scholarly journals Research on Telecom Fraud Detection Model Based on Cellular Network Data

2020 ◽  
Vol 2 (1) ◽  
pp. 12
Author(s):  
Kaiyuan Guo ◽  
Wenbo Wang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Convolutional Neural Network ◽  
Model Evaluation ◽  
Cellular Network ◽  
Network Data ◽  
Data Set ◽  
Machine Learning Classification ◽  
Detection Model ◽  
Depth Analysis

<p align="justify">With the rapid development of wireless communication technology, the use of mobile phones and other means of communication for telecommunications fraud has become a major problem that endangers user security. Aiming at this problem, this paper constructs a telecom fraud user detection model by in-depth analysis and mining of cellular network data. The model includes data processing, CNNcombine algorithm and model evaluation. First, in the data processing part, the data set is subjected to feature screening, coding, sampling, and the like. Secondly, the CNNcombine algorithm is a combination of a one-dimensional convolutional neural network and multiple traditional classification algorithms. The convolutional neural network is applied to solve classification problems other than text image signals. Finally, in the model evaluation part, it is proved that the CNNcombine algorithm has higher accuracy than the common machine learning classification algorithm such as XGBoost to detect telecom fraud users.</p>

scholarly journals Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lee Ming Jun Melvin ◽  
Rajesh Elara Mohan ◽  
Archana Semwal ◽  
Povendhan Palanisamy ◽  
Karthikeyan Elangovan ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Urban Environment ◽  
Field Trial ◽  
Detection Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Inspection Robot ◽  
Online Test

AbstractDrain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called ‘Raptor’. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.

scholarly journals Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Minghui Wei ◽  
Jingjing Tang ◽  
Haotian Tang ◽  
Rui Zhao ◽  
Xiaohui Gai ◽  
...  
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Data Processing ◽  
Convolutional Neural Network ◽  
Detection Model ◽  
Model Based ◽  
Urban Buildings ◽  
Intelligent Detection ◽  
Neural Network Algorithm ◽  
Public Datasets

It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obvious advantages in algorithm complexity and recognition accuracy. It is 25 percent more accurate than the latest model. The model can make full use of mobile computing resources, avoid network delay and dependence, and guarantee the real-time requirement of data processing. Moreover, the model can also well realize the augmented reality navigation and interaction effect of buildings in outdoor scenes. To sum up, this study provides a research idea for the identification, data processing, and intelligent detection of urban buildings.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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