Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT

2019 ◽  
Author(s):  
Faisal Mehmood ◽  
Israr Ullah ◽  
Shabir Ahmad ◽  
DoHyeun Kim
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Smart Home ◽  
Learning Algorithm ◽  
Detection Mechanism ◽  
Home Appliances ◽  
Deep Learning Algorithm ◽  
Iot Devices ◽  
Smart Home Appliances

scholarly journals Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 279 ◽  
Author(s):  
Bambang Susilo ◽  
Riri Fitri Sari
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Strategies ◽  
Learning Algorithm ◽  
Human Life ◽  
Denial Of Service ◽  
The Internet ◽  
Dos Attacks ◽  
Deep Learning Algorithm ◽  
Iot Devices

The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.

Real-Time Road Monitoring Using Deep Learning Algorithm Deployed on IoT Devices

2021 ◽  
pp. 137-147
Author(s):  
Nilay Nishant ◽  
Ashish Maharjan ◽  
Dibyajyoti Chutia ◽  
P. L. N. Raju ◽  
Ashis Pradhan
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Iot Devices

scholarly journals Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

2020 ◽  
Vol 9 (3) ◽  
pp. 1498-1508
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Detection ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Single Shot ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Rgb Images ◽  
Deep Convolution Network

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.

scholarly journals A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Linghua Zhao ◽  
Zhihua Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Moving Target ◽  
Deep Learning Algorithm

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.

scholarly journals Smart Home System Based on Deep Learning Algorithm

2019 ◽  
Vol 1187 (3) ◽  
pp. 032086
Author(s):  
Yanfei Peng ◽  
Jianjun Peng ◽  
Jiping Li ◽  
Ling Yu
Keyword(s):  
Deep Learning ◽  
Smart Home ◽  
Learning Algorithm ◽  
Deep Learning Algorithm
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