scholarly journals Detection of Node Capture under Asynchronous Sleep Mode Based on Recurrent Neural Network

CONVERTER ◽  
2021 ◽  
pp. 198-208
Author(s):  
Jintao Gu, Jianyu Wang, Hao Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Early Stage ◽  
Sensor Nodes ◽  
Sleep Mode ◽  
Detection Accuracy ◽  
Daily Lives ◽  
Industrial Manufacturing ◽  
Node Capture ◽  
Physical Node

In recent years, wireless sensor networks (WSNs) have found numerousapplications in industrial manufacturing and people’s daily lives. However, security risks associated with the use of WSNs have also become increasingly pronounced. An attacker launching an internal attack on a WSNmust first physically capture several nodes, i.e., take control of the target nodes by acquiring, cracking, and analyzing important information carried by the target nodes, thus laying the groundwork for subsequent attack steps. Therefore, physical node capture is a critical step in an internal attack on aWSN. Detecting behaviorsthat indicatephysical capture of nodes provides an early warning of anetwork attack, allowing steps to be taken to prevent further attacks from being launched from the captured nodes. This paper proposes an RNN (recurrent neural network)-based detection method that can be used to detect node capture in WSNs with asynchronous sleep mode at an early stage (i.e., before captured nodes rejoin the network).Thus, the methodenables early detection of network attacks. During the decision-making process, a common monitoring mechanism that relies on cooperation between neighboring nodes is employed to improve detection accuracy. The proposed method obtains the sensor nodes’ states and makes a judgment with the help of RNN, achieving accurate detection of node capture under the condition of unsynchronized clocks. Simulation results demonstrate the proposed method’s capability to achieve high detection accuracy.

Jaya Ant Lion Optimization-Driven Deep Recurrent Neural Network for Cancer Classification Using Gene Expression Data (Preprint)

2020 ◽  
Author(s):  
Ramachandro Majji
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Early Stage ◽  
Data Transformation ◽  
Cancer Classification ◽  
Data Normalization ◽  
Ant Lion Optimization ◽  
Deep Recurrent Neural Network ◽  
Novel Strategy ◽  
Feature Dimension

BACKGROUND Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. OBJECTIVE Propose an automatic prediction system for classifying cancer to malignant or benign. METHODS This paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is the data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer-based on the reduced dimension features to produce a satisfactory result. RESULTS The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, the maximal sensitivity of 95.95%, and the maximal specificity of 96.96%. CONCLUSIONS The resulted output of the proposed JayaALO-based DeepRNN is used for cancer classification.

scholarly journals Performance Analysis and Recognition of Speech using Recurrent Neural Network

2019 ◽  
Vol 1 (1) ◽  
pp. 87-95
Author(s):  
Bishon Lamichanne ◽  
Hari K.C.
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Performance Analysis ◽  
Recurrent Neural Network ◽  
Error Rate ◽  
English Language ◽  
Daily Lives ◽  
Crucial Step ◽  
The Neural Network ◽  
Positive Results

Speech is one of the most natural ways to communicate between people. It plays an important role in our daily lives. To make machines able to talk with people is a challenging but very useful task. A crucial step is to enable machines to recognize and understand what people are saying. Hence, speech recognition becomes a key technique providing an interface for communication between machines and humans. There has been a long research history on speech recognition. Neural network is known as a technique that has ability to classify nonlinear problem. Today, lots of research are going in the field of speech recognition with the help of the Neural Network. Even though positive results have been obtained from continuous study, research on minimizing the error rate is still gaining lots attention. The English language offers a number of challenges for speech recognition. This paper implements the RNN to analyze and recognize speech from the set of spoken words.

scholarly journals Calibrating Network Traffic with One-Dimensional Convolutional Neural Network with Autoencoder and Independent Recurrent Neural Network for Mobile Malware Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Songjie Wei ◽  
Zedong Zhang ◽  
Shasha Li ◽  
Pengfei Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Network Traffic ◽  
Malware Detection ◽  
Smart Devices ◽  
Detection Accuracy ◽  
One Dimensional ◽  
Mobile Malware ◽  
Mobile Malware Detection

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. The autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training. We conduct a series of experiments to evaluate the effectiveness of the proposed method and its performance for the 1DCAE-IndRNN-integrated detection procedure. The detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98% detection accuracy and recall rates with clear advantages over other benchmark methods.

scholarly journals Diagnosis and Classification of Alzheimer's Disease by Using a Convolution Neural Network Algorithm

2021 ◽  
Author(s):  
Mosleh Hmoud Al-Adhaileh
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Classification Systems ◽  
Detection Accuracy ◽  
Learning Approaches

Abstract Alzheimer's disease (AD) is a high-risk and atrophic neurological illness that slowly and gradually destroys brain cells (i.e. neurons). As the most common type of amentia, AD affects 60–65% of all people with amentia and poses major health dangers to middle-aged and elderly people. For classification of AD in the early stage, classification systems and computer-aided diagnostic techniques have been developed. Previously, machine learning approaches were applied to develop diagnostic systems by extracting features from neural images. Currently, deep learning approaches have been used in many real-time medical imaging applications. In this study, two deep neural network techniques, AlexNet and Restnet50, were applied for the classification and recognition of AD. The data used in this study to evaluate and test the proposed model included those from brain magnetic resonance imaging (MRI) images collected from the Kaggle website. A convolutional neural network (CNN) algorithm was applied to classify AD efficiently. CNNs were pre-trained using AlexNet and Restnet50 transfer learning models. The results of this experimentation showed that the proposed method is superior to the existing systems in terms of detection accuracy. The AlexNet model achieved outstanding performance based on five evaluation metrics (accuracy, F1 score, precision, sensitivity and specificity) for the brain MRI datasets. AlexNet displayed an accuracy of 94.53%, specificity of 98.21%, F1 score of 94.12% and sensitivity of 100%, outperforming Restnet50. The proposed method can help improve CAD methods for AD in medical investigations.

scholarly journals Convolutional Recurrent Neural Network-Based Event Detection in Tunnels Using Multiple Microphones

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2695 ◽  
Author(s):  
Nam Kyun Kim ◽  
Kwang Myung Jeon ◽  
Hong Kook Kim
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Background Noise ◽  
Source Separation ◽  
Error Rates ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Noisy Signal ◽  
Recognition Error

This paper proposes a sound event detection (SED) method in tunnels to prevent further uncontrollable accidents. Tunnel accidents are accompanied by crashes and tire skids, which usually produce abnormal sounds. Since the tunnel environment always has a severe level of noise, the detection accuracy can be greatly reduced in the existing methods. To deal with the noise issue in the tunnel environment, the proposed method involves the preprocessing of tunnel acoustic signals and a classifier for detecting acoustic events in tunnels. For preprocessing, a non-negative tensor factorization (NTF) technique is used to separate the acoustic event signal from the noisy signal in the tunnel. In particular, the NTF technique developed in this paper consists of source separation and online noise learning. In other words, the noise basis is adapted by an online noise learning technique for enhancement in adverse noise conditions. Next, a convolutional recurrent neural network (CRNN) is extended to accommodate the contributions of the separated event signal and noise to the event detection; thus, the proposed CRNN is composed of event convolution layers and noise convolution layers in parallel followed by recurrent layers and the output layer. Here, a set of mel-filterbank feature parameters is used as the input features. Evaluations of the proposed method are conducted on two datasets: a publicly available road audio events dataset and a tunnel audio dataset recorded in a real traffic tunnel for six months. In the first evaluation where the background noise is low, the proposed CRNN-based SED method with online noise learning reduces the relative recognition error rate by 56.25% when compared to the conventional CRNN-based method with noise. In the second evaluation, where the tunnel background noise is more severe than in the first evaluation, the proposed CRNN-based SED method yields superior performance when compared to the conventional methods. In particular, it is shown that among all of the compared methods, the proposed method with the online noise learning provides the best recognition rate of 91.07% and reduces the recognition error rates by 47.40% and 28.56% when compared to the Gaussian mixture model (GMM)–hidden Markov model (HMM)-based and conventional CRNN-based SED methods, respectively. The computational complexity measurements also show that the proposed CRNN-based SED method requires a processing time of 599 ms for both the NTF-based source separation with online noise learning and CRNN classification when the tunnel noisy signal is one second long, which implies that the proposed method detects events in real-time.

Lane marking detection and classification with combined deep neural network for driver assistance

2018 ◽  
Vol 233 (5) ◽  
pp. 1259-1268 ◽  
Author(s):  
Weiwei Zhang ◽  
Hui Liu ◽  
Xuncheng Wu ◽  
Lingyun Xiao ◽  
Yubin Qian ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Lane Detection ◽  
Convolution Neural Network ◽  
Detection Accuracy ◽  
Lane Marking ◽  
Fitting Method ◽  
Boundary Fitting ◽  
The Time Domain ◽  
Continuous Frames

An efficient approach for lane marking detection and classification by the combination of convolution neural network and recurrent neural network is proposed in this paper. First, convolution neural network is trained for lane marking features extraction, and then these convolution neural network features of continuous frames are transferred to recurrent neural network model for lane boundary detection and classification in the time domain. At last, a lane boundary fitting method based on dynamic programming is proposed to improve the lane detection accuracy and robustness. The method presented generates satisfactory results of lane detection and type classification under various traffic conditions according to the experimental results, which show that the approach provided in this paper outperforms traditional methods and the total lane markings classification reached 95.21% accuracy.

scholarly journals Automatic Detection of Grammatical Errors in English Verbs Based on RNN Algorithm: Auxiliary Objectives for Neural Error Detection Models

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yizhou He
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Error Detection ◽  
Rapid Development ◽  
Automatic Detection ◽  
Detection Accuracy ◽  
Original Text ◽  
Detection Technology ◽  
Grammatical Errors ◽  
English Verb

With the rapid development of neural network technology, we have widely used this technology in various fields. In the field of language translation, the research on automatic detection technology of English verb grammatical errors is in a hot stage. The traditional manual detection cannot be applied to the current environment. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. The experimental results show that the algorithm model studied in this paper can effectively improve the detection accuracy and feedback efficiency and is more applicable and effective than the traditional manual detection method.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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