Saeed Mohsen^1,2,*, Sherif S. M. Ghoneim³, Mohammed S. Alzaidi³, Abdullah Alzahrani³, Ashraf Mohamed Ali Hassan⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758

Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast… More >

Qaiser Abbas¹, Turki Alghamdi¹, Yazed Alsaawy¹, Tahir Alyas^2,*, Ali Alzahrani¹, Khawar Iqbal Malik³, Saira Bibi⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4261-4276, 2023, DOI:10.32604/cmc.2023.034482

Abstract The emergence of deep fake videos in recent years has made image falsification a real danger. A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content. Because of how clever these videos are frequently, Manipulation is challenging to spot. Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human. In earlier times, it was not so easy to alter the videos, which required expertise in the domain… More >

Lili Lv^*, Xiaodong Wang^†

Oncology Research, Vol.28, No.7-8, pp. 835-835, 2020

Abstract This article has no abstract. More >

BIDYUT MALLICK^1,#, ASHISH RANJAN SHARMA^2,#, MANOJIT BHATTACHARYA³, SANG-SOO LEE^1,*, CHIRANJIB CHAKRABORTY^4,*

BIOCELL, Vol.45, No.4, pp. 953-961, 2021, DOI:10.32604/biocell.2021.015530

Abstract PPARγ is a peroxisome proliferator-activated receptor (PPAR) family protein and is a target for type 2 diabetes (T2D). In this paper, we have performed a molecular docking analysis between ligand molecules (CID9816265, CID11608015, CID20251380, CID20251343, CID20556263, CID624491, CID42609928, and CID86287562) and PPARγ to determine the ligand specificity. It also helps to understand the ligand-binding domain (LBD) activity of PPARγ during the binding of the ligand. Further, a molecular dynamics simulation study was performed to determine the ligand biding stability in the PPARγ LBD. Its ligand specificity informed us about the potentiality of selecting a partial agonist. The study also shows… More >