IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning

Visual place recognition: A survey from deep learning perspective

Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices’ overhead by deploying computation offloading and task caching strategy reasonably

Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.

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Yang Li

Papers

Visual saliency guided complex image retrieval