Aqsa Aqdus¹, Rashid Amin^1,*, Sadia Ramzan¹, Sultan S. Alshamrani², Abdullah Alshehri³, El-Sayed M. El-kenawy⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1413-1435, 2023, DOI:10.32604/cmc.2023.031719

Abstract The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane’s forwarding capabilities.… More >

M. Murugesan^*, S. Thilagamani

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 389-405, 2022, DOI:10.32604/iasc.2022.024641

Abstract Automatic anomaly activity detection is difficult in video surveillance applications due to variations in size, type, shape, and objects’ location. The traditional anomaly detection and classification methods may affect the overall segmentation accuracy. It requires the working groups to judge their constant attention if the captured activities are anomalous or suspicious. Therefore, this defect creates the need to automate this process with high accuracy. In addition to being extraordinary or questionable, the display does not contain the necessary recording frame and activity standard to help the quick judgment of the parts’ specialized action. Therefore, to reduce the wastage of time… More >

T. Jayasree^1,*, S.Emerald Shia²

Sound & Vibration, Vol.55, No.2, pp. 141-161, 2021, DOI:10.32604/sv.2021.011734

Abstract This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks (FFNN). The important pathological voices such as Autism Spectrum Disorder (ASD) and Down Syndrome (DS) are considered for analysis. These pathological voices are known to manifest in different ways in the speech of children and adults. Therefore, it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects. The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques. In this work, three… More >

Machiraju Jayalakshmi^{1, *}, S. Nagaraja Rao²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1081-1096, 2020, DOI:10.32604/cmc.2020.011710

Abstract In recent years, the development in the field of computer-aided diagnosis (CAD) has increased rapidly. Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images. The existing algorithms have drawbacks with respect to their accuracy, efficiency, and limited learning processes. To address these issues, we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast, 2D- discrete wavelet transformation (2D-DWT) to extract the features, probabilistic principal component analysis (PPCA) and linear discriminant analysis (LDA) to normalize and reduce the features, and a feed-forward neural network (FNN)… More >