Mohammad Mehedi Hassan^1,*, Salman A. AlQahtani², Mabrook S. AlRakhami¹, Ahmed Zohier Elhendi³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3101-3123, 2024, DOI:10.32604/cmes.2024.047940

Abstract In the current landscape of the COVID-19 pandemic, the utilization of deep learning in medical imaging, especially in chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant. Despite its potential, deep learning’s “black box” nature has been a major impediment to its broader acceptance in clinical environments, where transparency in decision-making is imperative. To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced deep learning models. This integration forms a sophisticated and transparent framework for COVID-19 identification, enhancing the capability of standard Convolutional Neural Network… More >

Umar Abdulkadir Isa^*, Anil Fernando^*

Journal on Artificial Intelligence, Vol.5, pp. 31-42, 2023, DOI:10.32604/jai.2023.040213

Abstract Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%–98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating a model-independent, locally accurate explanation… More >

Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Han-Lin Chou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 107-134, 2023, DOI:10.32604/cmes.2023.024018

Abstract Our previous work has introduced the newly generated program using the code transformation model GPT-2, verifying the generated programming codes through simhash (SH) and longest common subsequence (LCS) algorithms. However, the entire code transformation process has encountered a time-consuming problem. Therefore, the objective of this study is to speed up the code transformation process significantly. This paper has proposed deep learning approaches for modifying SH using a variational simhash (VSH) algorithm and replacing LCS with a piecewise longest common subsequence (PLCS) algorithm to faster the verification process in the test phase. Besides the code transformation model GPT-2, this study has… More > Graphic Abstract

Ji-Won Baek¹, Kyungyong Chung^2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6573-6586, 2023, DOI:10.32604/cmc.2023.035246

Abstract Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships. It is possible to offer the explainable basis of decision-making for inference results. Through the causality of risk factors that have an ambiguous association in big medical data, it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status. In addition, the technique makes it possible to accurately predict disease risk for anomaly detection. Vision transformer for anomaly detection from image data makes classification through MLP. Unfortunately, in MLP, a vector… More >

Wahidul Hasan Abir¹, Faria Rahman Khanam¹, Kazi Nabiul Alam¹, Myriam Hadjouni², Hela Elmannai³, Sami Bourouis⁴, Rajesh Dey⁵, Mohammad Monirujjaman Khan^1,*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2151-2169, 2023, DOI:10.32604/iasc.2023.029653

Abstract Nowadays, deepfake is wreaking havoc on society. Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos. Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information. These manipulated pictures and videos will undoubtedly have an enormous societal impact. Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human… More >

Priti Shaw¹, Kaustubh Pachpor², Suresh Sankaranarayanan^3,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 311-325, 2023, DOI:10.32604/csse.2023.025281

Abstract Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem, especially in developing countries. There have been researches on human sepsis, vaccine response, and immunity. Also, machine learning methodologies were used for predicting infant mortality based on certain features like age, birth weight, gestational weeks, and Appearance, Pulse, Grimace, Activity and Respiration (APGAR) score. Sepsis, which is considered the most determining condition towards infant mortality, has never been considered for mortality prediction. So, we have deployed a deep neural model which is the state of art and performed a comparative analysis of machine… More >

Talha Saeed, Chu Kiong Loo^*, Muhammad Shahreeza Safiruz Kassim

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076

Abstract

Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >

Jiyoung Yun, Mainak Basak, Myung-Mook Han^*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2827-2843, 2021, DOI:10.32604/cmc.2021.017266

Abstract Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that has infected many people and caused many deaths on a nearly unprecedented level. As more people are infected each day, it continues to pose a serious threat to humanity worldwide. As a result, healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds. In most cases, healthy people experience tolerable symptoms if they are infected. However, in other cases, patients may suffer severe symptoms and require treatment in an intensive care unit. Thus, hospitals should select patients who have a high risk… More >