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Using Deep Learning to Predict Stock Movements Direction in Emerging Markets: The Case of Qatar Stock Exchange
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Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
Deep learning approaches have been utilized to predict stocks. In this study, we use convolutional neural network (CNN) to predict stocks direction in Qatar stock exchange (QE) as a case of emerging markets. Prediction in ...
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Objective: Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for accurate seizure prediction. This work proposes a deep convolutional generative adversarial network (DCGAN) to generate ...
Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques
(
MDPI
, 2021 , Article)
Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. ...
Untrained Neural Network Priors for Inverse Imaging Problems: A Survey
(
IEEE Computer Society
, 2023 , Article)
In recent years, advancements in machine learning (ML) techniques, in particular, deep learning (DL) methods have gained a lot of momentum in solving inverse imaging problems, often surpassing the performance provided by ...
A Bi-layered parallel training architecture for large-scale convolutional neural networks
(
IEEE Computer Society
, 2019 , Article)
Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very ...
Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions
(
John Wiley and Sons Ltd
, 2022 , Article)
Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy ...
DCNN-GA: A Deep Neural Net Architecture for Navigation of UAV in Indoor Environment
(2021 , Article)
The applications of unmanned aerial vehicles (UAVs) in military, intelligent transportation, agriculture, rescue operations, natural environment mapping, and many other allied domains has increased exponentially during the ...
DTW based Authentication for Wireless Medical Device Security
(
Institute of Electrical and Electronics Engineers Inc.
, 2018 , Conference Paper)
Wireless medical devices play an important role in providing safety and privacy to patients suffering from major health issues. These light-weight devices can be worn inside or outside the patient's body and provide more ...
ISDNet: AI-enabled Instance Segmentation of Aerial Scenes for Smart Cities
(
Association for Computing Machinery
, 2021 , Article)
Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, and so on, which are necessary for the evolution of smart ...
Deep CNN-Based real-time traffic light detector for self-driving vehicles
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance ...