Dmitry Gura^1,2, Bo Dong^3,*, Duaa Mehiar⁴, Nidal Al Said⁵

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1995-2014, 2024, DOI:10.32604/cmc.2024.048238 - 15 May 2024

Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake… More >

Min-Jeong Kim¹, Byeong-Uk Jeon¹, Hyun Yoo², Kyungyong Chung^3,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2371-2386, 2023, DOI:10.32604/iasc.2023.040310 - 21 June 2023

Abstract With the increasing number of digital devices generating a vast amount of video data, the recognition of abnormal image patterns has become more important. Accordingly, it is necessary to develop a method that achieves this task using object and behavior information within video data. Existing methods for detecting abnormal behaviors only focus on simple motions, therefore they cannot determine the overall behavior occurring throughout a video. In this study, an abnormal behavior detection method that uses deep learning (DL)-based video-data structuring is proposed. Objects and motions are first extracted from continuous images by combining existing More >

Madhuri Agrawal^*, Shikha Agrawal

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493 - 15 March 2023

Abstract

Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of ‘suspicious fall’ events in learning the

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S. Praveenkumar^*, T. Karthick

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2417-2433, 2023, DOI:10.32604/csse.2023.032480 - 21 December 2022

Abstract Because stress has such a powerful impact on human health, we must be able to identify it automatically in our everyday lives. The human activity recognition (HAR) system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions. Using the multimodal dataset DEAP (Database for Emotion Analysis using Physiological Signals), this paper presents deep learning (DL) technique for effectively detecting human stress. The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition… More >

Khaled Mohamad Almustafa¹, Taiseer Abdalla Elfadil Eisa², Amani Abdulrahman Albraikan³, Mesfer Al Duhayyim^4,*, Manar Ahmed Hamza⁵, Abdelwahed Motwakel⁵, Ishfaq Yaseen⁵, Muhammad Imran Babar⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2483-2499, 2022, DOI:10.32604/cmc.2022.024688 - 29 March 2022

Abstract In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of… More >

E. Elamaran^1,*, B. Sudhakar²

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1467-1477, 2022, DOI:10.32604/iasc.2022.020625 - 09 December 2021

Abstract Essential components in wireless systems are schedulin and resource allocation. The problems in scheduling refers to inactive users in a given time slot and in terms of resource allocation it refers to the issues in the allocation of physical layer resources such as power and bandwidth among the active users. In the Long Time Evolution (LTE) downlink scheduling the optimized problem refers to the flow deadlines that incorporate the formulation in the surveyed scheduling algorithm for achieving enhanced performance levels. The major challenges appear in the areas of quality and bandwidth constrains in the video… More >

Syeda Binish Zahra^1,2, Muhammad Adnan Khan^2,*, Sagheer Abbas¹, Khalid Masood Khan², Mohammed A. Al-Ghamdi³, Sultan H. Almotiri³

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1269-1282, 2021, DOI:10.32604/cmc.2020.012778 - 26 November 2020

Abstract Biomechanics is the study of physiological properties of data and the measurement of human behavior. In normal conditions, behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style, body movements in walking patterns, writing style and voice tunes. One cannot perform any change in these inputs that make results reliable and increase the accuracy. The aim of our study is to perform a comparative analysis between the marker-based motion capturing system (MBMCS) and the marker-less motion capturing system (MLMCS) using the lower body joint angles of human… More >