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Enhanced Predictive modeling for Myocardial Infarction Risk Assessment through Multimodal Data Integration

Enhanced Predictive Modeling for Myocardial Infarction Risk ...

引用

Electronics and Renewable systems (ICEARS), international conference on

作者： Abdulkadar S Dharun S Kirthivashan V Janani B M.E. Department of Computer Science and Engineering K.S. Rangasamy College of Technology India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Since myocardial infarction (MI) continues to be a major cause of death worldwide, precise risk prediction is essential. In order to improve MI risk assessment, this work presents an improved predictive model that makes use of multimodal data, including clinical, genetic, imaging, and lifestyle information. By utilizing sophisticated machine learning methods, specifically the XGBClassifier, the model combines various datasets to reveal intricate patterns, attaining exceptional sensitivity and specificity. Robust reliability testing, a comparison with current approaches, and the use of interpretability tools such as SHAP guarantee that the suggested model provides useful information for clinical decision-making. Addressing dataset biases and extending feature integration to improve generalizability are two potential future steps.

关键词： measurement Renewable energy sources Accuracy Myocardium Predictive models Sensitivity and specificity Real-time systems Risk management Reliability Testing

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hA Hybrid Model Combining Vision Transformer and Generative Pre-trained Transformer for Image Captioning

hA Hybrid Model Combining Vision Transformer and Generative ...

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Pervasive Computational Technologies (ICPCT), international conference on

作者： Hina Gupta Jatangi Y D M V Prasada Vishwesh M. Jagadeesh M. Sucharitha Vitta Sai Pradyothan School of Computer Science Engineering & Technology Bennett University Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Creating descriptive captions for images is now becoming a mission-critical application area in the intersection of natural language processing and computer vision. This work provides the hybrid model VisionGPT2, combining Vision Transformer for encoding images and GPT2 for text generation, which enables accurate, coherent, and context-aware image descriptions. The model is tested over a dataset by using BLEU score metrics and has achieved a high BLEU score of 0.4877, which ascertains the strength of cross-modal architectures for image captioning. In this paper, the design and training process of VisionGPT2 has been discussed along with the benefits and challenges faced while proceeding and the potential applications the model has gained in performance for future use.

关键词： Training measurement computer vision Image coding Computational modeling Generative Pre-trainer transformer Mission critical systems Transformers Natural language processing Hybrid power systems

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Attentiveness in Virtual Reality Based on Body joint Positions

Attentiveness in Virtual Reality Based on Body Joint Positio...

引用

IEEE international conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Vinayak Venugopal Sagnik Dakshit Mission Bay High school San Diego USA Computer Science The University of Texas at Tyler Tyler USA

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

Advancements in Artificial Intelligence (AI) and Virtual Reality (VR) have introduced transformative applications across fields, such as education and healthcare, through immersive learning environments. However, maintaining user attentiveness remains a challenge in VR environments, where immersion and physical interaction are crucial to efficacy, which consequently impacts the outcomes of the experience expected from the environments. This study investigates the utilization of full-body joint positions as predictors of user attentiveness in both supervised and unsupervised learning settings. By tracking joint movements in the Plant Walk VR experience, we applied machine learning models to classify attentiveness without engineered features, achieving 100% accuracy on limited privately collected data. Additionally, this study explores feature importance from both the best performing model, namely the interpretable decision tree model, and the explanation methods identifying six critical joint positions as predictors. The high-performance results of all machine learning models elucidate the potential of joint-based attentiveness monitoring to enhance user engagement and could inform the development of adaptive VR systems for educational and therapeutic applications.

关键词： Solid modeling Adaptation models Tracking Machine learning Predictive models Prediction algorithms Classification algorithms Decision trees Unsupervised learning Immersive learning

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joint Task Offloading and UAV Deployment for Collaborative HAP-UAV systems

Joint Task Offloading and UAV Deployment for Collaborative H...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Kun Wang Haotian Ma Luwei Fu Qinyong Li Zhiwei Zhao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In emergency scenarios such as natural disasters, unmanned aerial vehicles (UAVs) are widely employed to provide communication and computation services when ground infrastructure fails. However, UAVs face limitations in resources. High Altitude Platforms (HAPs), operating in the stratosphere, offer a promising solution with their wide coverage and substantial payload capacity. This paper proposes a collaborative task offloading scheme that combines UAVs and HAPs to minimize task delays, considering task types and the mobility of ground devices, which are typically overlooked. We jointly optimize UAV deployment and task scheduling, modeling deployment as a maximum clique problem and task scheduling as a device-UAV matching problem solved by a stable matching algorithm. Numerical simulations demonstrate the effectiveness of the proposed solution.

关键词： Processor scheduling Simulation Collaboration Terrestrial atmosphere Autonomous aerial vehicles Numerical simulation Scheduling Numerical models Delays Payloads

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Emotion Recognition Using EEG Signals in Human Brain Waves and Deep Learning Approaches

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Narit Hnoohom Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Thailand Department of Computer Engineering Faculty of Engineering Image Information and Intelligence Laboratory Mahidol University Nakhon Pathom Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. The classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). These bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). The LSTM and GRU models yield intermediate results, nearing 92% accuracy. The findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. This research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems.

关键词： Deep learning Emotion recognition Accuracy Computational modeling Bidirectional long short term memory Mental health computer architecture Brain modeling Electroencephalography Recording

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Image Captioning using CNN and LSTMs

Image Captioning using CNN and LSTMs

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Rajeel Ahmad Ansari Divyapratap Singh Chauhan B. Jothi Krishnaveni S Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Image captioning is a pivotal field in AI, enabling machines to generate descriptive text for images, with applications in accessibility, content creation, and human-computer interaction. This study identifies the shortcomings of the conventional CNN-RNN model by proposing an advanced architecture that incorporates InceptionV3, LSTM, attention mechanism, and beam search. The image features are extracted using Incep-tionV3; captions are generated using LSTMs, where an attention mechanism is used to guide the model to identify important regions of the image for each word. Beam search has been employed to choose the most optimal captions. The suggested model is tested on the Flickr8k data set with the help of the BLEU metric. The outcomes show that the caption relevance, diversity, and interpretability of the model are enhanced, with BLEU-1 and BLEU-2 scores of 0.5365 and 0.3218 respectively, demonstrating its potential in real-world applications such as providing accessibility and machine learning-based content generation.

关键词： Training measurement Human computer interaction Technological innovation Attention mechanisms Computational modeling Feature extraction Intelligent systems Long short term memory Tuning

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management

Waste Sorting System Using AI: Development of An Embedded So...

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international Image Processing, Applications and systems conference (IPAS)

作者： Amal Remili Mouna Zouari Mahdi Jihen Frikha Elleuch Ahmed Fakhfakh Dorra Sellami Electrical Engineering Department National Engineering School of Sfax Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

关键词： Waste management measurement computer vision Computational modeling Image processing Real-time systems Plastics Artificial intelligence System validation Sorting

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Comparative Study of Multilingual Intelligent Virtual Assistant

Comparative Study of Multilingual Intelligent Virtual Assist...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Pallavi Yevale Nilesh Uke Department of Computer Engineering Vishwakarma Institute of Information Technology International Institute of Information Technology Pune India Department of Computer Engineering Indira College of Engineering and Management Pune India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Every conversational system or goal-oriented dialogue system must have spoken language understanding (SLU), which is a crucial element. To help the user achieve their desired goals, it is essential to comprehend the user's statement. The multilingual intelligent virtual assistant model based techniques for accurately reacting to the user without delay are taken into consideration by many studies in recent days, but very few review articles are available on multilingual virtual assistant. In this study, multilingual intelligent virtual assistant systems were examined. To enhance accuracy and F1 measure regarding the multilingual intelligent virtual assistant model, benefits and downsides of every strategy, challenges and potential areas for future research are observed. The challenges examined in the study such as over fitting issues, lack of datasets, failure in recognizing low resource images, and poor detection performance can be overcome by future researches.

关键词： Location awareness Accuracy Reviews Social networking (online) Virtual assistants Computational modeling Medical services Time measurement Multilingual Time factors

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Improved Object Detection Through a Learning-Based joint-Initiative Supervised Autonomy (JISA) Framework

Improved Object Detection Through a Learning-Based Joint-Ini...

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Control, Automation, and Instrumentation (IC2AI), international conference on

作者： Andrea Tarabay Naseem Daher Dept. of Electrical and Computer Engineering American University of Beirut Beirut Lebanon Dept. of Electrical and Computer Engineering Dept. of Mechanical Engineering American University of Beirut Beirut Lebanon

ISBN: (数字)9798331529604

ISBN: (纸本)9798331529611

Object detection is faced with accuracy and robustness limitations due to unstructured environments with objects of different sizes and occlusions. Despite advancements in object detection models, these models have not reached a point to possess a comparable level of perception to humans, thus limiting their ability to effectively address challenges in diverse environments. Hence, adopting a human-in-the-loop approach in autonomous systems can handle complex perception scenarios. To tackle these issues, the joint-Initiative Supervised Autonomy (JISA) framework is used to allow a human supervisor to intervene once the agent is uncertain of its predictions based on its self-confidence (SC) metric, or when the human deems the prediction incorrect based on his/her situation awareness (SA). However, the existing JISA framework formally defines the levels of autonomy between the human and the agent, but does not provide a means for learning from human interventions. In this paper, we present an enhanced version of JISA in object detection applications by implementing a learning scheme that is inspired by the Interactive Corrections and Reinforcement for an Epistemic and Aleatoric uncertainty-aware Teaching (ICREATe) algorithm. To address these challenges, we evaluate the results of the proposed framework and demonstrate that the model effectively learns and improves its performance through human feedback.

关键词： Training measurement Limiting Automation Accuracy Autonomous systems Instruments Object detection Human in the loop Robustness

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Real-Time Yoga Pose Recognition and Correction Feedback System Using Deep Learning and computer Vision

Real-Time Yoga Pose Recognition and Correction Feedback Syst...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Vishal Chauhan Mukul Aggarwal Yash Singhal Swapnil Shukla Department of IT KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Yoga pose detection is a rapidly evolving field that integrates advanced technology with wellness, addressing the need for accurate and adaptable systems for diverse user groups. This paper proposes a novel framework that combines RGB images, depth maps, and skeletal joint data to enhance multi-modal feature extraction and improve spatial-temporal analysis for tasks such as action recognition and human activity modeling. The framework overcomes challenges such as pose variations and occlusions through techniques like data augmentation, model optimization, and robust performance evaluation. Experimental results demonstrate the system’s high accuracy of 91.7%, successfully detecting six distinct yoga pose sequences in real time and providing self-correction feedback to users, thereby improving performance. The practical applications of this approach are significant, with potential use cases in yoga studios, fitness centers, and home environments, promoting scalable, precise, and globally accessible yoga practices.

关键词： Deep learning Performance evaluation computer vision Accuracy Image recognition Feature extraction Data augmentation Real-time systems Data models Optimization

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