Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (20)
  • Open Access

    ARTICLE

    A Study on the Explainability of Thyroid Cancer Prediction: SHAP Values and Association-Rule Based Feature Integration Framework

    Sujithra Sankar1,*, S. Sathyalakshmi2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3111-3138, 2024, DOI:10.32604/cmc.2024.048408 - 15 May 2024

    Abstract In the era of advanced machine learning techniques, the development of accurate predictive models for complex medical conditions, such as thyroid cancer, has shown remarkable progress. Accurate predictive models for thyroid cancer enhance early detection, improve resource allocation, and reduce overtreatment. However, the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency. This paper proposes a novel association-rule based feature-integrated machine learning model which shows better classification and prediction accuracy than present state-of-the-art models. Our study also focuses on the application of SHapley Additive exPlanations (SHAP) values as… More >

  • Open Access

    ARTICLE

    MAIPFE: An Efficient Multimodal Approach Integrating Pre-Emptive Analysis, Personalized Feature Selection, and Explainable AI

    Moshe Dayan Sirapangi1, S. Gopikrishnan1,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2229-2251, 2024, DOI:10.32604/cmc.2024.047438 - 15 May 2024

    Abstract Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health… More >

  • Open Access

    ARTICLE

    Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging

    Mohammad Mehedi Hassan1,*, 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 - 11 March 2024

    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… More >

  • Open Access

    ARTICLE

    Explainable AI and Interpretable Model for Insurance Premium Prediction

    Umar Abdulkadir Isa*, Anil Fernando*

    Journal on Artificial Intelligence, Vol.5, pp. 31-42, 2023, DOI:10.32604/jai.2023.040213 - 11 August 2023

    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… More >

  • Open Access

    ARTICLE

    Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

    Bao Rong Chang1, Hsiu-Fen Tsai2,*, Han-Lin Chou1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 107-134, 2023, DOI:10.32604/cmes.2023.024018 - 05 January 2023

    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 More > Graphic Abstract

    Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

  • Open Access

    ARTICLE

    Explainable Anomaly Detection Using Vision Transformer Based SVDD

    Ji-Won Baek1, Kyungyong Chung2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6573-6586, 2023, DOI:10.32604/cmc.2023.035246 - 28 December 2022

    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.… More >

  • Open Access

    ARTICLE

    Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

    Wahidul Hasan Abir1, Faria Rahman Khanam1, Kazi Nabiul Alam1, Myriam Hadjouni2, Hela Elmannai3, Sami Bourouis4, Rajesh Dey5, Mohammad Monirujjaman Khan1,*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2151-2169, 2023, DOI:10.32604/iasc.2023.029653 - 19 July 2022

    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… More >

  • Open Access

    ARTICLE

    Explainable AI Enabled Infant Mortality Prediction Based on Neonatal Sepsis

    Priti Shaw1, Kaustubh Pachpor2, Suresh Sankaranarayanan3,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 311-325, 2023, DOI:10.32604/csse.2023.025281 - 01 June 2022

    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 More >

  • Open Access

    ARTICLE

    Ensembles of Deep Learning Framework for Stomach Abnormalities Classification

    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 - 11 October 2021

    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

    More >

  • Open Access

    ARTICLE

    Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients

    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 - 24 August 2021

    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… More >

Displaying 11-20 on page 2 of 20. Per Page