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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Fatima, N. Sabiyath Singh, Ramendra Swathi, Akuthota Choudhary, Amar Ahmed, Azhar Sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson’s Disease Through Drawn Image Patterns 2

Exploring the Efficacy of Modified Transfer Learning in Iden...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Daiyan, Nabil Haque, Md Rakibul Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331508227

Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes. ©2024 IEEE.

关键词： Convolutional neural networks

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Efficient Hyperspectral Data Compression using 3D Convolutional Autoencoder 3

Efficient Hyperspectral Data Compression using 3D Convolutio...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Afrin, Afsana Mamun, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

Hyperspectral image has large size due to abundance of spectral bands preventing efficient storage and transmission in real-time applications. This paper introduces a 3dimensional convolutional autoencoder (CAE) for hyperspectral data compression, utilizing CAE for both compression and subsequent decompression. Our method achieves improved Compression Ratio (CR) values of 982 and 960 on the KSC and Botswana datasets, along with better Peak Signal-to-Noise Ratio (PSNR) results of 55.29 and 52.64, surpassing existing 2D convolutional autoencoder-based HSI compression. Our approach also shows negligible differences in classification accuracy between original image and the reconstructed version of the compressed image, confirming its effectiveness in real-world applications. © 2024 IEEE.

关键词： Convolution

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A Way to Detect Seizure through ML for Enhancing the Epilepsy Way of Management 5

A Way to Detect Seizure through ML for Enhancing the Epileps...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Anirudh Khan, Shinora Chhabra, Rithik Kumar, Rajendra Sharda University School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

This study unveils a groundbreaking system leveraging the capabilities of machine learning to forecast and identify seizures, thereby making a substantial positive impact on the lives of individuals grappling with seizures and alleviating concerns for their caregivers. Demonstrating an exceptional accuracy rate of 98.9%, machine learning techniques are applied to not only categorize seizures but also to predict their onset. This innovative approach facilitates prompt intervention and personalized support, addressing a critical requirement in healthcare. The system employs a comprehensive methodology, utilizing cutting-edge sensors such as electroencephalography (EEG) for monitoring brain activity, electrocardiography (ECG) for tracking heart rate, and thermography for temperature monitoring. This holistic physiological monitoring provides invaluable insights for healthcare professionals, enabling them to tailor treatment plans based on individual needs. However, the true ingenuity lies in the real-time alert system, which promptly notifies designated caregivers through SMS and email upon detecting a potential seizure. This feature not only ensures swift response times but also offers peace of mind and heightened safety for individuals with seizures and their families. Furthermore, the system is meticulously designed to be user-friendly and seamlessly integrates with assistive technologies, ensuring accessibility for users with varying levels of technical expertise. Essentially, this study offers a ground-breaking approach to seizure detection through machine learning, offering a revolutionary fix. that greatly improves the general well-being and safety of those who have seizures and their families. This is a significant development in medical technology with its comprehensive monitoring and real-time alerts serving as a vital lifeline for individuals in need. © 2024 IEEE.

关键词： EEG-based seizure prediction Epileptic seizure detection Machine learning Seizure detection

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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Survey on Personalized Voice Assistant 6

Survey on Personalized Voice Assistant

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Srikrishnan, S. Pushparaj, Ashwin Suresh, Advaith Nair, Akash Krishnan George, Teena Sharika, T.R. Adi Shankara Institute of Engineering and Technology Kalady Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350381689

This survey reviews the current state of personalized voice assistant systems that utilize natural language processing, speech recognition, and AI techniques. It examines methodologies for developing intelligent assistants that can securely access local data, learn preferences, and control IoT devices via voice. The paper analyzes the capabilities, advantages, and limitations of existing voice assistant solutions. It also explores their integration with web technologies and discusses challenges like privacy, speech accuracy, and language modeling constraints. The goal is to provide an overview of personalized voice assistant technologies, their potential impact, and future research directions toward enabling seamless voice-based interactions. © 2024 IEEE.

关键词： Speech recognition

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Data Protection Strategies: Employing IBBE and Attribute-Based CPRE for Secure Data Distribution

Data Protection Strategies: Employing IBBE and Attribute-Bas...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Murali, Erasappa Jerinjose, J. Sharanya, L. Lakshmi Lokeswari, K. Roshini, K. Kalpana, D. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350378092

Worldwide, people and businesses are starting to adopt cloud computing more and more. Although there are still many issues that need to be overcome, cryptography can assist users in public clouds in protecting their data. We present a practical and safe identity-based data group sharing and distribution system for public clouds that enables data owners to simultaneously broadcast encrypted data to multiple recipients. Security issues with public cloud infrastructure are the driving force behind data confidentiality regulations. We employ conditional proxy re-encryption that is both ascribe-based and timed-release, which gives the cloud server a reencryption key. These ways, only disseminators whose characteristics comply with the encrypted data's access policy are able to distribute it to other groups beyond the release period. Through this, we are able to disseminate data groups in a secure and adaptable manner. The distribution of cipher texts can be controlled by data owners with fine-grained and timed-release access control by re-encryption conditions that are connected to characteristics and release time. © 2024 IEEE.

关键词： Ciphertext

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Validata: Enhancing Fake News Detection Using Recursive Feature Selection and Machine Learning in Web-Based Applications

Validata: Enhancing Fake News Detection Using Recursive Feat...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Jain, Mayank Kumar Karneedi, Sonali Pedapati, Venkata Vyjayanthi Gopalani, Dinesh Meena, Yogesh Kumar Manipal University Jaipur Malaviya National Institute of Technology Department of Computer Science and Engineering Rajasthan Jaipur India Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Malaviya National Institute of Technology Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350364866

The emergence of news through social media and other mainstream media, e.g., e-newspapers has made it easier to claim genuinity. It has been considered that fake news that is spread this way could hamper democracy, public health, and social harmony. In many instances, the fast spreading of illegitimate news has resulted in mass hysteria, riots, and widespread panic among the masses of people. And the presence of social media platforms, or the Internet in general has made news easily accessible to everyone. Not only does this connect people with genuine stories, but it has become a haven to easily spread rumors;be it political, or related to health, finance, or technology. Modern problems require modern solutions. So, we have devised the 'Validata' model. The 'Validata' model extracts two hundred unique features by using Recursive Feature Selection (RFS). These features are then input into Machine Learning (ML) classifiers. The experiment was run on three publicly available datasets from Kaggle, Reuters, and McIntire. Our proposed model has yielded higher accuracy as compared to other innovative models. © 2024 IEEE.

关键词： Fake news Machine Learning Recur-sive Feature Selection TF-IDF

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Empowering flood forecasting through meteorological and social media data

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International Journal of Information technology (Singapore) 2024年第6期16卷 3757-3770页

作者： Debata, Siddharth Elango, Sivasankar Computer Science and Engineering National Institute of Technology Tiruchirapalli Tamil Nadu Tiruchirapalli 620015 India

Floods pose diverse challenges for organisms, infrastructure, and the environment, especially with the increasing frequency of natural disasters. Many existing works focused on traditional flood prediction methods, relying on statistical models which struggle in adapting to the dynamic environmental shifts, resulting in inaccurate forecasts and impeding effective disaster response. To address this, we propose a flood prediction paradigm that integrates both statistical models and social media data analysis techniques. By employing ensemble models combining classifiers such as K-Nearest Neighbor (KNN), Logistic Regression, Random Forest for statistical data analysis, alongside advanced natural language processing techniques such as Bidirectional Encoder Representations from Transformers (BERT), Long Short Term Memory (LSTM), and FastText for tweet analysis, our multimodal framework offers a comprehensive solution for flood forecasting. Leveraging rainfall data and user tweets, this study explores the fusion of diverse datasets to enhance predictive accuracy and provide timely insights into flood risk. The proposed system achieved an accuracy of 97%. Experimental results exhibit that the system outperforms other traditional methods. It captured the dynamic interplay between environmental data and human-generated content by amalgamating the predictive capabilities of diverse classifiers with advanced natural language processing techniques. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Feature fusion Flood forecasting Multimodal approach VARMA model

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Desktop Voice Assistant for Elderly People using Feed-forward Neural Network for Intent Recognition 2024

Desktop Voice Assistant for Elderly People using Feed-forwar...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Garg, Kashish Alam, Taj Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

technology has deeply embedded itself in our daily lives, making it easier to carry out tasks. However, the use of technology can be complicated for some like elderly people, cognitively and visually impaired individuals, illiterate, or people with limited education. A proposed desktop voice assistant for elderly people using a feed-forward neural network for intent recognition to simplify complex tasks and make technology more accessible for those who find it difficult. The assistant is a companion to assist, automate, and make it easier for older people to use computers. It aids them in understanding, responding to user needs, and assisting with task management and data retrieval. The Feed-forward Neural Network trains the pre-processed data for voice-to-intent recognition. The proposed and developed desktop assistant does voice recognition with an accuracy of around 78.5 percent, external APIs are used to enhance user experience simplify interactions, and boost productivity. The assistant's configurable personality, which is tailored to the user's preferences, makes it stand out. It provides a high level of interactivity, enabling it to be used quickly and is very flexible regarding individual requirements. © 2024 Copyright held by the owner/author(s).

关键词： Feedforward neural networks

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