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Emotion Speech Recognition in the Cloud 2

Emotion Speech Recognition in the Cloud

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2nd international conference on Intelligent data Communication technologies and Internet of Things, IDCIoT 2024

作者： Ganesh, Devalla Bhaskar Prasanth, Balisetty Chowdary, Manne Teena Avala, Varsha Sree Gangashetty, Suryakanth V Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350327533

Emotion speech recognition in the cloud has emerged as a transformative technology with the potential to revolutionize various sectors, induding customer service, mental health, and human-computer interaction. This abstract provides an overview of the key concepts and approaches involved in the development and deployment of emotion speech recognition systems in doud-based environments. Emotion speech recognition leverages advances in machine learning, natural language processing, and cloud computing to detect and analyze emotional cues in spoken language. By identifying emotional states, such as happiness, sadness, anger, and more, these systems offer valuable insights for businesses and healthcare providers, enabling them to improve customer experiences and mental health services. © 2024 IEEE.

关键词： Cloud computing Deep learning models Emotion recognition Natural language processing Speech analysis

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Revolutionizing Blood Donor Management using Cloud computing

Revolutionizing Blood Donor Management using Cloud Computing

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2024 international conference on advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Partha Sarathy, E. Ramamurthy, Praveen M. Saxoniel, Ramavath Vijay Kumar, M. Cloudin, S. Ramesh, Akshay Kcg College Of Technology Computer Science And Engineering Tamil Nadu Chennai India JS React Python Lumel Technologies PlanoTX United States

ISBN: (纸本)9798350364828

Revolutionizing Blood Donor Management Using Cloud computing addresses the critical need for an efficient and secure system to manage blood donors and facilitate timely donations in emergencies. Leveraging the capabilities of cloud computing, the system allows hospitals to register, update, and remove donor information through a web application. Donors willing to contribute can register their details, and the cloud-based database ensures real-time synchronization across multiple hospitals in a community cloud setting. The system employs robust security measures to safeguard donor information, per- mitting only authorized hospital personnel to access and manage the data. Notably, a donor's ability to contribute is restricted for three months after a donation to ensure their well-being. The web application includes an admin-controlled hospital registration process, allowing for secure management of hospital details. Through this innovative solution, the project aims to streamline blood donor management, foster collaboration among hospitals, and ultimately contribute to more effective and timely responses in emergency situations. © 2024 IEEE.

关键词： Hospitals

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Integration of Generative AI and Deep Tabular data Learning Architecture for Heart Attack Prediction 3rd

Integration of Generative AI and Deep Tabular Data Learning ...

引用

3rd international conference on Advanced Network technologies and Intelligent computing (ANTIC)

作者： Singh, Priya Kirar, Jyoti Singh Banaras Hindu Univ Varanasi Uttar Pradesh India Jawahar Lal Nehru Univ Delhi India

ISBN: (纸本)9783031640667;9783031640674

Heart attacks, also known as myocardial infarctions, are currently the top cause of death across all age categories. Through early detection, recent advances in healthcare-particularly in artificial intelligence (AI)-have increased the survival rate of heart attack patients. However, an enormous quantity of high-quality data is necessary for meaningful advancement in AI research for heart attacks. Real-world cardiac attack datasets were used to validate the research findings. A detailed analysis showed that the CTGAN model produced synthetic heart attack data superior to expectations. The TabNet architecture also beat all prior deep-learning and machine-learning classifiers in the classification stage. In conclusion, this study shows the potential of using deep generative models to generate high-quality synthetic data for heart attack research. It emphasizes the superior performance of interpretable TabNet over traditional machine-learning methods in categorizing heart attacks.

关键词： CTGAN Synthetic data Tabular data TabNet

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Analysis for Predicting English Premier League Matches Using Clifford-Steerable Convolutional Networks 5

Analysis for Predicting English Premier League Matches Using...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Mohideen AbdulKader, M. Vaithieswaran, S. Advay Rao, Tanuj School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamilnadu Chengalpattu India

ISBN: (纸本)9798350356236

The most widely watched football league globally is the Barclays English Premier League (EPL), the future performance of which attracts attention from both scholars and practitioners in terms of outcome prediction. It is still challenging to provide accurate predictions per match result because of the many attempts tried in the past due to complicated interference of many factors that entice game dynamics. In order to predict the EPL matches, this paper proposed a Cubature Kalman Filter Haar Wavelet Transform Clifford-Steerable Convolutional Neural Network with Mud Ring Algorithm (CKFHWTCSCNNet-MRA). The data was downloaded from Kaggle and the OCRCKF was used for data pre-processing. For feature selection the Q-DHWT was used, which stands for Quantized Discrete Haar Wavelet Transform. CS-CNNs were then applied to predict match results while The Mud Ring Algorithm (MRA) was applied to the analysis of hyperparameters. The rationale and validation of the proposed model is established based on the performance measures employed in predicting wins/loses of the match which yielded an outstanding accuracy, specificity, sensitivity and recall of 99%, F1-Score of 99% and error of 0.25%. © 2025 IEEE.

关键词： Image reconstruction

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Health Monitoring with Smart Pillows 15

Health Monitoring with Smart Pillows

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Shijitha, R. Vijayashree, B. Anupriya, B. Kavya, S.P. Suvathi, S. Avinshilingam Institute for Home Science and Higher Education for Women Coimbatore India

ISBN: (纸本)9798331300579

An individual's subjective health is intricately linked to the quality of their sleep. Monitoring vital signs such as body temperature and perspiration during sleep is critical for both clinical diagnosis and sleep assessment. This project utilizes various sensors, including LM35 temperature sensors, accelerometer, pulse sensor, proximity sensor, Arduino UNO, Node MCU, buzzer, and LED. Embedded sensors track the patient's movements, temperature, pulse rate, Spo2, and sleep patterns throughout the night. Collected data, including sleep duration, interruptions, and quality, is sent to a mobile app for analysis. The Internet of Things (IoT) facilitates automatic oxygen supply activation if needed. IoT-enabled pillows adjust surface and room temperature, ensuring a comfortable sleep environment based on patient preferences. Real-time data on temperature, oxygen level, breathing and pulse rates, and sleeping patterns are displayed on a mobile or PC interface. Consequently, this smart pillow serves as an efficient patient monitoring system, offering portability and ease of use. © Grenze Scientific Society, 2024.

关键词： Proximity sensors

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Securing Medical data Records using Blockchain in a Cloud computing Environment 3

Securing Medical Data Records using Blockchain in a Cloud Co...

引用

3rd international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2023

作者： Hebballi, Anandarshan K Bharath, J. Agarwal, Akansha Challa, Manoj CMR Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665494007

In the current age of Information technology and Smart systems, where data is the new oil, it has become highly crucial to effectively store and share any sort of digital data. In recent years the high volumes of Medical Health data have been under the data breaches by the third parties in the current centralized storage systems, raising concerns for patient data privacy and also the inconvenience for patients to access their own Health records. Blockchain technology with the integration of cloud storage for medical records provides enhanced security of data over the network through the immutable nature of blocks in blockchain and an effective decentralized storage system can be achieved by cloud computing. The model discussed in this paper gives the architecture and implementation of Medical Health Records systems secured through Blockchain and cloud technologies with various tools used. The working prototype which has been implemented is discussed with the demonstration of accessing and sharing of medical data by the patients with the doctors and insurance agencies. The proposed system meets the requirements of patient data privacy by giving greater control of medical data to patients. © 2023 IEEE.

关键词： Blockchain

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Ultrasound-based Breast Cancer Detection using CNN: A Transfer Learning Approach 15

Ultrasound-based Breast Cancer Detection using CNN: A Transf...

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Patil, Chandrashekhar H. Naik, Sachin Patil, Kalpesh Sathe, Saloni Bhingradiya, Meet Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Application Pune India

ISBN: (纸本)9798331300579

One of the most important tasks in medical diagnostics is the identification of breast cancer using ultrasound imaging. In this work, ultrasound pictures are classified using a Convolutional Neural Network (CNN) technique into three classes: benign (non-hazardous), malignant (dangerous), and normal (no traces). The collection of data includes 1587 photos, of which 26.53% are malignant, 56.71% benign, and 16.76% normal cases. The CNN model makes use of transfer learning, extracting features using a pre-trained VGG16 architecture. Dropout regularisation, thick layers, and global average pooling are added for fine-tuning. Techniques for augmenting data are used during training to increase the resilience of the model. The model achieves a test accuracy of 85.49%, demonstrating its efficacy in breast cancer classification. Furthermore, confusion matrices are generated to visualize classification performance for each class, revealing insights into model behaviour. This study underscores the potential of CNN-based approaches in aiding medical professionals in accurate breast cancer detection through ultrasound imagery. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Feature Enhanced Semi-Supervised Attention U-Net for Cell Segmentation 15

Feature Enhanced Semi-Supervised Attention U-Net for Cell Se...

引用

15th international conference on computing Communication and Networking technologies, ICCCNT 2024

作者： Raj, Ardra S. Asha, S.B. Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Cell segmentation is crucial for accurately assessing disease severity. Despite advances in deep learning, there is still a gap in understanding how U-Net variants perform in addressing the issue of limited labeled data in cell segmentation. We compare various U-Net models to see how they fare with live cell images. We also examine how different feature extraction methods affect their performance. Using the Laplacian and Standard Deviation features improves performance by more than 3%, giving Attention U-Net mean Intersection over Union (IoU) scores of 0.7956 and mean Dice scores of 0.8844. In addition, we introduce a semi-supervised Mean Teacher Attention U-Net that leverages the same feature extraction techniques. Remarkably, it achieves an IoU score of 0.7908 and a Dice score of 0.8831, outperforming the existing approaches even with only 60% labeled data and utilizing limited computational resources. ©2024 IEEE.

关键词： Deep learning

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Level Up: Exploring the Dynamics of Student Achievement through Game-Based Learning 15

Level Up: Exploring the Dynamics of Student Achievement thro...

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Zuhaib, Affan Katta, Abhinav Prathima, T. Prasuna, P.M. Department of Artificial Intelligence and Data Science CBIT Telangana Hyderabad India Department of IT Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of CSE Ramachandra College of Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798331300579

This research is designed to solve the lack of tracking knowledge in games based on learning from a data- driven approach. Specifically, the project will use data from educational games to predict student outcomes in real time and offer personalized learning tailored to each student's learning needs. Machine learning will be used to evaluate game data and build predictive models for the project. The project will bridge this gap by using a data-driven approach to predict student achievement over time. By analysing data from educational games, the project will provide personalized learning to meet the needs of each student. By providing real-time feedback to students, the program aims to help them identify areas where they need to strengthen their knowledge, empowering them to make decisions when they want to learn more about their education. To achieve this goal, the project will use analysing techniques to evaluate data from game logs and develop predictive models. These models will provide a real-time view of students and help them identify areas where they need to improve their knowledge. The data used by this project is one of the largest open datasets of game engines. It provides valuable sources of tracking information for educational games. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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AI-ML Trained Object Recognition System Development using Google Teachable Machine with the Help of data sciences 15

AI-ML Trained Object Recognition System Development using Go...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Prasad, Rajneesh Pavithra, G. Manjunath, T.C. Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore560111 India

ISBN: (纸本)9798331300579

Object recognition is a crucial task in computer vision and artificial intelligence, as it plays a vital role in many applications such as autonomous vehicles, surveillance systems, and *** this paper, we present an overview of AI Trained object recognition using Google Teachable *** Teachable Machine is a web-based platform that allows users to train machine learning models without any coding or programming *** explore the steps involved in training an object recognition model using Google Teachable Machine and evaluate the performance of the model on a real-world *** results show that Google Teachable Machine is a powerful and user-friendly tool for training object recognition models with high *** research focuses on the development of an AI-ML trained object recognition system using Google Teachable Machine, enhanced by data science *** objective is to create a robust and efficient system capable of accurately identifying and classifying various objects in *** the user-friendly interface of Google Teachable Machine, the system is trained with diverse datasets, incorporating advanced machine learning algorithms and data preprocessing *** approach ensures high accuracy and reliability in object recognition *** integration of data science principles allows for thorough analysis and optimization of the training data, improving the system's performance and *** resulting system demonstrates significant potential for applications in areas such as security, automation, and augmented reality, showcasing the synergy between AI, machine learning, and data sciences in solving complex recognition problems. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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