LITERATURE REVIEW: PENGENALAN WAJAH MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK

Facial recognition to detect the identity of the gallon user's face in honesty in the school environment has many methods such as local, global, and hybrid approaches. The main problem of using the gallon of honesty is that the program uses the Self-service system, which is a self-service system, where the buyer serves itself unattended. The water charging activity is still found by users who are dishonest, such as taking water but not putting money into the place that has been provided, the thing that should be when the user fills the water then the user must also enter Money into the box provided. Because of the absence of supervision in this program of honesty then it is difficult to know who is dishonest in order to be able to do prevention for the dishonesty that has occurred when using the gallon of honesty program. Facial recognition using the Convolutional Neural Network (CNN) method to classify images. A literature review is used to analyse and focus on techniques in conducting facial recognition on the use of gallons of honesty. Keywords: facial recognition, convolutional neural network methods, a gallon of honest

Convolutional Neural Network Transfer Learning for Robust Face Recognition in NAO Humanoid Robot

Applications of transfer learning for convolutional neural networks (CNNs) have shown to be an efficient alternative for solving recognition tasks rather than designing and training a new neural network from scratch. However, there exists several popular CNN architectures available for various recognition tasks. Therefore, choosing an appropriate network for a specific recognition task, specifically designed for a humanoid robotic platform, is often challenging. This study evaluates the performance of two well-known CNN architectures; AlexNet, and VGG-Face for a face recognition task. This is accomplished by applying the transfer learning concept to the networks pre-trained for different recognition tasks. The proposed face recognition framework is then implemented on a humanoid robot known as NAO to demonstrate the practicality and flexibility of the algorithm. The results suggest that the proposed pipeline shows excellent performance in recognizing a new person from a single example image under varying distance and resolution conditions usually applicable to a mobile humanoid robotic platform

О разработке адаптивной образовательной платформы с использованием технологий машинного обучения

В исследовании рассматривается создание адаптивной автоматизированной платформы для обучения иностранному языку на примере английского язык

Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 minutes. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps

Human activity recognition by using convolutional neural network

In recent years, many researchers have studied the HAR (Human Activity Recognition) system. HAR using smart home sensor is based on computing in smart environment, and intelligent surveillance system conducts intensive research on peripheral support life. The previous system studied in some of the activities is a fixed motion and the methodology is less accurate. In this paper, vision-based studies using thermal imaging cameras improve the accuracy of motion recognition in intelligent surveillance systems. We use one of the deep learning architectures widely used in image recognition systems called Convolutional Neural Networks (CNN). Therefore, we use CNN and thermal cameras to provide accuracy and many features through the proposed method

Classification of red blood cell shapes in flow using outlier tolerant machine learning

The manual evaluation, classification and counting of biological objects demands for an enormous expenditure of time and subjective human input may be a source of error. Investigating the shape of red blood cells (RBCs) in microcapillary Poiseuille flow, we overcome this drawback by introducing a convolutional neural regression network for an automatic, outlier tolerant shape classification. From our experiments we expect two stable geometries: the so-called `slipper' and `croissant' shapes depending on the prevailing flow conditions and the cell-intrinsic parameters. Whereas croissants mostly occur at low shear rates, slippers evolve at higher flow velocities. With our method, we are able to find the transition point between both `phases' of stable shapes which is of high interest to ensuing theoretical studies and numerical simulations. Using statistically based thresholds, from our data, we obtain so-called phase diagrams which are compared to manual evaluations. Prospectively, our concept allows us to perform objective analyses of measurements for a variety of flow conditions and to receive comparable results. Moreover, the proposed procedure enables unbiased studies on the influence of drugs on flow properties of single RBCs and the resulting macroscopic change of the flow behavior of whole blood.Comment: 15 pages, published in PLoS Comput Biol, open acces

Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images

Although the orbit analysis (orbit shape and size) is commonly used to diagnose rotating machinery, the diagnosis heavily depends on the expert knowledge or experience due to the difficulties of extracting mathematical features for data-driven approaches. Therefore, in this paper, we propose an autonomous orbit pattern recognition algorithm using the deep learning method on shaft orbit shape images. In details, the convolutional neural network is implemented to construct weights between neurons and to generate the entire structure of the neural network. Then, the created network enables us to classify fault modes of rotating machinery via orbit images. Furthermore, we demonstrate the proposed framework through a rotating testbed

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa