Publications
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ACE: how Artificial Character Embodiment shapes user behaviour in multi-modal interactions
The ACE -how Artifcial Character Embodiment shapes user behavior in multi-modal interactions -workshop aims to bring together researchers, practitioners and experts on the topic of embodiment, to analyze and foster discussion on its efects on user behavior in multi-modal interaction. ACE is aimed at stimulating multidisciplinary discussions on the topic, sharing recent progress, and providing […]
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A Secured Blockchain Framework for Healthcare Data Management System
In the healthcare system, electronic medical records are very critical, and they must be authenticated and verified. During the medical check-up, a large amount of patient medical data is generated which includes reports related to blood, lifethreatening diseases, and personal information such as credit card numbers and addresses. Any privacy breach in patient medical records […]
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Insights Into the Importance of Linguistic Textual Features on the Persuasiveness of Public Speaking
In both professional and private life, there is a growing need for public speaking skills. With this background, our research project’s long-term aims are to develop tools that can analyse public speeches and provide useful feedback. The impact of audio and visual characteristics on the automatic analysis of speech quality has been widely explored in […]
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CAPSULE TRANSFORMER NETWORK FOR DYNAMIC HAND GESTURE RECOGNITION USING MULTIMODAL DATA
In recent years, deep learning techniques have achieved remarkable success in video analysis and more especially in action and gesture recognition. Even though convolutional neural networks (CNNs) remain the most widely used models, they have difficulty in capturing the global contextual information involving spatial and temporal domains or intermodality due to the local feature learning […]
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Fully Residual Unet-based Semantic Segmentation of Automotive Fisheye Images: a Comparison of Rectangular and Deformable Convolutions
Semantic image segmentation is an essential task for autonomous vehicles and self-driving cars where a complete and real-time perception of the surroundings is mandatory. Convolutional Neural Network approaches for semantic segmentation standout over other state-of-the-art solutions due to their powerful generalization ability over unknown data and end-to-end training. Fisheye images are important due to their […]
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Forecasting photovoltaic energy for a winter house using a Hybrid Deep Learning Model
As part of the energy transition, controlling energy consumption is a challenge for everyone. To this end, a number of sustainable solutions are being proposed, notably for BIPV (Building Integrated Photovoltaics) buildings. In addition, artificial intelligence (AI) is an effective tool for analyzing photovoltaic (PV) energy production and consumption data. It will then be possible […]
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Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data
Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, the goal is to eliminate […]
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MR-STGN: Multi-Residual Spatio Temporal Graph Network using Attention Fusion for Patient Action Assessment
Accurate assessment of patient actions plays a crucial role in healthcare as it contributes significantly to disease progression monitoring and treatment effectiveness. However, traditional approaches to assess patient actions often rely on manual observation and scoring, which are subjective and time-consuming. In this paper, we propose an automated approach for patient action assessment using a […]
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EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
Facial expressions play a crucial role in human communication serving as a powerful and impactful means to express a wide range of emotions. With advancements in artificial intelligence and computer vision, deep neural networks have emerged as effective tools for facial emotion recognition. In this paper, we propose EmoNeXt, a novel deep learning framework for […]
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Machine Learning Assistive State of Charge Estimation of Li-Ion Battery
For an effective and economical deployment of battery-powered electric vehicles, mobile phones, laptops, and medical gadgets, the State of Charge (SoC) of the batteries must be properly assessed. It permits a safe operation, have a longer usable battery life, and prevent malfunctions. In this context, the battery management systems provide diverse SoC estimation solutions. However, […]
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Hybradization of Emperical Mode Decomposition and Machine Learning for Categorization of Cardiac Diseases
The arrhythmia is one of the cardiovascular diseases which has several types. In literature, researchers have presented a broad study on the strategies utilized for Electrocardiogram (ECG) signal investigation. Automated arrhythmia detection by analyzing the ECG data is reported using a number of intriguing techniques and discoveries. In order to effectively categorize arrhythmia, a novel […]
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Artificial Intelligence Assistive Fire Detection and Seeing the Invisible Through Smoke using Hyperspectral and Multi-spectral Images
Abstract— The global warming has serious impact on our climate. Due to this, the frequency and the intensity of forest fires is increasing. It has shown serious challenges such as the protection of resources, human and wild life, health, and property. This study focuses on developing an artificial intelligence assistive innovative solution for active fire […]
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