scholarly journals A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Taowen Xiao ◽  
Zijian Cai ◽  
Cong Lin ◽  
Qiong Chen
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Wireless Communication ◽  
Lightweight Design ◽  
Object Identification ◽  
Feature Representation ◽  
Data Set ◽  
Evaluation Standard ◽  
Artificial Intelligence Technology ◽  
Underwater Wireless Communication

Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.

scholarly journals Artificial intelligence in artistic practices

2021 ◽  
pp. 41-50
Author(s):  
Asmati Chibalashvili
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Visual Arts ◽  
Human Condition ◽  
Human Intervention ◽  
Basic Principles ◽  
Generative Art ◽  
Works Of Art ◽  
Artificial Intelligence Technology ◽  
Emotionally Intelligent

The article considers methods of involving artificial intelligence in artistic practices. Based on the analysis of ways to use this technology in visual arts and music, the basic principles of working with artificial intelligence technology are identified, including: imitation of historical art, implemented in projects The Next Rembrandt and Choral; generative art, which is found in the works “Hyperbolic Composition І” and “Hyperbolic Composition ІІ” of S. Eaton and also in the AIVA program (Artificial Intelligence Virtual Artist). The importance of the mechanisms of neurobiology in the process of working with artificial intelligence on the example of the project “Neural Zoo” of S. Crespo, Iamus program, in which the development of musical material is based on the principle of evolution, is stated. In the application Endel and in the opera “Emotionally intelligent” Artificially Intelligent Brainwave Opera» of E. Perlman, a neural network is used to read information about the human condition and its further processing for modification into a sound landscape or image. The development of artificial intelligence and its use in artistic practices opens up new opportunities, expanding both the field of authors of artistic content and attracting new audience. This phenomenon provokes many issues, including: the ability to think artificially of artificial intelligence, the ability to create works of art without human intervention, as well as issues related to copyright.

Library book intelligent recommendation system based on artificial intelligence

2021 ◽  
pp. 1-6
Author(s):  
Lu Pang
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Absolute Error ◽  
Recall Rate ◽  
Data Set ◽  
Evaluation Data ◽  
Artificial Intelligence Technology ◽  
Collaborative Filtering Algorithm ◽  
Book Evaluation ◽  
Evaluation Matrix

In order to improve the accuracy of intelligent recommendation of library books, an intelligent recommendation system of library books based on artificial intelligence was designed. The system uses artificial intelligence technology to clean up and normalize the data, automatically extracts the user’s historical evaluation data of books, divides the whole user space into several similar user clusters through the similar user clustering module, constructs the user book evaluation matrix according to the historical evaluation data, and uses the hybrid collaborative filtering algorithm which integrates user based and project-based to predict each user a book evaluation matrix of similar user clusters was used to realize the intelligent recommendation of library books, and the recommendation results were displayed to users through the user interface module. The results show that the average absolute error and root mean square error of the system are always the lowest, and the recommendation accuracy is high. When the control parameter is 0.4, the best intelligent book recommendation effect can be obtained; the recommended recall rate is not affected by the sparse density of the data set, and the stability is strong.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

scholarly journals Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

2022 ◽  
Vol 30 (7) ◽  
pp. 1-23
Author(s):  
Hongwei Hou ◽  
Kunzhi Tang ◽  
Xiaoqian Liu ◽  
Yue Zhou
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Research Result ◽  
Customer Information ◽  
Rural Governance ◽  
Artificial Intelligence Technology ◽  
Reasonable Prediction

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

The Application of Artificial Intelligence Technology in the Selective Laster Sintering

2014 ◽  
Vol 1037 ◽  
pp. 236-239
Author(s):  
Li Yuan Cai ◽  
Qing Shun Wang ◽  
Wei Sun
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bp Neural Network ◽  
Research Method ◽  
Control Method ◽  
Laser Sintering ◽  
Sintering Process ◽  
Optimal Parameters ◽  
Artificial Intelligence Technology ◽  
Neural Network Algorithm

Based on laser sintering constituency as the research object, this paper aimed at the perspective of artificial intelligence technology. It uses the new control theory and research method of BP neural network algorithm and tries to provide reference for optimizing the sintering process of laser district. This paper argues that the application of artificial intelligence technology to laser sintering constituency. Through the simulation, it can make up for the inadequacy of the traditional control method. Under certain conditions, the goal of process optimization will be achieved by finding the optimal parameters.

scholarly journals Convolution Based Neural Network Model to Prevent Diseases Classification and Prediction Using Deep Learning

2021 ◽  
Vol 10 (8) ◽  
pp. 46-50
Author(s):  
D J Samatha Naidu ◽  
M.Gurivi Reddy
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Internet ◽  
Learning Models ◽  
Data Set ◽  
Proposed Model ◽  
Recent Developments ◽  
Automatic Image Recognition ◽  
Recognition Systems

The farmer is a backbone to nation, but majority of the cultivated crops in india affecting by various diseases at various stages of its cultivation. Recent research works shows that diseases are not providing accurate results and few identifying but not providing optimized solutions to the system. In proposed work, the recent developments of Artificial intelligence through Deep Learning show that AIR (Automatic Image Recognition systems) using CNN algorithm models can be very beneficial in such scenarios. The Rice leaf diseases images related dataset is not easily available to automate , so that we have created our own trained data set which is small in size hence we have used transfer learning to develop our Proposed model which supports deep learning models. The Proposed CNN architecture illustrated based on VGG-16 model and it is trained, tested on given dataset collected from rice fields and the internet. The accuracy of the proposed model is moderately accurate with 92.46%.

scholarly journals Quality Inspection of Digital Archives of Application and Installation in Power Business Expanding based on Artificial Intelligence Technology

2021 ◽  
Vol 2079 (1) ◽  
pp. 012030
Author(s):  
Haihong Liang ◽  
Ling Zeng ◽  
Xiaozhou Shen ◽  
Weiwei Shi ◽  
Jiujiao Cang
Keyword(s):  
Artificial Intelligence ◽  
Classification Accuracy ◽  
Detection Method ◽  
Detection Methods ◽  
Digital Archive ◽  
Quality Inspection ◽  
Digital Archives ◽  
Evaluation Standard ◽  
Quality Detection ◽  
Artificial Intelligence Technology

Abstract The existing quality detection methods of business expansion digital archives have the problem of fuzzy evaluation standard, which leads to low classification accuracy. This paper designs a quality detection method of business expansion Digital Archives based on artificial intelligence technology. The business characteristics of business development are extracted, the minimum business data unit is described, the digital archive catalogue database is established, the digital archive evaluation standard is defined, the text similarity is calculated, the user model is established, and the quality inspection mode is established by using artificial intelligence technology. Experimental results: the average classification accuracy of the designed method based on artificial intelligence technology and the other two quality detection methods is 55.763, 43.560 and 42.605, which proves that the quality detection method based on artificial intelligence technology has higher use value.

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