Search

  • Select Article Type
  • Abstract Supplements
  • Blood Group Review
  • Call to Arms
  • Communications
  • Hypothesis
  • In Memoriam
  • Interview
  • Introduction
  • Letter to the Editor
  • Short Report
  • abstract
  • Abstracts
  • Article
  • book-review
  • case-report
  • case-study
  • Clinical Practice
  • Commentary
  • Conference Presentation
  • conference-report
  • congress-report
  • Correction
  • critical-appraisal
  • Editorial
  • Editorial Comment
  • Erratum
  • Events
  • in-memomoriam
  • Letter
  • Letter to Editor
  • mini-review
  • minireview
  • News
  • non-scientific
  • Obituary
  • original-paper
  • original-report
  • Original Research
  • Pictorial Review
  • Position Paper
  • Practice Report
  • Preface
  • Preliminary report
  • Product Review
  • rapid-communication
  • Report
  • research-article
  • Research Communicate
  • research-paper
  • Research Report
  • Review
  • review -article
  • review-article
  • review-paper
  • Review Paper
  • Sampling Methods
  • Scientific Commentary
  • serologic-method-review
  • short-communication
  • short-report
  • Student Essay
  • Varia
  • Welome
  • Select Journal
  • In Jour Smart Sensing And Intelligent Systems
  • Journal Of Nematology
  • International Journal Advanced Network Monitoring Controls

 

Article | 13-July-2020

Improved Faster R-CNN Algorithm for Sea Object Detection Under Complex Sea Conditions

problems faced by current object detection[3-6]. Reference[7] studied the object recognition of remote sensing images based on Faster R-CNN deep network, and proved that the deep learning method can realize the rapid and accurate recognition of remote sensing image objects. Reference[8] discusses that when satellites take remote sensing images of the sea, they are greatly affected by weather conditions. Remote sensing images may have problems such as relatively small ship size, cloud cover, and

Liu Yabin, Yu Jun, Hu Zhiyi

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 2, 76–82

Article | 02-April-2020

Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

, the traditional survey situation is that the site construction surveyors scan the survey images generated by radar equipment one by one according to their expert knowledge. This traditional method has a large workload, a large human factor, and a certain rate of omission and error. In recent years, with the continuous improvement of GPU, the field of deep learning is booming. In 2006, Hinton[4] and other researcher proposed the concept of deep learning, using convolutional neural network (CNN) to

He Li, Yubian Wang

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 44–53

research-article | 31-August-2021

Designing Convolutional Neural Network Architecture Using Genetic Algorithms

the network to understand complex ideas by building upon simpler ones. For example, a deep network can build the concept of an image of a car by combining simpler concepts, such as edges, corners, contour, and object parts [1]. Convolutional Neural Network (CNN) is one such type of deep networks. Yann LeCan carried out one of the first exercises on CNN. He taught a computer system how to recognize the differences between handwritten digits [2]. When the system chose incorrectly, he would correct

Ashray Bhandare, Devinder Kaur

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 3, 26–35

Article | 13-July-2020

Research on the Application of Convolutional Neural Networks in the Image Recognition

analytical judgments about the data. Compared to traditional machine learning methods, it has achieved good results in search technology, image recognition, machine translation, natural language processing, multimedia learning, speech, recommendation and personalization technologies. With the practice of researchers in various fields, many network models have been proposed, such as DBN (Deep Belief Network), CNN (Convolutional Neural Network), RNN (Recursive Neural Network), etc. The introduction of CNN

Gao Zhiyu, Liu Bailin, Gu Hongxian, Mu Jing

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 2, 31–38

research-article | 31-August-2021

Deep Learning in Product Manufacturing Record System

, energy consumption in life, finance and communications. Features such as face recognition, speech processing and video object detection are derived based on algorithms and extensive training in deep learning. The learned features extracted through different deep networks perform well in prediction. Therefore, learning deep features for prediction is becoming Getting more and more popular. In this paper, inspection records in product manufacturing system are used as training samples, CNN, STACK LSTM

Wenjing Wang, Li Zhao

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 3, 59–65

Article | 11-January-2021

Deep Capsule Network Handwritten Digit Recognition

topic “Dynamic Routing Between Capsules” [10] at a top-level conference on machine learning called “NIPS” and proposed Capsule network (CapsNet). This is a deep learning method that shakes the whole field of artificial intelligence. It breaks the bottleneck of convolutional neural network (CNN) and pushes the field of artificial intelligence to a new level. This paper focuses on the recognition of MNIST data set based on capsule network. MNIST[7] is a data set composed of numbers handwritten by

Yuxing Tan, Hongge Yao

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 4, 1–8

research-article | 06-November-2020

Competitive fitness analysis using Convolutional Neural Network

through a filter that lets the larvae, but not the adults, though. To further improve Caenorhabditis fitness analysis, here we present (i) a fitness assay protocol involving separation of parental and offspring generations and (ii) an open-source model for image analysis based on a convolutional neural network (CNN). Machine learning models are very convenient for image analysis because they can be trained on a sample of pictures to analyze images of interest with a high output rate. The concept of

Joanna K. Palka, Krzysztof Fiok, Weronika Antoł, Zofia M. Prokop

Journal of Nematology, Volume 52 , 1–15

research-article | 31-August-2021

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

challenges. It has a wide array of practical applications like remote sensing, autonomous driving, indoor navigation, video surveillance and virtual or augmented reality systems etc. Nowadays Deep Learning techniques [4] provide state-of-the-art performance for image segmentation and classification as well as for detection tasks and captioning using Convolutional Neural Network models and have been mainly accelerating the recent breakthroughs in semantic segmentation using different combinations of CNN

Muhammad Adeel Ahmed Tahir, Xiao Feng, Zaryab Shaker

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 3, 9–17

Article | 09-April-2018

Lidar Image Classification based on Convolutional Neural Networks

Yang Wenhui, Yu Fan

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 158–162

Article | 30-November-2018

Image Transformation Based on Generative Adversarial Networks

Jie Chen, Li Zhao

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 2, 93–98

research-article | 31-August-2021

Using Text and Visual Cues for Fine-Grained Classification

high accuracy and performs well on complex natural images but the drawback is that it requires a relatively large volume of training data. Further in 2014 Jaderberg et al [10] addresses the problem of text detection and recognition by generating text proposals with CNN and provides an end-to-end system for reading text in natural scene images. That system was capable of both text spotting and image retrieval and perform excellently on complex natural images. Yao et al [11] present a unified

Zaryab Shaker, Xiao Feng, Muhammad Adeel Ahmed Tahir

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 3, 42–49

research-article | 25-August-2020

1/10th scale autonomous vehicle based on convolutional neural network

autonomous vehicles is explored here (Paull et al., 2017; Srinivasa et al., 2019; Miao et al., 2012). In the study of Pannu et al. (2015), a method to build a self-driving car is presented using raspberry pi 2. The research mainly focusses on lane detection and obstacle avoidance using a camera and ultrasonic sensor as its input. A similar approach is shown in Bechtel et al. (2018), where the primary focus of research contribution is the CNN model used in the system. This research paper also compared

Avishkar Seth, Alice James*, Subhas C. Mukhopadhyay

International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–17

No Record Found..
Page Actions