Hybrid Deep Learning Approach for Efficient Outdoor Parking Monitoring in Smart Cities

2021 ◽  
pp. 463-474
Author(s):  
Alberto Tellaeche Iglesias ◽  
Iker Pastor-López ◽  
Borja Sanz Urquijo ◽  
Pablo García-Bringas
Keyword(s):  
Deep Learning ◽  
Smart Cities ◽  
Learning Approach

scholarly journals A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 14
Author(s):  
Saurav Kumar ◽  
Drishti Yadav ◽  
Himanshu Gupta ◽  
Om Prakash Verma ◽  
Irshad Ahmad Ansari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waste Management ◽  
Organic Waste ◽  
Learning Strategy ◽  
Smart Cities ◽  
Learning Approach ◽  
Generalization Capability ◽  
Novel Approach ◽  
Waste Segregation ◽  
Glass Metal

The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.

Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-based Smart Cities

2021 ◽  
pp. 1-1
Author(s):  
Mohamed Abdel-Basset ◽  
Hossam Hawash ◽  
Ripon K. Chakrabortty ◽  
Michael Ryan
Keyword(s):  
Deep Learning ◽  
Energy Management ◽  
Smart Cities ◽  
Learning Approach ◽  
Smart Energy Management

scholarly journals Passenger Control in Smart Cities Using Deep Learning

2020 ◽  
Vol 5 (7) ◽  
pp. 1428-1431
Author(s):  
Delna T D ◽  
Dhanya P Pauly ◽  
Dona Johnson ◽  
Jesta Jose
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Public Transport ◽  
Smart City ◽  
Smart Cities ◽  
Average Weight ◽  
Learning Approach ◽  
The Public ◽  
Day By Day ◽  
Growing Population

In the current smart city background, people are facing a lot of accidents at the major traffic points of the business towns due to growing population and vehicles growth in smart and metropolitan cities.In this method we consider the auto taxies as well as the public transport. We know that due to the overload in the vehicles the accidents are increasing day by day so using this method the number of accidents be able to be avoided or reduced. This system is introducing the deep learning approach to find the overload in vehicles. We are considering the luggage that is taken along with the passenger and an average weight is given for the load. Then it is combined with the number of passenger and system will predict whether the vehicle is overload or not. Mainly because of using deep learning concepts we can increase the speed of the process and the efficiency. The system will analyse the number of passengers using real time videos using camera and system detect and compare with the overloading conditions to avoidaccidents.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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