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Detection of Chronic Stress Based on Electroencephalography Responses Induced by the Stroop Color Word Test 16

Detection of Chronic Stress Based on Electroencephalography ...

引用

16th Biomedical Engineering International Conference, BMEiCON 2024

作者： Nugroho, Totok Yoshimura, Natsue Tejada, Laura Alejandra Martinez Tokyo Institute of Technology Dept. Information and Communications Engineering Tokyo Japan Tokyo Institute of Technology Dept. Computer Science Tokyo Japan

ISBN: (纸本)9798331505431

Stress significantly affects the learning, academic performance, and health of university students. Traditional detection methods often rely on subjective self-reports, that can be stigmatized. In this study, we developed an electroencephalography (EEG)-based system to offer a more objective approach to chronic stress assessment. Fourteen participants (aged 24-35) were evaluated using the Perceived Stress Scale (PSS) and the Trier Inventory for Chronic Stress (TICS), with EEG data collected during resting and cognitive tasks (i.e., the Stroop color word test). The study aimed to identify the most effective brain regions and EEG features for chronic stress detection. Data analysis involved feature extraction, normalization, classification, and regression to build and assess detection models. The results indicated no significant differences among resting state, cognitive task, and perturbation signals in the classification of chronic stress levels. Resting-state signals were more effective, with logistic regression, support vector machine classifier (i.e., SVC), and random forest classifiers achieving accuracies of 93.35%, 93.26%, and 94.69%, respectively. Furthermore, the EEG-based regression models estimated PSS and TICS scores with high accuracy, with adjusted R2 values exceeding 0.90, particularly for PSS. Contributive EEG features for chronic stress detection included gamma in the frontal, central, and parietal regions, as well as beta in the frontal and parietal regions. Limitations related to the sample size and circadian rhythm control were acknowledged, suggesting the need for future research with larger samples and varied recording schedules. © 2024 IEEE.

关键词： Electroencephalography

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Accelerating Cloud Deployment Through Containerization Technologies

Accelerating Cloud Deployment Through Containerization Techn...

引用

2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Lakkadwala, Aayushya Lakkadwala, Prashant Acropolis Institute of Technology and Research Dept. of Computer Science and Engineering Indore India Acropolis Institute of Technology and Research Dept. of Information Technology Indore India

ISBN: (纸本)9798350388800

This research investigates the effectiveness of containerization technologies, with a focus on Docker, in accelerating cloud application deployments. The study examines diverse methodologies aimed at optimizing container image size, refining container orchestration mechanisms for expedited deployments, and evaluating the integration of serverless functions within containers to reduce deployment durations. By synthesizing existing scholarly literature with empirical analyses, this paper aims to elucidate the efficacy of containerization in streamlining cloud deployment processes. It offers insights into optimal practices and identifies prospective avenues for future research and development in the field. © 2024 IEEE.

关键词： Cloud platforms

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Autoencoded Image Compression for Secure and Fast Transmission

Autoencoded Image Compression for Secure and Fast Transmissi...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Naveen, Aryan Kashyap Thunga, Sunil Murki, Anuhya Kalale, Mahati Anil, Shriya National Institute of Technology Karnataka Dept. of Information Technology Surathkal India National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798350376876

With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer. © 2024 IEEE.

关键词： Image compression

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Sub Event Relation Classification for Amharic Texts Using Deep Learning Approach

Sub Event Relation Classification for Amharic Texts Using De...

引用

2024 International Conference on information and Communication technology for Development for Africa, ICT4DA 2024

作者： Desta, Abinet Bizuayehu Abetw, Abebe Alemu Werku, Kebad Nigussie Asfaw, Nega Agmas Belachew, Habtamu Molla Alemu, Wubie Belay Debark University Dept. Information Technology Debark Ethiopia University of Gondar Dept. Computer Science Gondar Ethiopia Woldia University Dept. Computer Science Woldia Ethiopia

ISBN: (纸本)9798331527792

This research focuses on the challenge of Sub-event Relation Classification (SERC) in Amharic text, with the objective of identifying relationships between event triggers or mentions in sentences. The aim is to predict sub-event relationships in Amharic sentences, benefiting applications such as information extraction, question answering, and document summarization. Amharic text requires a lot of effort to manually identify event relationships, and there isn't yet a model that specializes in extracting event relationships from Amharic language. Deep learning techniques, particularly LSTM and BiLSTM, are used in this work to categorize the relation type between event pairs in unstructured Amharic text in order to address this difficulty. The models are trained and assessed using a dataset of 6622 manually annotated Amharic texts. Experimental results showcase the effectiveness of the BiLSTM model, achieving a high accuracy of 93% for Amharic sub-event relation classification. The findings of this research hold significant implications for natural language processing applications in Amharic and other languages. Moreover, the developed model can be extended to other languages, making it a valuable contribution to the field of natural language processing. © 2024 IEEE.

关键词： Amharic Sub-event Relation Classification (A-SERC) Bidirectional Long Short-Term Memory (BiLSTM) Deep Learning (DL) Long Short-Term Memory (LSTM) Natural Language Processing (NLP) Word2Vec

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Enhancing Workload Predictions Using Service Interactions in Cloud-Native Microservices 13

Enhancing Workload Predictions Using Service Interactions in...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Mahajan, Isham Nadig, Deepak Purdue University Dept. of Computer and Information Technology West LafayetteIN47907 United States

ISBN: (纸本)9798350376562

Container orchestration systems, such as Kuber-netes, often rely on manual resource allocation to manage re-sources, which can be inefficient and inflexible due to the frequent over-provisioning or underprovisioning of resources. Kubernetes horizontal pod autoscaler (HPA), vertical pod autoscaler (VPA), and Google Kubernetes Engine (GKE) Autopilot are primarily threshold-based, making them reactive rather than proactive as they adjust resources after exceeding utilization thresholds, leading to temporary degradation in quality of service (QoS). While some solutions utilize calls per minute (CPM) counts for requests to microservices to estimate resource consumption dynamically, they do not fully exploit distributed traces or associated microservices' interdependencies. We hypothesize that we can gain deeper insights into future workload patterns by exploiting microservices' interaction and the CPM counts for each pair of communicating microservices. In this paper, we propose a comprehensive machine learning workflow to assess whether factoring in the interdependencies between microservices results in improved workload prediction. Our findings indicate that an LSTM model performs well, with average mean absolute error (MAE) and root mean square error (RMSE) values of 7.02 and 10.54, respectively. The highest R2 score observed was 0.07. This suggests that although incorporating distributed traces and inter-microservice CPM counts provides valuable insights, the models fail to capture the full complexity of workload dynamics. These results highlight the potential for enhancing workload prediction accuracy and underscore the need to refine these methods further to achieve more proactive and efficient resource allocation in container orchestration systems. © 2024 IEEE.

关键词： Quality of service

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A Comparative Analysis of Machine Learning Classification Algorithms for Cardiovascular Disease Prediction 9

A Comparative Analysis of Machine Learning Classification Al...

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9th IEEE International Conference on Adaptive Science and technology, ICAST 2024

作者： Attipoe, Elliot Kojo Yussiff, Alimatu Saadia Asante-Mensah, Maame Gyamfua University of Cape Coast Dept. of Computer Science and Information Technology Cape Coast Ghana

ISBN: (纸本)9798350385403

Cardiovascular disease is a life-threatening condition accounting for over 17 million deaths globally every year. Several studies have revealed the advancement of machine learning algorithms in predictive modeling and risk assessment, leading to improved chronic disease management and preventive measures. The algorithms analyze complex medical datasets to uncover hidden patterns, trends, and correlations that might be difficult for the medical practitioner, thereby improving the efficiency and accuracy of the medical decision-making process. This study utilizes a cardiovascular disease dataset comprising 70,000 instances to compare the performance of ten classification algorithms in predicting cardiovascular diseases. A new machine learning model is proposed, which performs better than all existing models using the same dataset. The results show the XGBoost classifier as the best model for predicting cardiovascular diseases, with an accuracy of 73.3% and an AUC score of 80%. © 2024 IEEE.

关键词： Contrastive Learning

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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep Learning 4

WellBe: An Intelligent Elderly Care and Well-Being Monitorin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Raotole, Atharv Shirodkar, Sailee Sanjay Shukla, Rahul Sisodia, Jignesh Devadkar, Kailas Patel Institute of Technology Dept. of Information Technology Sardar Mumbai India Patel Institute of Technology Dept. of Computer Science and Engineering Sardar Mumbai India

ISBN: (纸本)9798350349900

Deep Learning refers to a subset of Machine Learning that utilizes deep neural networks to simulate the complex decision-making process of the human brain. In recent years, DL has made remarkable progress in addressing multiple unique challenges faced by most elderly individuals. While there have been significant contributions to independent systems catered for the elderly, there is the existence of an overall integrated AI platform that unifies the use of individual modules for an optimized solution. This paper introduces 'WellBe,' an innovative AI system that encompasses an alarm-based fall detection component, a facial emotion recognition (FER) system and a built-in personalized chatbot all under a user-friendly website. The novelty of the work extends beyond the aforementioned components, owing to the implementation of an optimized FER system able to handle wrinkles and changes in skin texture, thereby tailored exclusively for the elderly demographic. The system leverages a custom feature extraction module, fusing facial and wrinkle features, in addition to extending a convolutional neural network (CNN) architecture for accurate emotion classification, achieving promising performance with precision, recall, and F1-scores of 0.87, 0.91, and 0.89 for the 'Happy' (one of the predicted emotions) class, respectively. The fall detection component, on the other hand, utilizes a pre-trained YOLOv7 Pose model for the real-time identification of falls, sitting at a performance accuracy of 95.3%. Finally, the personalized chatbot is implemented using a rule-based approach enhanced with the Jaccard similarity algorithm, to facilitate prompt assistance and a sense of companionship to the end user. Overall, the integration of these components into a cohesive website offers a comprehensive solution for enhancing the quality of life for elderly individuals living alone, addressing loneliness, safety, and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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To Kill a Mockingbird: Cryptanalysis of an Authenticated Key Exchange Scheme for Drones 15

To Kill a Mockingbird: Cryptanalysis of an Authenticated Key...

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15th International Conference on information and Knowledge technology, IKT 2024

作者： Toghraee, Neda Mala, Hamid Dept. of Information Technology Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Drones have evolved into sophisticated autonomous systems with a multitude of applications, including military operations, environmental monitoring, traffic oversight, data transmission, package delivery, fire targeting, and film production. As the Internet of Drones (IoD) ecosystem expands, ensuring secure and real-time access for authorized users becomes increasingly vital. User and drone authentication is crucial for this reason. In response to these challenges, Srinivas et al. proposed a lightweight three-factor authentication protocol designed for the IoD. While the authors assert that their protocol is resilient against known cyber threats, our research identifies critical vulnerabilities that challenge this claim. Although this scheme has already been cryptanalyzed in previous studies, in this paper we propose two new attacks on this scheme. Firstly, we present a concrete attack against the perfect forward secrecy of this scheme, and then we show that it is vulnerable to unauthorized access attack by a valid user to an unauthorized area's information. These weaknesses highlight the pressing need for the development of more secure authentication mechanisms in the IoD environment. Moving forward, addressing these vulnerabilities will be essential for fostering trust and ensuring the safe integration of drones into various applications, ultimately contributing to the advancement of IoD technology. © 2024 IEEE.

关键词： Authentication Protocol

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Deep Dive into IIoT Intrusion Detection Systems: A Review and Future Directions 3

Deep Dive into IIoT Intrusion Detection Systems: A Review an...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Pawar, Sushama Laxman Karyakarte, Mandar S. BRACT's Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

The Industrial Internet of Things (IIoT) has quickly become a cutting-edge technology that can transform various sectors by digitalizing and connecting them, boosting business opportunities and global economic growth. Sectors like manufacturing, logistics, transportation, energy, and aviation are increasingly adopting IIoT. However, IIoT is vulnerable to cyber threats, requiring robust security measures. The numerous sensors in IIoT systems generate vast amounts of data, attracting cybercriminals. An Intrusion Detection System (IDS), which observe network traffic to detect unusual behavior, is crucial for securing IIoT applications. Recently, machine learning and deep learning techniques have shown potential in mitigating security threats and enhancing IDS performance. This paper reviews different deep learning-based IDS methods for IIoT, including techniques, datasets, and comparative analysis, and highlights existing limitations and future research directions. © 2024 IEEE.

关键词： Network intrusion

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Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT 21

Empowering Low-Resource Language Translation: Methodologies ...

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21st International Conference on Spoken Language Translation, IWSLT 2024

作者： Anand, Harpreet Singh Dash, Amulya Ratna Sharma, Yashvardhan Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798891761414

This paper presents the methodologies implemented for the Automatic Speech Recognition and Machine Translation for the language pairs Bhojpuri-Hindi and Marathi-Hindi for the Dialectal and Low-Resource shared task proposed by The International Conference on Spoken Language Translation (IWSLT) for 2024. The implemented method uses the transcriptions generated through a fine-tuned Whisper models(for Marathi-Hindi) and vakyansh-wav2vec model (for Bhojpuri-Hindi) and generates the translations using fine-tuned NLLB(No Language Left Behind) Models for both the tasks. The selection of more accurate translation is done through sentence-embeddings generated using the MuRIL(Multilingual Representations for Indian Languages)(Khanuja et al., 2021) model for the Marathi-Hindi task. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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