Electric bicycles (e-bikes) have gained considerable popularity due to their environmentally friendly nature and suitability as a mode of transportation. However, they face challenges related to manual switches for po...
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During the COVID-19 pandemic, most countries have experienced some form of remote education through video conferencing software platforms. However, these software platforms fail to reduce immersion and replicate the c...
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ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
During the COVID-19 pandemic, most countries have experienced some form of remote education through video conferencing software platforms. However, these software platforms fail to reduce immersion and replicate the classroom experience. The currently emerging Metaverse addresses many of such limitations by offering blended physical-digital environments. This paper aims to assess how the Metaverse can support and improve e-learning. We first survey the latest applications of blended environments in education and highlight the primary challenges and opportunities. Accordingly, we derive our proposal for a virtual-physical blended classroom configuration that brings students and teachers into a shared educational Metaverse. We lOcus on the system architecture of the Metaverse classroom to achieve real-time synchronization of a large number of participants and activities across physical (mixed reality classrooms) and virtual (remote VR platform) learning spaces. Our proposal attempts to transform the traditional physical classroom into virtual-physical cyberspace as a new social network of learners and educators connected at an unprecedented scale.
This study addresses the optimization of deep learning and transfer learning models for Traffic Sign Recognition (TSR) under diverse environmental conditions and class imbalances. Traffic Sign Recognition Database (TS...
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Multiple Sclerosis (MS) is a chronic autoimmune disorder that targets the central nervous system, resulting in considerable physical and cognitive disabilities. Timely and precise diagnosis is essential for the effect...
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Artificial Intelligence (AI) part of Federated learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has no...
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The traffic and medical authorities utilize accident severity prediction through social media postings for better administration and robust emergency response. Deep learning-based models can be used for accident sever...
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作者：
Ghosh, SambhramThenmozhi, M.
School of Computing College of Engineering and Technology Department of Networking and Communications SRM Nagar Kattankulathur Tamil Nadu Chengalpattu District 603203 India
Ayurveda has been an integral part of ancient Indian medicine and it still plays an important role in the creation of Homeopathic medicines. In this paper we have introduced a comprehensive system for detecting, gradi...
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Brain tumour detection and classification are crucial for early diagnosis, improving patient survival rates. Magnetic Resonance Imaging (MRI) offers non-invasive, high-resolution brain images, but manual interpretatio...
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This study presents a deep learning model created for enabling comprehensive wildfire control by seamlessly combining satellite images, weather data and terrain details. Current systems face challenges in comprehensiv...
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ISBN:
(纸本)9798350386356;9798350386349
This study presents a deep learning model created for enabling comprehensive wildfire control by seamlessly combining satellite images, weather data and terrain details. Current systems face challenges in comprehensively analyzing these factors due to limitations in data integration, dynamic fire behavior prediction, and post-fire ecological impact evaluation. By improving detection and accurate assessment of impact, the system addresses all aspects of wildfire management from forecasting to post event analysis. The model integrates soil quality examination and vegetation regrowth simulation Using image analysis and state of the art deep learning methods. This holistic approach of Image analysis employs Convolutional Neural Networks (CNN) for predicting wildfire risk and Recurrent Neural Networks (RNN) for assessing soil and hydrological effects. This adaptable approach, which aims to transform the way fire control is done, can be readily adjusted to changing conditions and takes correlations between different aspects into account. It surpasses conventional techniques by including soil quality analysis, vegetation regrowth modeling, and vegetation damage evaluation. The adaptable nature of this method proves invaluable, in lessening the impact of wildfires with a focus, on evaluating vegetation damage and promoting restoration.
In the domain of Interactive Systems (IS), machine learning (ML) emerges as a profound, user-friendly, and accurate approach to enhancing emotional intelligence in digital systems. This research work focuses on facial...
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