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共 31082 条记录，以下是261-270 订阅

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Psychological Wellbeingness Prediction Using Machine Learning with Hybrid Hyparameter Tuning Techniques

Psychological Wellbeingness Prediction Using Machine Learnin...

引用

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

作者： Rajah, Puvanenthira Habib, Masruk Ravikumar, R.N. Nadarajan, Sivakumar Chougule, Bhagyashree Amit Kirubhakaran, M. Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Tatyasaheb Kore Institute of Engineering and Technology Dept. of Computer Engineering Maharashtra India Suriya Engineering College Dept. of Ai & Ds Erode India

ISBN: (纸本)9798350379716

This study investigates the application of machine learning (ML) techniques in predicting Psychological Well-being outcomes, emphasizing the use of ensemble methods like AdaBoost and Random Forest for enhanced accuracy. With Psychological well-being issues affecting a significant portion of the global population, traditional assessment methods are challenged by issues of accessibility, stigma, and subjectivity. Leveraging data from various surveys, this research compares different ML algorithms, including Logistic Regression and Support Vector Machines, in predicting psychological well-being conditions. The results highlight the superior performance of AdaBoost and Random Forest, particularly when hyperparameters are finely tuned, achieving accuracies up to 0.996. The study underscores the potential of ML in improving psychological well-being interventions by offering precise, unbiased, and accessible diagnostic tools. Future research directions include the integration of multimodal data and the development of models that can suggest personalized treatment plans while ensuring data privacy. This paper advocates for the integration of advanced computational techniques with clinical insights to revolutionize psychological well-being diagnostics and care. © 2024 IEEE.

关键词： Clinical research

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Advancements in Quantum Machine Learning and Quantum Deep Learning: A Comprehensive Review of Algorithms, Challenges, and Future Directions

Advancements in Quantum Machine Learning and Quantum Deep Le...

引用

2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Priyanka, G.S. Venkatesan, M. Prabhavathy, P. Nit Puducherry Dept. of Computer Science & Engineering Karaikal India Vit University Dept. of Information Technology Vellore India

ISBN: (纸本)9798350314946

At the nexus of quantum computing and machine learning, quantum machine learning (QML) and quantum deep learning (QDL) have emerged as promising fields. By enhancing computational capabilities and addressing challenging data analysis problems, these approaches have the potential to revolutionise a number of domains by leveraging the special properties of quantum systems, such as superposition and entanglement. In this article, we give a thorough analysis of QML and QDL algorithms, discussing their underlying ideas, benefits over conventional models, and potential uses. We talk about the difficulties these algorithms face, such as noisy quantum hardware, constrained qubit resources, training and optimisation issues, input representation and data encoding problems, and limited interpretability. Additionally, we look at current developments and lines of inquiry that aim to address these issues, including hybrid classical-quantum methods, error-mitigating techniques, better data representation techniques, quantum transfer learning, and resource optimisation. © 2023 IEEE.

关键词： Deep learning

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Optimizing Human Activity Recognition for Precision in Yoga Practice Analysis 8

Optimizing Human Activity Recognition for Precision in Yoga ...

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. Rodrigues, Maria Viola Ritesh Muhammed Elham, P.N. Mannan, Mohammed Abdul St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

The proposed system on enhancing human activity recognition in yoga presents an innovative solution using advanced technologies to accurately and quickly analyze data of yoga poses. Through meticulous data collection and preprocessing, including noise removal, normalization, and handling missing data, the system ensures a clean and standardized dataset. Pose estimation algorithms contribute to accurately mapping the body's posture, extracting essential features like joint angles and body orientation. The features form the basis for a powerful machine learning model, trained on a labelled dataset and employing neural networks, enabling the system to recognize yoga poses with remarkable precision. The study embraces an iterative enhancement process, continuously refining the model based on feedback and performance metrics, ensuring ongoing improvements in accuracy. Ultimately, the proposed system represents a cutting-edge solution for advancing human activity recognition in yoga, providing practitioners with real- time feedback and guidance, fostering improved techniques, and enhancing the overall yoga experience. © 2024 IEEE.

关键词： Mapping

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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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2024 International Conference on information technology Research and Innovation, ICITRI 2024

作者： Senjaya, Remi Musa, Purnawarman Sularsih, Puji Novrina, Novrina Aryunani, Witari Suhartini, Suhartini Gunadarma University dept. of Informatics Engineering Depok Indonesia Gunadarma University dept. of Electrical Engineering Depok Indonesia Gunadarma University dept. of Information Technology Depok Indonesia Gunadarma University dept. of Computer Science Depok Indonesia

ISBN: (纸本)9798350376210

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects' characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain's image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique;however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%. © 2024 IEEE.

关键词： Convolutional neural networks

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Prioritization of Medical Image Security Features: Fuzzy AHP Approaches 6

Prioritization of Medical Image Security Features: Fuzzy AHP...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Satish Devi, Mausmi Singh, Sunil Chaurasia, Pawan Kumar Khan, Raees Ahmad BBA University Dept. of Information Technology Lucknow India PSIT Dept. of Computer Science & Engineering AKTU Lucknow India

ISBN: (纸本)9798350304480

Medical images are sensitive data about patients, hospitals, healthcare sector and, therefore, require secure transmission to protect their privacy. To ensure the security of medical image transmission, three essential factors are vitally employed: integrity, confidentiality, and authentication. In this study, we propose an F-AHP technique to determine the relative importance of these security factors. The technique was implemented using 'Octave MATLAB tools'to obtain the weights for each factor, and the results showed that integrity is the most critical factor for the transmission of medical image security than confidentiality and authentication. This technique can help healthcare organizations to choose the appropriate security measures for transmitting medical images while ensuring that patient data is protected during transmission. Overall, the researcher highlights the importance of security in medical image transmission and provides a framework for healthcare organizations to implement effective security measures. © 2023 IEEE.

关键词： Authentication Confidentiality Fuzzy AHP information Security Integrity Medical Image Security

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EfficientNet Convolutional Neural Network for Automated Cervical Spine Fracture Identification 6

EfficientNet Convolutional Neural Network for Automated Cerv...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Sidpara, Pragati Agarwal, Anshul Swain, Debabrata Gupta, Rajeev Kumar Bhilare, Amol Upkare, Makarand Pandit Deendayal Energy University Dept. of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331516796

The aim of this study is to develop an automatic system that uses deep learning techniques to detect cervical spine fractures from medical images. Using a DICOM (Digital imaging and communications in medicine) images of cervical spine, the system can identify a fracture and specify which vertebrae has a fracture. This process involves first processing provided DICOM images, then applying data augmentation techniques including metadata information, finally designing neural network architecture for feature extraction. We use a method called transfer learning, utilizing a pre-trained model like EfficientNet, which is used for feature extraction, while optimizing network parameters using AdanW optimizer and CosineAnnealingWarmup learning rate scheduler. The results demonstrate the successful training of neural network models to identify fracture and that fractured vertebrae, with commendable performance and accuracy level noted during evaluation on validation data. This study has important implications for medical imaging and healthcare industries, since automated fracture detection can increase diagnostic efficiency and accuracy. Moreover, such a system would provide good support to healthcare professionals, specifically radiologists, to identify cervical spine fracture easily. © 2024 IEEE.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Breathing+: A Game-based System for Enhancing Respiratory Training and Motivation 6

Breathing+: A Game-based System for Enhancing Respiratory Tr...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Lin, Chang Yu Chu, Edward T.-H. National Yunlin University of Science and Technology Dept. of Computer Science and Information Engineering Yunlin Taiwan

ISBN: (纸本)9798350323535

Breathing difficulties, such as shortness of breath or rapid breathing, can significantly harm health and diminish the quality of life. Breathing exercises are widely recognized for their effectiveness in resolving such difficulties. They increase lung function, ease discomfort, and alleviate stress. However, current breathing-assisted methods, such as the incentive spirometer, do not accurately track the duration and frequency of training despite their advantages. Furthermore, the process of performing breathing exercises is tedious over time, making users feel bored and reducing their motivation. Thus, we presented Breathing+, a breathing video game designed to address these limitations by providing a series of game challenges. Breathing+ incorporated sensors to detect breathing signals and provided users with real-time feedback on a screen. According to the experimental results, Breathing+ achieved a breathing recognition accuracy of 95%. In addition, the response time to the user's breathing was around 0.23 s. © 2023 IEEE.

关键词： Motivation

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Pathways for Security of Embedded System: Issues and Challenges 2

Pathways for Security of Embedded System: Issues and Challen...

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

作者： Diwaker, Sanjai K. Chaurasia, Pawan Kumar Khaliq, Mazhar Khwaja Moinuddin Chishti Language Dept. of Computer Science and Information Technology Lucknow India Babasaheb Bhimrao Ambedkar Central University Dept. of Information Technology Lucknow India

ISBN: (纸本)9798350364699

In today's scenario, security threats are increasing day by day as technology. Security breaches not only affect financial loss but also affect the trustworthiness and integrity of the software design and embedded software development industry. These breaches directly affect the security attributes. The objective of security in an embedded system is to protect the device from vulnerable attributes. The aim of this paper, identify challenges in embedded systems applied in various applications, research gaps, and untouched areas to discover possible parameters for sustainable devices. © 2024 IEEE.

关键词： Access Control Authenticity Authorization Cyber security Device Manager Healthcare Internet of Medical Things

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