare diseases, and employs XAI algorithms, specifically SHAP and LIME, to enhance transparency. The system incorporates Random Forest Classifier and Decision Tree models, showcasing high accuracy and robustness. The explanation models contribute to user understanding, while performance metrics offer insights into model strengths and generalization abilities. Figures depict SHAP outputs for Decision Tree and Random Forest models, emphasizing transparency in medical predictions. This proposed system addresses critical healthcare challenges, fostering informed decision-making and user trust. © 2024 IEEE.

关键词： Transparency

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Research on pressure control system of evaporation tank based on adaptive fuzzy PID 2

Research on pressure control system of evaporation tank base...

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2nd International Conference on Physics, Photonics, and Optical Engineering, ICPPOE 2023

作者： Xiong, Qi Huang, Wei Wang, Duanlian Wen, Zhiyuan College of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510674684

For the field of fiber optic prefabricated rod manufacturing, raw material evaporation tank pressure control system of nonlinearity, hysteresis and other issues, this paper proposes to combine PID control algorithm and fuzzy control algorithm, designed based on the adaptive fuzzy PID evaporation tank pressure control system. The simulation results in Matlab show that, through the traditional PID and adaptive fuzzy PID control algorithm comparison analysis, obtained adaptive fuzzy PID control system response speed, small overshooting, good stability and other characteristics, can well meet the requirements of the control of evaporation tank pressure system. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Fuzzy control

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Generating Book Suggestions Using a Hybrid Model with Sentiment Analysis

Generating Book Suggestions Using a Hybrid Model with Sentim...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Pagadala, Manvitha Cheekatla, Sreshta Karthik, N. Vani, V. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Recommendation Systems are ubiquitous, whether it is in social media posts, advertisements, or digital book and movie libraries. Many recommender systems suggest items based on the product or its use and do not consider the user's sentiments towards the item or user preferences. This paper employs a hybrid model comprising a mix of popularity and content-based approaches to generate book suggestions along with sentiment analysis. Sentiment analysis of the book descriptions and user feedback enhances the recommendations. This recommendation system also incorporates classification and filtering of explicit books based on the user's age to generate the top 10 recommendations of books. The measurement metrics implemented in testing this proposed model are Precision, Recall, Mean Absolute Error, and Mean Squared Error to test the recommendation system's performance. The proposed system with high precision and low error results in notable additions to existing recommendation systems. © 2024 IEEE.

关键词： classification content-based hybrid model recommendation system sentiment analysis

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Unraveling Dyscalculia: Identifying Mathematical Learning Difficulties in Early Education

Unraveling Dyscalculia: Identifying Mathematical Learning Di...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Mukherjee, Kingshuk Kumar, Reitika Vasishat, Sam Bhargava, Naman Upadhyay, Siddhi Muhuri, Samya Thapar Institute of Engineering and Technology Department of Computer Science & Engineering Patiala India

ISBN: (纸本)9798350319019

This study presents a comprehensive intervention approach designed to address dyscalculia in early education. The strategy employs a comprehensive approach that incorporates colour identification, numerical enumeration, mathematical computation, and verbal communication. The initiative seeks to employ a technology for creating interactive learning modules that successfully engage students in various numerical activities. Student progress is evaluated by the application of fuzzy logic, which allows for a thorough understanding of each student's unique learning trajectory. The adaptive approach tailors the difficulty level of exercises based on the student's performance, ensuring a tailored learning experience. This intervention specifically focuses on primary school students, aiming to provide early support and intervention to those who are either at risk of or have already been diagnosed with dyscalculia. The objective of the study is to provide a robust foundation for forthcoming mathematical abilities. © 2024 IEEE.

关键词： Fuzzy logic

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Deepfake Circumvention Using Machine Learning 4

Deepfake Circumvention Using Machine Learning

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Mathupriya, S. Roopa, D. Simon Jacob, A. Santhosh, G. Arvind, M. Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Deepfake is an AI-based technology that creates videos which are quite genuine but fake, and propagates massive threats to media credibility and public trust. This paper presents a new deepfake detection system that employs the Vision Transformer (ViT) model that can be easily accessed through web interface built with Streamlit. The strategy is aimed at differentiating fake and original video content with a high degree of efficiency, thus offering the efficient tool for real-time content verification. The addition of confidence scores improves interpretability of the system and as such ideal for application in media authenticity and countering fake news. The platform is easy to use with further plans to enhance the features of the platform such as;ability to notify authorities automatically, real-time video processing, ability to work with multiple languages and ways to share the content through Social media, to add to its uses in protecting the content. © 2024 IEEE.

关键词： Machine vision

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Text-Based Emotion Recognition Using Deep Learning 2

Text-Based Emotion Recognition Using Deep Learning

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Chaithra, I.V. Samanvaya, K.J. Department of Computer Science & Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Text-based emotion recognition has garnered significant interest due to its expansive applications in various domains such as customer feedback analysis, mental health support, and human-computer interaction. In this paper, we present a comprehensive study on leveraging deep learning techniques for text- based emotion recognition. Our approach encompasses the entire pipeline, starting from data collection and preprocessing to model training and evaluation. We utilize a diverse dataset of labeled text samples representing different emotions and employ preprocessing techniques such as text normalization, tokenization, and feature extraction using TF-IDF and word embedding's. For model training, we explore deep learning architectures including LSTM, GRU and Transformer, optimizing hyper parameters for accuracy and efficiency. Evaluation metrics like accuracy, precision, recall, and F1- score are essential for measuring the effectiveness of our models. Our experimental results demonstrate the effectiveness of deep learning models in accurately recognizing emotions from text, showcasing promising results across various evaluation scenarios. This research contributes to advancing the field of text-based emotion recognition and provides insights into practical implementations for real-world applications. © 2024 IEEE.

关键词： Contrastive Learning

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A Classification based approach for early stage Prediction and Multi-dimensional analysis of Alzheimer's Disease 7

A Classification based approach for early stage Prediction a...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Anumandla, Sannith Reddy Cherupally, Shivani Reddy, Alluri Supriya Vamshi Krishna, A. Pranitha, K. B V Raju Institute of Technology Computer Science & Engineering Narsapur India

ISBN: (纸本)9798350372816

Alzheimer's disease (AD) presents significant challenges to global healthcare systems due to their rising incidence rates and profound impact on individuals and societies. Early prediction and intervention strategies are urgently needed to address thiue cognitive decline associated with AD, especially with an aging population. This research paper explores the integration of machine learning (ML) techniques to predict AD in its early stages. Various ML algorithms including Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, and Voting classifiers were employed and optimized using the Open Access Series of Imaging Studies (OASIS) dataset. Our models demonstrated commendable performance in AD prediction, achieving high Precision, Recall, Accuracy, and F1-score metrics. Beyond prediction, our research extended to disease progression forecasting, drug discovery, and image analysis, leveraging ML to unravel intricate disease dynamics and enhance diagnosis precision and treatment efficacy. The comprehensive framework outlined in this research facilitates accurate AD prediction and analysis, empowering clinicians to intervene earlier and mitigate the severe social, financial, and economic implications of AD. Adoption of our multifaceted classification scheme could transform healthcare, leading to more efficient disease management, improved patient outcomes, and reduced societal burdens associated with neurodegenerative diseases. © 2024 IEEE.

关键词： Support vector machines

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Fruit Quality Detection Using Convolutional Neural Networks 6th

Fruit Quality Detection Using Convolutional Neural Networks

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Kumar, Chayan Singh, Amit Gupta, Asita Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819778614

Fruit ripening stage is a major determinant of fruit quality. Factors such as size, shape, and color are typically used to assess quality during the selection process. This work addresses the detection of fruit quality by comparing images from the dataset to real-time images. In the context of detecting fruit quality, real-time images refer to images that are captured and processed immediately as they are acquired, without any significant delay. These images are taken in the present moment, reflecting the current state of the fruits being examined. The term real-time implies that there is little to no time lag between capturing the image and its analysis. When comparing images from a dataset to real-time images, the idea is to assess the quality of the fruits based on certain criteria, such as size, shape, and color. The lightweight DenseNet model is proposed for the detection of fruit quality. The process of training and testing the proposed lightweight model involves a combination of sophisticated algorithms, datasets, and validation techniques. We first apply data preprocessing and then split the data into its training and testing datasets. The training of the CNN model will take place in its three layers, which are the convolutional layer, the maxpool layer, and the dropout layer. We then apply the ReLU activation function to start the testing of the model. The results show that the efficacy of the proposed lightweight DenseNet model has achieved an accuracy of 99.48% over an 80:20 ratio on an open, freely available dataset (Fruit Dataset. 2023. Last Accessed: 10 May 2023. Available at: https://***/cvstock-932a9-h58gl/***). In addition, the proposed model is also a promising approach for fruit quality detection and has the potential to be used in a variety of applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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