scholarly journals Multiple Feature Dependency Detection for Deep Learning Technology—Smart Pet Surveillance System Implementation

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1387
Author(s):  
Ming-Fong Tsai ◽  
Pei-Ching Lin ◽  
Zi-Hao Huang ◽  
Cheng-Hsun Lin
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Technologies ◽  
Learning Technology ◽  
Image Identification ◽  
Detection And Identification ◽  
Multiple Feature ◽  
Feature Dependency ◽  
Traditional Image ◽  
Dependency Detection

Image identification, machine learning and deep learning technologies have been applied in various fields. However, the application of image identification currently focuses on object detection and identification in order to determine a single momentary picture. This paper not only proposes multiple feature dependency detection to identify key parts of pets (mouth and tail) but also combines the meaning of the pet’s bark (growl and cry) to identify the pet’s mood and state. Therefore, it is necessary to consider changes of pet hair and ages. To this end, we add an automatic optimization identification module subsystem to respond to changes of pet hair and ages in real time. After successfully identifying images of featured parts each time, our system captures images of the identified featured parts and stores them as effective samples for subsequent training and improving the identification ability of the system. When the identification result is transmitted to the owner each time, the owner can get the current mood and state of the pet in real time. According to the experimental results, our system can use a faster R-CNN model to improve 27.47%, 68.17% and 26.23% accuracy of traditional image identification in the mood of happy, angry and sad respectively.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

FACE DETECTION AND IDENTIFICATION USING DEEP LEARNING TECHNOLOGY WITH CGAN AND SRGAN

2021 ◽  
Author(s):  
Phan Anh Cang ◽  
To Huynh Thien Truong ◽  
Cao Hùng Phi ◽  
Phan Thuong Cang
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Learning Technology ◽  
Detection And Identification

scholarly journals An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level

2019 ◽  
Vol 9 (16) ◽  
pp. 3414 ◽  
Author(s):  
Ren-Hung Hwang ◽  
Min-Chun Peng ◽  
Van-Linh Nguyen ◽  
Yu-Lun Chang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Technologies ◽  
Word Embedding ◽  
Detection Systems ◽  
Real Time Analysis ◽  
Detection Delay ◽  
Novel Approach ◽  
Prior Literature

Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art systems also face tremendous challenges to satisfy real-time analysis requirements due to the major delay of the flow-based data preprocessing, i.e., requiring time for accumulating the packets into particular flows and then extracting features. If detecting malicious traffic can be done at the packet level, detecting time will be significantly reduced, which makes the online real-time malicious traffic detection based on deep learning technologies become very promising. With the goal of accelerating the whole detection process by considering a packet level classification, which has not been studied in the literature, in this research, we propose a novel approach in building the malicious classification system with the primary support of word embedding and the LSTM model. Specifically, we propose a novel word embedding mechanism to extract packet semantic meanings and adopt LSTM to learn the temporal relation among fields in the packet header and for further classifying whether an incoming packet is normal or a part of malicious traffic. The evaluation results on ISCX2012, USTC-TFC2016, IoT dataset from Robert Gordon University and IoT dataset collected on our Mirai Botnet show that our approach is competitive to the prior literature which detects malicious traffic at the flow level. While the network traffic is booming year by year, our first attempt can inspire the research community to exploit the advantages of deep learning to build effective IDSs without suffering significant detection delay.

scholarly journals Real Time Attendance Marking System

2020 ◽  
Vol 5 (8) ◽  
pp. 1112-1116
Author(s):  
Riya John ◽  
Akhilesh. s ◽  
Gayathri Geetha Nair ◽  
Jeen Raju ◽  
Krishnendhu. B
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Educational Institution ◽  
Rfid Tags ◽  
Learning Technology ◽  
Business Organizations ◽  
Data Set ◽  
Real Time Image ◽  
Time Image ◽  
Excel File

Attendance management is an important procedure in an educational institution as well as in business organizations. Most of the available methods are time consuming and manipulative. The traditional method of attendance management is carried out in handwritten registers. Other than the manual method, there exist biometric methods like fingerprint and retinal scan, RFID tags, etc. All of these methods have disadvantages, therefore, in order to avoid these difficulties here, we introduce a new method for attendance management using deep learning technology. Using deep learning we can easily train a data-set. Real-time face algorithms are used and recognized faces of students in real-time while attending lectures. This system aims to be less time- consuming in comparison to the existing system of marking attendance.The program runs on anaconda flask server.Here real time image is captured using mobile phone camera. The faces on the image of the persons are then recognized and attendance is marked on an excel file.

Curved Hull Plate Classification for Determining Forming Method using Deep Learning

2019 ◽  
Vol 35 (4) ◽  
pp. 328-337
Author(s):  
Byeongseop Kim ◽  
Seunghyeok Son ◽  
Cheolho Ryu ◽  
Jong Gye Shin
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Three Dimensional ◽  
Learning Technologies ◽  
Learning Technology ◽  
Production Plan ◽  
Forming Process ◽  
Thermal Forming ◽  
Constant Size ◽  
Plate Shape

Curved hull plate forming, the process of forming a flat plate into a curved surface that can fit into the outer shell of a ship’s hull, can be achieved through either cold or thermal forming processes, with the latter processes further subcategorizable into line or triangle heating. The appropriate forming process is determined from the plate shape and surface classification, which must be determined in advance to establish a precise production plan. In this study, an algorithm to extract two-dimensional features of constant size from three-dimensional design information was developed to enable the application of machine and deep learning technologies to hull plates with arbitrary polygonal shapes. Several candidate classifiers were implemented by applying learning algorithms to datasets comprising calculated features and labels corresponding to various hull plate types, with the performance of each classifier evaluated using cross-validation. A classifier applying a convolution neural network as a deep learning technology was found to have the highest prediction accuracy, which exceeded the accuracies obtained in previous hull plate classification studies. The results of this study demonstrate that it is possible to automatically classify hull plates with high accuracy using deep learning technologies and that a perfect level of classification accuracy can be approached by obtaining further plate data.

scholarly journals Real-Time Psychological Stress Detection According to ECG Using Deep Learning

2021 ◽  
Vol 11 (9) ◽  
pp. 3838
Author(s):  
Pengfei Zhang ◽  
Fenghua Li ◽  
Rongjian Zhao ◽  
Ruishi Zhou ◽  
Lidong Du ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Psychological Stress ◽  
Short Term Memory ◽  
High Stress ◽  
High Accuracy ◽  
Learning Technology ◽  
Monitoring Methods ◽  
Conventional Methods ◽  
Detection Of Stress

Today, excessive psychological stress has become a universal threat to humans. That stress can heavily affect work and study when a person repeatedly is exposed to high stress. If that exposure is long enough, it can even cause cardiovascular disease and cancer. Therefore, both monitoring and managing of stress is imperative to reduce the bad outcomes from excessive psychological stress. Conventional monitoring methods firstly extract the characteristics of the RR interval of an electrocardiogram (ECG) from a time domain and a frequency domain, then use machine learning models, like SVM, random forest, and decision tree, to distinguish the level of that stress. The biggest limitation of using these methods is that at least one minute of ECG data and other signals are indispensable to ensure the high accuracy of the results. This will greatly affect the real-time application of the models. To satisfy real-time detection of stress with high accuracy, we proposed a framework based on deep learning technology. The proposed monitoring framework is based on convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM). To evaluate the performance of this network, we conducted the experiments applying conventional methods. The data for the 34 subjects were collected on the server platform created by the group at the Institute of Psychology of the Chinese Academy of Sciences and our group. The accuracy of the proposed framework was up to 0.865 on three levels of stress using a 10 s ECG signal, a 0.228 improvement compared with conventional methods. Therefore, our proposed framework is more suitable for real-time applications.

scholarly journals Real-Time Object Detection For Wayang Punakawan Identification Using Deep Learning

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Afandi Nur Aziz Thohari ◽  
Rifki Adhitama
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Three Dimensional ◽  
Learning Technology ◽  
Single Shot ◽  
Training Process ◽  
Real Time System ◽  
Good Ability ◽  
Object Based ◽  
Wayang Kulit

Indonesia is a country that has a variety of cultures, one of which is wayang kulit. This typical javanese performance art must continue to be preserved so that to be known by future generations. There are many wayang figures in Indonesia, and the most famous is punakawan. Wayang punakawan consists of four character namely semar, gareng petruk, and bagong. To preserve wayang punakawan to be known by the next generation, then in this study created a system that is able to identify real-time punakawan object using deep learning technology. The method that used is Single Shot Multiple Detector (SSD) as one of the models of deep learning that has a good ability in classifying data with three-dimensional structures such as real-time video. SSD model with MobileNet layer can work in slight computation, so that it can be run in real-time system. To classify object there are two steps that must be done such as training process and testing process. Training process takes 28 hours with 100.000 steps of iteration.The result of training process is a model which used to identify object. Based on the test result obtained an accuracy to detect object was 98,86%. This prove that the system has been able to optimize object in real-time accurately.

scholarly journals Review of Machine Learning Technologies and Neural Networks in Drug Synergy Combination pharmacological research

2020 ◽  
Vol 6 (3) ◽  
pp. 27-32
Author(s):  
Artur S. Ter-Levonian ◽  
Konstantin A. Koshechkin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Technologies ◽  
Learning Technology ◽  
Preclinical Research ◽  
Processing Technologies ◽  
Drug Synergy ◽  
Positive Effects ◽  
The Past ◽  
The Right

Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting. Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases. Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019). Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies. Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research.

scholarly journals Deep Learning based Cell Classification in Imaging Flow Cytometer

2021 ◽  
Vol 1 (2) ◽  
pp. 18-27
Author(s):  
Yi Gu ◽  
Aiguo Chen ◽  
Xin Zhang ◽  
Chao Fan ◽  
Kang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Throughput ◽  
Cell Sorting ◽  
Future Trend ◽  
Flow Cytometer ◽  
Learning Technologies ◽  
Cell Analysis ◽  
Cell Classification ◽  
Cell Image

Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area.

scholarly journals Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 650
Author(s):  
Minki Kim ◽  
Sunwon Kang ◽  
Byoung-Dai Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Hair Loss ◽  
Medical Image Analysis ◽  
Density Measurement ◽  
Hair Follicles ◽  
Learning Technologies ◽  
Tissue Imaging ◽  
Learning Technology ◽  
Hair Density

Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.

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