Publications
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Reinforcement Learning for Optimal HVAC Control: from Theory to Real-world Applications
The HVAC system accounted for a significant portion of the building’s energy consumption, resulting in enormous CO2 emissions. Among the numerous HVAC control methods, reinforcement learning (RL) gives the ability to control complex systems without requiring an explicit model of the building’s thermal dynamics. This study conducted a concise review of previous research on the […]
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Occupancy Prediction in Buildings Using Cascaded LSTM Model
Buildings are one of the prominent sectors among global primary energy consumption. A large portion of this energy consumption is influenced by occupancy interaction with the buildings. Occupancy prediction in buildings without intruding their privacy helps to enhance the building energy management. Due to the complex relations of the inputs and the temporal dependency, modeling […]
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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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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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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 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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Developing innovation culture through the transformation of a master learning path
Every year, around one thousands of postgraduate students attends CESI preparing expertise master courses on diverse aspects of management. During this year, MS® postgraduate students work in companies and spend one week per month at CESI. For each student, one of the issue of the year is to write a professional thesis. On student size, […]
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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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Toward Universal Creativity Assessment by Untrained Judges
The purpose of a creativity session being first to generate many ideas, how to fast and reliably assess each one’s creativity, even with non-creativity experts? Creative people appearing not only to be good at generating ideas but also at implicitly evaluating them, improving people’s assessment would further improve their own creativity. This paper investigates canonical […]
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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 […]