scholarly journals Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

2021 ◽  
Author(s):  
Xuan Zhang ◽  
Guohui Wang
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Accurate Method ◽  
Photometric Stereo ◽  
The Body ◽  
Collection System ◽  
Testing Methods ◽  
Pose Detection ◽  
Stereo Algorithm

There are hundreds of welded studs in a car. The posture of a welded stud determines the quality of the body assembly thus affecting the safety of cars. It is crucial to detect the posture of the welded studs. Considering the lack of accurate method in detecting the position of welded studs, this paper aims to detect the weld stud’s pose based on photometric stereo and neural network. Firstly, a machine vision-based stud dataset collection system is built to achieve the stud dataset labeling automatically. Secondly, photometric stereo algorithm is applied to estimate the stud normal map which as input is fed to neural network. Finally, we improve a lightweight YOLOv4 neural network which is applied to achieve the detection of stud position thus overcoming the shortcomings of traditional testing methods. The research and experimental results show that the stud pose detection system designed achieves rapid detection and high accuracy positioning of the stud. This research provides the foundation combining the photometric stereo and deep learning for object detection in industrial production.

scholarly journals Glucose level detection system in glucose solution using TCS3200 sensor with If-Else method

2021 ◽  
Vol 13 (2) ◽  
pp. 110-116
Author(s):  
Kemal Thoriq Al-Azis ◽  
Alfian Ma'arif ◽  
Sunardi Sunardi ◽  
Fatma Nuraisyah ◽  
Apik Rusdiarna Indrapraja
Keyword(s):  
Glucose Level ◽  
Color Change ◽  
Glucose Solution ◽  
Detection System ◽  
The Body ◽  
Glucose Levels ◽  
Non Invasive ◽  
Eye Health ◽  
Sugar Levels

Early and routine examination of glucose levels plays an important role in preventing and controlling diabetes mellitus and maintaining the quality of life. Checking blood sugar levels by hurting the body (invasive) can lead to infections caused by needles. As an alternative, the examination is carried out in a non-invasive way using excretory fluid in the form of urine, which is reacted with Benedict's solution that create a color change. Experts in the laboratory only carry out an examination using non-invasive methods because in determining glucose levels, it requires accuracy and eye health factors. Therefore, a glucose level detection system was created using a sample of glucose solution to determine the system's parameters using the if-else method. The glucose level detection system is conducted by mixing the glucose solution with Benedict's solution to produce a color change. Then the reaction results are read by the TCS3200 sensor and processed by Arduino to be classified, according to predetermined parameters. The decision results based on the classification of the glucose level parameters that have been determined are displayed on a 16x2 LCD. The results achieved in this study on 10 samples of glucose solution that were tested and processed by the if-else method were successfully read and classified based on predetermined parameters.

scholarly journals Design of a Fracture Detection System based on Deep Program in a Convolutional Neural Network

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 509-518
Author(s):  
Payman Hussein Hussan ◽  
Syefy Mohammed Mangj Al-Razoky ◽  
Hasanain Mohammed Manji Al-Rzoky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bone Fractures ◽  
Detection System ◽  
Training Data ◽  
Learning Models ◽  
Fracture Detection ◽  
Data Set ◽  
Final Fracture

This paper presents an efficient method for finding fractures in bones. For this purpose, the pre-processing set includes increasing the quality of images, removing additional objects, removing noise and rotating images. The input images then enter the machine learning phase to detect the final fracture. At this stage, a Convolutional Neural Networks is created by Genetic Programming (GP). In this way, learning models are implemented in the form of GP programs. And evolve during the evolution of this program. Then finally the best program for classifying incoming images is selected. The data set in this work is divided into training and test friends who have nothing in common. The ratio of training data to test is equal to 80 to 20. Finally, experimental results show good results for the proposed method for bone fractures.

scholarly journals Detection of Various Neoplasm’s in Medical Images Using Edge Detection and Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 59-62
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
The Body ◽  
Internal Cavity ◽  
Cell Detection ◽  
The People ◽  
Abnormal Cells ◽  
Occurrence Matrix

Brain tumor is one in all the extraordinary illness causes death among the people. Neoplasm is associate unconfined expansion of tissue in any neighborhood of the body. During the process have a tendency to tend to stand live taking man photos as input; resonance imaging that is guided into internal cavity of brain and offers the entire image of brain. In this paper brain tumor detection system is proposed. Here bunch methodology supported intensity was enforced. The Probabilistic Neural Network square measure used to identify the various levels of tumor like Malignant, Benign or traditional. PNN with Radial Basis are used for classification and segmentation of cells. In order to classify the normal or abnormal cells, proper decision need to be taken. This could be done in 2 levels: Gray-Level Co-occurrence Matrix and the classification are performed based on Neural Networks. The tumor cell detection is manually performed by the schematic methodology for X-radiation.

scholarly journals Combining Wavelet Statistical Texture And Recurrent Neural Network For Tumour Detection And Classification Over MRI

2019 ◽  
Vol 8 (6) ◽  
pp. 3769-3778
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Recurrent Neural Network ◽  
Detection System ◽  
Region Growing ◽  
Texture Features ◽  
Noise Removal ◽  
The Body ◽  
Mr Images ◽  
The Brain

Brain tumor is one of the major causes of death among other types of the cancer because Brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper we have introduced brain tumor detection system based on combining wavelet statistical texture features and recurrent neural network (RNN). Basically, the system consists of four phases such as (i) feature extraction (ii) feature selection (iii) classification and (iii) segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on sparse principle component analysis (SPCA) approach. The next step is to classify the brain image using Recurrent Neural Network (RNN). After classification, proposed system extracts tumor region from MRI images using modified region growing segmentation algorithm (MRG). This technique has been tested against the datasets of different patients received from muthu neuro center hospital. The experimentation result proves that the proposed system achieves the better result compared to the existing approaches

scholarly journals A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks

2020 ◽  
Vol 4 (5) ◽  
pp. 61-74
Author(s):  
Rabie A. Ramadan ◽  
Kusum Yadav
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Process ◽  
Detection Accuracy ◽  
Iot Security ◽  
Shuffled Frog Leaping

Nowadays, IoT has been widely used in different applications to improve the quality of life. However, the IoT becomes increasingly an ideal target for unauthorized attacks due to its large number of objects, openness, and distributed nature. Therefore, to maintain the security of IoT systems, there is a need for an efficient Intrusion Detection System (IDS). IDS implements detectors that continuously monitor the network traffic. There are various IDs methods proposed in the literature for IoT security. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time, this paper proposes a hybrid IDS system where a pre-processing phase is utilized to reduce the required time and feature selection as well as the classification is done in a separate stage. The feature selection process is done by using the Enhanced Shuffled Frog Leaping (ESFL) algorithm and the selected features are classified using Light Convolutional Neural Network with Gated Recurrent Neural Network (LCNN-GRNN) algorithm. This two-stage method is compared to up-to-date methods used for intrusion detection and it over performs them in terms of accuracy and running time due to the light processing required by the proposed method.

Advanced Brain Tumor Detection System

2020 ◽  
Vol 3 (2) ◽  
pp. 31-45
Author(s):  
Monica S. Kumar ◽  
Swathi K. Bhat ◽  
Vaishali R. Thakare
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Language Processing ◽  
Detection System ◽  
The Body ◽  
Tumor Segmentation ◽  
Cancer Type ◽  
Brain Tumor Segmentation ◽  
Secondary Tumor ◽  
Tumor Region

Brain tumor segmentation and detection is one of the most critical parts in the field of medical regions. Tumor is a cancer type that can be visible in any part of the body in case of primary and secondary tumor. The different type of brain tumor is glioma, benign, malignant, meningioma. This research helps in retrieving the tumor region in the brain with the help of 2D MRI images. The system predicts using MATLAB which is a programming platform and analyze the tumor from different method like canny edge, Otsu's binary, fuzzy c-means (FCM), and k-means clustering to improve the borders using the pixel technique. Using convolution neural network (CNN), neural network, and natural language processing, the system detects brain tumor based on the pre-processing and post-processing feature. Moreover, the authors figure out which tumor affected is the most important feature to protect the lifespan in the initial stages. Finally, it acknowledges the result in the mail format to the doctor or patient.

Development of a Awaking Behavior Detection System Using a Neural Network

2008 ◽  
Vol 128 (11) ◽  
pp. 1649-1656 ◽  
Author(s):  
Hironobu Satoh ◽  
Fumiaki Takeda ◽  
Yuhki Shiraishi ◽  
Rie Ikeda
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Behavior Detection

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Sign in / Sign up

Export Citation Format

Share Document