Jiaqi Wang¹, Jie Zhang², Genlin Ji^2,*, Bo Sheng³

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1629-1642, 2022, DOI:10.32604/iasc.2022.029535 - 25 May 2022

Abstract The surveillance applications generate enormous video data and present challenges to video analysis for huge human labor cost. Reconstruction-based convolutional autoencoders have achieved great success in video anomaly detection for their ability of automatically detecting abnormal event. The approaches learn normal patterns only with the normal data in an unsupervised way due to the difficulty of collecting anomaly samples and obtaining anomaly annotations. But convolutional autoencoders have limitations in global feature extraction for the local receptive field of convolutional kernels. What is more, 2-dimensional convolution lacks the capability of capturing temporal information while videos change… More >

S. Karthic^1,*, S. Manoj Kumar²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 439-453, 2022, DOI:10.32604/iasc.2022.025153 - 15 April 2022

Abstract In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as More >

Xiaosha Qi¹, Genlin Ji^2,*, Jie Zhang², Bo Sheng³

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1861-1875, 2022, DOI:10.32604/iasc.2022.027182 - 24 March 2022

Abstract Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features. In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE).… More >

Ahmed S. Almasoud¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Abubakar Elsafi⁵, Mesfer Al Duhayyim⁶, Ishfaq Yaseen⁷, Manar Ahmed Hamza^7,*, Abdelwahed Motwakel⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 871-886, 2022, DOI:10.32604/cmc.2022.024596 - 24 February 2022

Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM… More >

I. Kalphana^1,*, T. Kesavamurthy²

Computer Systems Science and Engineering, Vol.41, No.1, pp. 171-185, 2022, DOI:10.32604/csse.2022.019799 - 08 October 2021

Abstract Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal More >