Open Access
ARTICLE
Anum Saher1, Ghulam Gilanie1,*, Sana Cheema1, Akkasha Latif1, Syeda Naila Batool1, Hafeez Ullah2
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.057047
Abstract Schizophrenia is a severe mental illness responsible for many of the world’s disabilities. It significantly impacts human society; thus, rapid, and efficient identification is required. This research aims to diagnose schizophrenia directly from a high-resolution camera, which can capture the subtle micro facial expressions that are difficult to spot with the help of the naked eye. In a clinical study by a team of experts at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, there were 300 people with schizophrenia and 299 healthy subjects. Videos of these participants have been captured and converted into their frames using… More >
Open Access
ARTICLE
Omer Nabeel Dara1,*, Tareq Abed Mohammed2, Abdullahi Abdu Ibrahim1
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.058736
（This article belongs to the Special Issue: Medical Imaging Decision Support Systems Using Deep Learning and Machine Learning Algorithms)
Abstract Healthcare polypharmacy is routinely used to treat numerous conditions; however, it often leads to unanticipated bad consequences owing to complicated medication interactions. This paper provides a graph convolutional network (GCN)-based model for identifying adverse effects in polypharmacy by integrating pharmaceutical data from electronic health records (EHR). The GCN framework analyzes the complicated links between drugs to forecast the possibility of harmful drug interactions. Experimental assessments reveal that the proposed GCN model surpasses existing machine learning approaches, reaching an accuracy (ACC) of 91%, an area under the receiver operating characteristic curve (AUC) of 0.88, and an More >
Open Access
ARTICLE
Satya Narayan Das1,2,*, Mrutyunjaya Panda2,*
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.057013
Abstract In today’s world of massive data and interconnected networks, it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content. Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images, but they have some flaws. To overcome this, the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image. Recently, quantum logistic maps, which combine the concept of quantum computing with traditional techniques, have been considered a niche and promising area of research that has attracted… More >
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