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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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WorkoutNet: A Deep Learning Model for the Recognition of Workout Actions from Still Images 3

WorkoutNet: A Deep Learning Model for the Recognition of Wor...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Dey, Arnab Dutta, Arpita Biswas, Samit Indian Institute of Engineering Science and Technology Dept. of Computer Science and Technology Howrah India

ISBN: (纸本)9798350338607

Workouts or Exercises are some repetitive physical activities essential for maintaining good health. Workouts make us relax and happy, give us more energy, lower the risk of chronic disease, and rejuvenate our bodies and minds. Proper technique and posture are essential to get the most out of any workout;the same workout may lead to injury if performed incorrectly. This paper presents a method to identify a person's workout action from realistic still images. In contrast to video-based action recognition, the challenge is more difficult for still photographs because the Spatio-temporal information is not present in images. This study proposes a deep learning-based model named WorkoutNet to recognize workout actions. The method achieved remarkable results on the Workout Action Image dataset, consisting of 2796 images prepared by us and on the publicly available Exercise image dataset. The suggested approach predicts different workout actions and classifies them into ten categories with high accuracy. The proposed WorkoutNet model attains 92.75% validation accuracy in classifying the workout action images, succeeded by the XceptionNet, VGG-19, DenseNet121, InceptionV3, MobileNet and VGG-16. Further, the classification performance has been evaluated using the F1-Measure and Confusion Matrix. The suggested model can be utilized in real-life to identify workout action images and assist humans doing workouts without a trainer or guide in understanding the right way. © 2023 IEEE.

关键词： Deep learning

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Enhancing Multi-label text classification using adaptive promptify concepts 15

Enhancing Multi-label text classification using adaptive pro...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Veerababu Uppukonda, Usha Rani Veeranjaneyulu, N. Vignan's Foundation for Science Technology & Research Dept. of Computer Science & Engineering Guntur India Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India Vignan's Foundation for Science Technology & Research Dept. of Information Technology Guntur India

ISBN: (纸本)9798350370249

In present days, Multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenges. Hence a model is developed to overcome the above said challenges by introducing Prompt engineering, GPT FineTuning methods and Pipeline algorithms. The experimental results are obtained based on the present Generative pre trained data produces labels for various text documents and files with an exceptional accuracy of 85%. The outcomes of the experiments reveal notable enhancements over the baseline models. © 2024 IEEE.

关键词： Fine-Tuning GPT Multi-Label Text classification NLP (Natural Language Processing) Pre-trained models Prompt engineering Transformers

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Prediction and Modelling of Suicidal Mental Tendencies 8

Prediction and Modelling of Suicidal Mental Tendencies

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

作者： Guruprasad, Sunitha Rao, Arnav G. Shreeraksha Shivani, K. Varsha, S.N. Yashoda, K. St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation. © 2024 IEEE.

关键词： Prediction models

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory Data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory Data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory Data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： Data visualization

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Enhancing Blind Communication With Voice-Assisted Email 4

Enhancing Blind Communication With Voice-Assisted Email

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Sasikala, C. Rupalakshmi, M. Sameer Ahammad, S. Meghana, U. Harshad Ali, M. Srinivasa Ramanujan Institute of Technology Dept. Computer Science and Engineering Anantapur India Anantapur India

ISBN: (纸本)9798350385793

Considering the key role of the internet in daily life, accessibility becomes crucial, especially for visually impaired individuals who heavily rely on it for accessing information. However, a significant portion of text materials on the internet remains inaccessible to blind users, limiting their opportunities for development and inclusion in various communities worldwide. Leveraging audio virtual environments, predominantly through screen reader technology, offers a notable advantage for blind individuals in accessing online applications like emails. This study presents a new design aimed at facilitating the email retrieval for blind users with the utilization of speech-to-text and text-to-speech technologies. This approach is particularly suitable for individuals with average human capabilities and a limited level of education, as it relies solely on interactive voice response for input, ensuring effective functionality for the proposed system. © 2024 IEEE.

关键词： Application programming interfaces (API)

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Advancing Medical Imaging: A Comprehensive Synthetic Dataset for Infant Brain MRI Analysis 2

Advancing Medical Imaging: A Comprehensive Synthetic Dataset...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Yashasvi, B.N. Kavya, N.P. R N S Institute of Technology VTU Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350371314

Accurate diagnosis and treatment planning are consistently challenging tasks in the medical field, especially when reliant on image diagnosis. The availability of sufficient and high-quality datasets becomes crucial in addressing this challenge. To tackle this issue, synthetic datasets can play a vital role in augmenting the number of samples while maintaining good quality. This study focuses on utilizing Deep Convolutional GANs (DCGANs) and Wasserstein GANs (WGANs) to generate synthetic datasets, specifically for Infant Brain MRI imaging. The Human Connectome Project (dHCP) dataset, comprising 800 samples categorized into four classes, was used for this study. Several key metrics, including the Similarity Index (SSI), Inception Score (IS), and Fréchet Inception Distance (FID), were employed to evaluate the performance of the generated synthetic datasets. The Inception Score was calculated for each class individually as well as for the overall synthetic dataset. For the normal class (first class), DCGAN achieved an Inception Score of lS.45, while WGAN scored 15.13. In the second class, DCGAN scored 19.70 compared to WGAN's 16.21. Similarly, in the third class, DCGAN scored lS.65 and WGAN 15.65, and in the abnormal class (fourth class), DCGAN scored 17.58 against WGAN's 16.00. The overall synthetic dataset scored 19.29 for DCGAN and 16.47 for WGAN. As the higher Inception Scores across different classes and for the overall dataset denoted that DCGAN performed well compare to WGAN in generating synthetic images for the Infant Brain MRI dataset. © 2024 IEEE.

关键词： Generative adversarial networks

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Fake/Counterfeit Medical Product Identification Using Blockchain & QR Code 2

Fake/Counterfeit Medical Product Identification Using Blockc...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Devanshu Singhal, Aryan Singh, Khushi Singh, Pushpa Gl Bajaj Institute of Technology and Management Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Counterfeiting in the field of medicine has raised substantial concerns as a global issue, with its prevalence noticeably expanding in recent years. Counterfeit medicines pose a particularly severe threat to patient's life or safety and cause severe implications. It is difficult to trace a medical product as well as to identify its authenticity. This paper aims to combat the widespread issue of counterfeit goods in industries like Pharmaceuticals and Healthcare by creating a Blockchain based product identification system. Combining the potential of Blockchain technology along with a secure QR code mechanism will offer robust security, immutability of data and have transparency in all the transactions. Thus, this system will ensure product traceability in the supply chain whereas the QR code will make it feasible for consumers to access product information instantly. © 2024 IEEE.

关键词： blockchain counterfeiting identification medicines QR code

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