scholarly journals Artificial Neural Networks in Pavement Engineering: A Recent Review

2021 ◽  
Author(s):  
Abhishek Kurian ◽  
Elvin Sunildutt
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Civil Engineering ◽  
Developed Countries ◽  
Pavement Management ◽  
Economic Management ◽  
Learning Techniques ◽  
Pavement Engineering ◽  
Artificial Neural

The application of Artificial Neural Networks (ANN) in civil engineering has increased drastically in the past few years. ANN tools are nowadays used commonly in developed countries over various fields of civil engineering like geotechnical, structural, traffic, pavement engineering etc. This paper deals with the review of recent advancements and utilization of ANNs in pavement engineering. The review will focus on pavement performance prediction, maintenance strategies, distress intensity detection through deep learning techniques, pavement condition index prediction etc. The use of ANNs in pavement management systems are expected to furnish a systematic schedule and economic management strategies in the field of pavement engineering. The use of ANNs combined with deep learning techniques help to address complex problems in pavement engineering and pave the way to a sustainable future.

Heliport Detection Using Artificial Neural Networks

2020 ◽  
Vol 86 (9) ◽  
pp. 541-546
Author(s):  
Emre Başeski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Learning Approaches ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Critical Technology ◽  
Artificial Neural ◽  
Military Facilities

Automatic image exploitation is a critical technology for quick content analysis of high-resolution remote sensing images. The presence of a heliport on an image usually implies an important facility, such as military facilities. Therefore, detection of heliports can reveal critical information about the content of an image. In this article, two learning-based algorithms are presented that make use of artificial neural networks to detect H-shaped, light-colored heliports. The first algorithm is based on shape analysis of the heliport candidate segments using classical artificial neural networks. The second algorithm uses deep-learning techniques. While deep learning can solve difficult problems successfully, classical-learning approaches can be tuned easily to obtain fast and reasonable results. Therefore, although the main objective of this article is heliport detection, it also compares a deep-learning based approach with a classical learning-based approach and discusses advantages and disadvantages of both techniques.

scholarly journals Sistem Deteksi Infeksi COVID-19 Pada Hasil X-Ray Rontgen menggunakan Algoritma Convolutional Neural Network (CNN)

2021 ◽  
Vol 4 (2) ◽  
pp. 217-227
Author(s):  
Muhammad Saiful ◽  
◽  
Lalu Muhammad Samsu ◽  
Fathurrahman Fathurrahman ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Infection Detection ◽  
Learning Techniques ◽  
Artificial Neural

The development of the world's technology is growing rapidly, especially in the field of health in the form of detection tools of various objects, including disease objects. The technology in point is part of artificial intelligence that is able to recognize a set of imagery and classify automatically with deep learning techniques. One of the deep learning networks widely used is convolutional neural network with computer vision technology. One of the problems with computer vision that is still developing is object detection as a useful technology to recognize objects in the image as if humans knew the object of the image. In this case, a computer machine is trained in learning using artificial neural networks. One of the sub types of artificial neural networks that are able to handle computer vision problems is by using deep learning techniques with convolutional neural network algorithms. The purpose of this research is to find out how to design the system, the network architecture used for COVID-19 infection detection. The system cannot perform detection of other objects. The results of COVID-19 infection detection with convolutional neural network algorithm show unlimited accuracy value that ranges from 60-99%.

SỬ DỤNG DEEP NEURAL NETWORKS PHÁT HIỆN GAI ĐỘNG KINH TRONG BẢN GHI EEG

2020 ◽  
pp. 77-84
Author(s):  
Xuyến
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Artificial Neural

Deep Neural Networks là một thuật toán dạy cho máy học, là phương pháp nâng cao của mạng nơ-ron nhân tạo (Artificial Neural Networks) nhiều tầng để học biểu diễn mô hình đối tượng. Bài báo trình bày phương pháp để phát hiện spike tự động, giải quyết bài toán cho các bác sỹ khi phân tích dữ liệu khổng lồ được thu thập từ bản ghi điện não để xác định một khu vực của não gây ra chứng động kinh. Hàng triệu mẫu được phân tích thủ công đã được đào tạo lại để tìm các gai liêp tiếp phát ra từ vùng não bị ảnh hưởng. Để đánh giá phương pháp đề xuất, tác giả đã xây dựng hệ thống trong đó sử dụng một số mô hình deep learning đưa vào thử nghiệm hỗ trợ các bác sỹ khám phát hiện và chẩn đoán sớm bệnh.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

International Diversified Portfolio Optimization With Artificial Neural Networks

2019 ◽  
pp. 201-223
Author(s):  
Mehmet Fatih Bayramoglu ◽  
Cagatay Basarir
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Learning Techniques ◽  
Portfolio Performance ◽  
Foreign Companies ◽  
Developed Markets ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Diversified Portfolio

Investing in developed markets offers investors the opportunity to diversify internationally by investing in foreign firms. In other words, it provides the possibility of reducing systematic risk. For this reason, investors are very interested in developed markets. However, developed are more efficient than emerging markets, so the risk and return can be low in these markets. For this reason, developed market investors often use machine learning techniques to increase their gains while reducing their risks. In this chapter, artificial neural networks which is one of the machine learning techniques have been tested to improve internationally diversified portfolio performance. Also, the results of ANNs were compared with the performances of traditional portfolios and the benchmark portfolio. The portfolios are derived from the data of 16 foreign companies quoted on NYSE by ANNs, and they are invested for 30 trading days. According to the results, portfolio derived by ANNs gained 10.30% return, while traditional portfolios gained 5.98% return.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

Artificial Neural Networks and Learning Techniques

2016 ◽  
pp. 89-112
Author(s):  
Pushpendu Kar ◽  
Anusua Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Work ◽  
The Neural Network ◽  
Learning Techniques ◽  
Learning Procedure ◽  
Processing Component ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems

2020 ◽  
pp. 13-38
Author(s):  
Melda Yucel ◽  
Sinan Melih Nigdeli ◽  
Gebrail Bekdaş
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Properties ◽  
Structural Design ◽  
Civil Engineering ◽  
Back Propagation ◽  
Training Algorithm ◽  
Design Problems ◽  
Engineering Problems ◽  
Artificial Neural

This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.

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