scholarly journals Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1161-1169
Author(s):  
Veeramosu Priyanka Brahmaiah ◽  
Yarlagadda Padma Sai ◽  
Mahendra N. Giri Prasad
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
Background Noise ◽  
Research Work ◽  
Convolution Neural Network ◽  
Normal Brain ◽  
Improved Outcomes ◽  
Dynamic Time ◽  
Dynamic Time Wrapping ◽  
Optimal Feature

Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

scholarly journals A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8023
Author(s):  
Tayyab Zafar ◽  
Khurram Kamal ◽  
Senthan Mathavan ◽  
Ghulam Hussain ◽  
Mohammed Alkahtani ◽  
...  
Keyword(s):  
Neural Network ◽  
Background Noise ◽  
Manufacturing Systems ◽  
Signal Reconstruction ◽  
Research Work ◽  
Hybrid Approach ◽  
Arma Model ◽  
Machining Process ◽  
Depth Of Cut ◽  
Milling Machine

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.

scholarly journals Dynamic All-Red Signal Control Based on Deep Neural Network Considering Red Light Runner Characteristics

2020 ◽  
Vol 10 (17) ◽  
pp. 6050
Author(s):  
Seong Kyung Kwon ◽  
Hojin Jung ◽  
Kyoung-Dae Kim
Keyword(s):  
Neural Network ◽  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Red Light ◽  
Transportation Systems ◽  
Signal Control ◽  
Traffic Efficiency ◽  
Intersection Safety ◽  
Dynamic Time ◽  
Dynamic Time Wrapping

Despite recent advances in technologies for intelligent transportation systems, the safety of intersection traffic is still threatened by traffic signal violation, called the Red Light Runner (RLR). The conventional approach to ensure the intersection safety under the threat of an RLR is to extend the length of the all-red signal when an RLR is detected. Therefore, the selection of all-red signal length is an important factor for intersection safety as well as traffic efficiency. In this paper, for better safety and efficiency of intersection traffic, we propose a framework for dynamic all-red signal control that adjusts the length of all-red signal time according to the driving characteristics of the detected RLR. In this work, we define RLRs into four different classes based on the clustering results using the Dynamic Time Wrapping (DTW) and the Hierarchical Clustering Analysis (HCA). The proposed system uses a Multi-Channel Deep Convolutional Neural Network (MC-DCNN) for online detection of RLR and also classification of RLR class. For dynamic all-red signal control, the proposed system uses a multi-level regression model to estimate the necessary all-red signal extension time more accurately and hence improves the overall intersection traffic safety as well as efficiency.

scholarly journals Mobile-based Skin Lesions Classification Using Convolution Neural Network

2020 ◽  
Vol 4 (2) ◽  
pp. 26-37 ◽  
Author(s):  
Nazia Hameed ◽  
Antesar Shabut ◽  
Fozia Hameed ◽  
Silvia Cirstea ◽  
Sorrel Harriet ◽  
...  
Keyword(s):  
Neural Network ◽  
Research Work ◽  
Classification Problem ◽  
Skin Lesions ◽  
Cloud Services ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Early Classification ◽  
Cloud Server

This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas.

scholarly journals Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwei Gu ◽  
Shah Nazir ◽  
Cheng Hong ◽  
Sulaiman Khan
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Intrusion Detection ◽  
Communication Systems ◽  
Computer Network ◽  
Research Work ◽  
Convolution Neural Network ◽  
Smart Devices ◽  
Identification System ◽  
Network Building

With the development of communication systems, information securities remain one of the main concerns for the last few years. The smart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios. Intruders are trying to attack the network and capture the organization’s important information for its own benefits. Intrusion detection is a way of identifying security violations and examining unwanted occurrences in a computer network. Building an accurate and effective identification system for intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication. In the proposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high security of the network. A convolution neural network based approach is followed for the feature classification and malicious data identification purposes. In the end, comparative results are generated after evaluating the performance of the proposed algorithm to other rival algorithms in the proposed field. These comparative algorithms were FGSM, JSMA, C&W, and ENM. After evaluating the performance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different parametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores. These results reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the applicability of the proposed algorithms in the network security field.

Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series

2022 ◽  
pp. 1510-1521
Author(s):  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Chaotic System ◽  
Research Work ◽  
Euler Method ◽  
Open Loop ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Dynamic Time

Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features, and can be simulated by nonlinear dynamic time series outputs of chaotic systems. This article presents the research work of chaotic system generator design based on artificial neural network (ANN), for studying the chaotic features of human brain dynamics. The ANN training performances of Nonlinear Auto-Regressive (NAR) model are evaluated for the generation and prediction of chaotic system time series outputs, based on varying the ANN architecture and the precision of the generated training data. The NAR model is trained in open loop form with 1,000 training samples generated using Lorenz system equations and the forward Euler method. The close loop NAR model is used for the generation and prediction of Lorenz chaotic time series outputs. The training results show that better training performance can be achieved by increasing the number of feedback delays and the number of hidden neurons, at the cost of increasing the computational load.

scholarly journals Voice Based Retrieval using Convolution Neural Network in Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 1829-1831
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Data ◽  
Convolution Neural Network ◽  
Replacement Method ◽  
Sequences Of Function ◽  
Word Search ◽  
Audio Files ◽  
Dynamic Time ◽  
The Right

In this paper, we propose a Content Based Voice Retrieval(CBVR) is used to search a specific audio files from a large data base. Using Deep learning features are learned automatically in the training phase. Convolution Neural Network is used in this research for CBVR. On this paper, we recommend a novel technique to key word search(KWS) in low-resource languages, which presents an replacement method for retrieving the phrases of curiosity, in particular for the out of vocabulary (OOV) ones. Our procedure contains the approaches of question-by using-illustration retrieval tasks into KWS and conducts the hunt by the use of the subsequence dynamic time warping (sDTW) algorithm. For this, text queries are modeled as sequences of function vectors and used as templates within the search. A Convolution neural network-headquartered model is informed to gain knowledge of a frame-degree distance metric to be used in sDTW and the right question model frame representations for this realized distance. This new procedure can be used as a substitute to traditional LVCSR-situated KWS programs, or in combination with them, to attain the intention of filling the gap between OOV and in-vocabulary (IV) retrieval performances.

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