Deep learning in robotics: a review of recent research

HA Pierson, MS Gashler - Advanced Robotics, 2017 - Taylor & Francis
Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …

A comprehensive review on the application of artificial neural networks in building energy analysis

SR Mohandes, X Zhang, A Mahdiyar - Neurocomputing, 2019 - Elsevier
This paper presents a comprehensive review of the significant studies exploited Artificial
Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of …

An intelligent driven deep residual learning framework for brain tumor classification using MRI images

H Mehnatkesh, SMJ Jalali, A Khosravi… - Expert Systems with …, 2023 - Elsevier
Brain tumor classification is an expensive complicated challenge in the sector of clinical
image analysis. Machine learning algorithms enabled radiologists to accurately diagnose …

Automated identification of diabetic retinopathy using deep learning

R Gargeya, T Leng - Ophthalmology, 2017 - Elsevier
Purpose Diabetic retinopathy (DR) is one of the leading causes of preventable blindness
globally. Performing retinal screening examinations on all diabetic patients is an unmet …

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

G Arango-Argoty, E Garner, A Pruden, LS Heath… - Microbiome, 2018 - Springer
Background Growing concerns about increasing rates of antibiotic resistance call for
expanded and comprehensive global monitoring. Advancing methods for monitoring of …

Performance evaluation of deep learning based network intrusion detection system across multiple balanced and imbalanced datasets

A Meliboev, J Alikhanov, W Kim - Electronics, 2022 - mdpi.com
In the modern era of active network throughput and communication, the study of Intrusion
Detection Systems (IDS) is a crucial role to ensure safe network resources and information …

Deep convolutional neural network based detection system for real-time corn plant disease recognition

S Mishra, R Sachan, D Rajpal - Procedia Computer Science, 2020 - Elsevier
Corn is one of the most popular food grains in the India and crop loss due to diseases
substantially affects the Indian economy and threatens the food availability. Recent access …

Using deep and convolutional neural networks for accurate emotion classification on DEAP data

S Tripathi, S Acharya, R Sharma, S Mittal… - Proceedings of the …, 2017 - ojs.aaai.org
Emotion recognition is an important field of research in Brain Computer Interactions. As
technology and the understanding of emotions are advancing, there are growing …

FlowPic: A generic representation for encrypted traffic classification and applications identification

T Shapira, Y Shavitt - IEEE Transactions on Network and …, 2021 - ieeexplore.ieee.org
Identifying the type of a network flow or a specific application has many advantages, such
as, traffic engineering, or to detect and prevent application or application types that violate …

Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images

D Manno, G Cipriani, G Ciulla, V Di Dio… - Energy Conversion and …, 2021 - Elsevier
Losses of electricity production in photovoltaic systems are mainly caused by the presence
of faults that affect the efficiency of the systems. The identification of any overheating in a …