Farhad Mortezapour Shiri^1,*, Ehsan Ahmadi², Mohammadreza Rezaee¹, Thinagaran Perumal¹

Journal on Artificial Intelligence, Vol.6, pp. 85-103, 2024, DOI:10.32604/jai.2024.048911

Abstract Automatic detection of student engagement levels from videos, which is a spatio-temporal classification problem is crucial for enhancing the quality of online education. This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos. The evaluation of these models utilizes the DAiSEE dataset, a public repository capturing student affective states in e-learning scenarios. The initial model integrates EfficientNetV2-L with Gated Recurrent Unit (GRU) and attains an accuracy of 61.45%. Subsequently, the second model combines EfficientNetV2-L with bidirectional GRU (Bi-GRU), yielding an accuracy of 61.56%. The… More >

Mohammed A. Al Ghamdi^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 881-894, 2023, DOI:10.32604/cmc.2023.037826

Abstract Considerable resources, technology, and efforts are being utilized worldwide to eradicate the coronavirus. Although certain measures taken to prevent the further spread of the disease have been successful, efforts to completely wipe out the coronavirus have been insufficient. Coronavirus patients have symptoms similar to those of chest Tuberculosis (TB) or pneumonia patients. Chest tuberculosis and coronavirus are similar because both diseases affect the lungs, cause coughing and produce an irregular respiratory system. Both diseases can be confirmed through X-ray imaging. It is a difficult task to diagnose COVID-19, as coronavirus testing kits are neither excessively available nor very reliable. In… More >

Badriyya B. Al-onazi¹, Saud S. Alotaib², Saeed Masoud Alshahrani^3,*, Najm Alotaibi⁴, Mrim M. Alnfiai⁵, Ahmed S. Salama⁶, Manar Ahmed Hamza⁷

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5447-5465, 2023, DOI:10.32604/cmc.2023.033564

Abstract The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this… More >

S. Gnanapriya^1,*, K. Rahimunnisa²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1105-1119, 2023, DOI:10.32604/iasc.2023.032832

Abstract The performance of Hand Gesture Recognition (HGR) depends on the hand shape. Segmentation helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural networks. The crucial segmentation task is extremely complicated because of the background complexity, variation in illumination etc. The proposed modified UNET and ensemble model of Convolutional Neural Networks (CNN) undergoes a two stage process and results in proper hand gesture recognition. The first stage is segmenting the regions of the hand and the second stage is gesture identification. The modified UNET segmentation model is trained using… More >

Zhaoqing Xie^1,*, Qing Liu², Yulian Cao³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 153-166, 2021, DOI:10.32604/iasc.2021.016246

Abstract Accurate prediction of water level in inland waterway has been an important issue for helping flood control and vessel navigation in a proactive manner. In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction. The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic water level features through layer-by-layer feature granulation with a greedy layer wise unsupervised learning algorithm. Six representative reaches in Yangtze… More >

Sulaiman Khan¹, Shah Nazir¹, Habib Ullah Khan^2,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 895-915, 2021, DOI:10.32604/cmc.2021.013878

Abstract During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.… More >

Shwet Ketu^*, Pramod Kumar Mishra

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1896-1919, 2021, DOI:10.32604/cmc.2020.012423

Abstract Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak. A deep learning-based ARIMA-LSTM hybrid… More >