Abdelwahed Motwakel^1,*, Marwa Obayya², Nadhem Nemri³, Khaled Tarmissi⁴, Heba Mohsen⁵, Mohammed Rizwanulla⁶, Ishfaq Yaseen⁶, Abu Sarwar Zamani⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735

Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusion-based predictive tool for energy… More >

Himanshu Padole^*, S. D. Joshi, Tapan K. Gandhi

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1655-1669, 2022, DOI:10.32604/iasc.2022.021310

Abstract Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) in the early stages, among which the graph-based methods have been more popular, because of their capability to utilize the relational information among different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both the static and the dynamic brain connectivity based features extracted from resting-state fMRI (rs-fMRI) data to detect AD in the early stages. First, our earlier proposed state-space model (SSM) based graph connectivity dynamics characterization method is used to design… More >

A. S. Al-Waisy¹, Mazin Abed Mohammed¹, Shumoos Al-Fahdawi¹, M. S. Maashi², Begonya Garcia-Zapirain³, Karrar Hameed Abdulkareem⁴, S. A. Mostafa⁵, Nallapaneni Manoj Kumar⁶, Dac-Nhuong Le^7,8,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2409-2429, 2021, DOI:10.32604/cmc.2021.012955

Abstract Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited… More >