COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and *** and reliable detection of COVID-19 is mandatory to control the sprea...
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COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and *** and reliable detection of COVID-19 is mandatory to control the spread of ***,prediction of COVID-19 spread in near future is also crucial to better plan for the disease *** this purpose,we proposed a robust framework for the analysis,prediction,and detection of *** make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the *** estimates,analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27,*** use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different *** predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well *** parameters of Gaussian distribution,i.e.,maximumtime and width,are determined through a statisticalχ^(2)-fit for the purpose of doubling times after April 21,*** COVID-19 detection,we proposed a novel method based on the Histogram of Oriented Gradients(HOG)and CNN in multi-class classification scenario i.e.,Normal,COVID-19,viral pneumonia *** results show the effectiveness of our framework for reliable prediction and detection of COVID-19.
Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN...
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The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce th...
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The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.
Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge,...
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Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge, this paper harnesses the potential of formal methods and deep reinforcement learning (DRL) for a more comprehensive solution that integrates Generalized Reactivity(1) (GR(1)) synthesis with DRL. The GR(1) synthesis module takes care of high-level task planning, ensuring a vehicle follows a correct-by-construction and verifiable plan for its mission. On the other hand, the DRL model operates as the low-level motion controller, allowing the vehicle to learn from experience and adjust its actions based on real-time sensor feedback. Therefore, the resulting controller for autonomous vehicles is not only guaranteed to finish its designated tasks but also intelligent to handle complex environments. Through comparative experimental studies, we demonstrate that the control program generated by the proposed approach outperforms the ones generated independently utilizing GR(1) reactive synthesis and DRL. IEEE
Trajectory contains spatial-data generated from traces of moving objects like people, animals, etc. Community generated from trajectories portrays common behaviour. Trajectory clustering based on community-detection i...
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Parkinson's disease (PD) is a neurodegenerative disorder which impacts the central nervous system and characterized by neuronal loss, particularly in the substantia nigra area of the brain. PD rendered dopamine-pr...
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Dynamic network architectures that utilize communication, computing, and storage resources at the wireless edge are key to delivering emerging services in next-generation networks (e.g., AR/VR, 3D video, intelligent c...
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Dynamic network architectures that utilize communication, computing, and storage resources at the wireless edge are key to delivering emerging services in next-generation networks (e.g., AR/VR, 3D video, intelligent cars, etc). Network slicing can be significantly enhanced by including dynamically available resources throughout the fog/edge/cloud continuum and using mmWave/THz bands. However, network slicing of dynamic multi-tier computing networks remains under-explored. In this paper, we present a self-learning end-to-end network slicing mechanism (SELF-E2E-NS) that facilitates collaboration between the Infrastructure Provider (InP) and tenants to slice their subscribers' resources (i.e., radio, computing, and storage) as fog resources. To adapt to the uncertain availability of resources at the edge and minimize the risk of non-satisfying service level agreements (SLAs), our slicing mechanism has two operational modes. Operational mode 1 is for joint network slicing (JNS) in which the InP infrastructure is augmented with fog resources and jointly sliced to meet high throughput and delay tolerant requirements. Operational mode 2 is for independent network slicing (INS) in which the InP infrastructure and fog resources are sliced separately to achieve high throughput, low-latency, and high-reliability requirements. Our schemes leverage mmWave/THz, fog/edge/cloud computing, and caching to achieve new service requirements. We design a DQ-E2E-JNS algorithm that uses Deep Dueling network and a MAAC-E2E-INS algorithm based on multi-agent actor-critic, which incorporate service-aware pricing feedback and fog trading matching, respectively. These algorithms find the optimal slice request admission and collaboration policy that maximizes the long-term revenue of the InP and tenants for each mode. The simulation results show that our novel slicing mechanism can serve up to 4 times more requests and effectively exploits different spectrum bands and fog resources to improve re
Understanding the learner’s requirements and status is important for recommending relevant and appropriate learning materials to the learner in personalized learning. For this purpose, the learning recommendatio...
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Evaluating company growth potential has moved away from traditional financial focused ratios and ratios analysis that has origins in the early twentieth-century economics. However, these conventional methods might not...
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Training a high-performance deep neural network requires large amounts of data and computational resources. Protecting the intellectual property (IP) and commercial ownership of a deep model is challenging yet increas...
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