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  • Open Access


    Stroke Risk Assessment Decision-Making Using a Machine Learning Model: Logistic-AdaBoost

    Congjun Rao1, Mengxi Li1, Tingting Huang2,*, Feiyu Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898

    Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE)… More >

  • Open Access


    A Survey of Knowledge Graph Construction Using Machine Learning

    Zhigang Zhao1, Xiong Luo1,2,3,*, Maojian Chen1,2,3, Ling Ma1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513

    Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

  • Open Access


    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

    Nilkanth Mukund Deshpande1,2, Shilpa Gite3,4,*, Biswajeet Pradhan5,6, Abdullah Alamri7, Chang-Wook Lee8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704

    Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement… More > Graphic Abstract

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

  • Open Access


    Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

    Mohamed Ammar1,*, Hamed Al-Raweshidy2,*

    Journal on Artificial Intelligence, Vol.5, pp. 195-218, 2023, DOI:10.32604/jai.2023.045199

    Abstract Despite advances in intelligent medical care, difficulties remain. Due to its complicated governance, designing, planning, improving, and managing the cardiac system remains difficult. Oversight, including intelligent monitoring, feedback systems, and management practises, is unsuccessful. Current platforms cannot deliver lifelong personal health management services. Insufficient accuracy in patient crisis warning programmes. No frequent, direct interaction between healthcare workers and patients is visible. Physical medical systems and intelligent information systems are not integrated. This study introduces the Advanced Cardiac Twin (ACT) model integrated with Artificial Neural Network (ANN) to handle real-time monitoring, decision-making, and crisis prediction. THINGSPEAK is used to create an… More >

  • Open Access


    Identification and validation of novel prognostic fatty acid metabolic gene signatures in colon adenocarcinoma through systematic approaches


    Oncology Research, Vol.32, No.2, pp. 297-308, 2024, DOI:10.32604/or.2023.043138

    Abstract Background: Colorectal cancer (CRC) belongs to the class of significantly malignant tumors found in humans. Recently, dysregulated fatty acid metabolism (FAM) has been a topic of attention due to its modulation in cancer, specifically CRC. However, the regulatory FAM pathways in CRC require comprehensive elucidation. Methods: The clinical and gene expression data of 175 fatty acid metabolic genes (FAMGs) linked with colon adenocarcinoma (COAD) and normal cornerstone genes were gathered through The Cancer Genome Atlas (TCGA)-COAD corroborating with the Molecular Signature Database v7.2 (MSigDB). Initially, crucial prognostic genes were selected by uni- and multi-variate Cox proportional regression analyses; then, depending… More >

  • Open Access


    Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach


    Oncology Research, Vol.32, No.2, pp. 409-419, 2024, DOI:10.32604/or.2023.042863

    Abstract Background: Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide. Therapeutic failure in lung cancer (LUAD) is heavily influenced by drug resistance. This challenge stems from the diverse cell populations within the tumor, each having unique genetic, epigenetic, and phenotypic profiles. Such variations lead to varied therapeutic responses, thereby contributing to tumor relapse and disease progression. Methods: The Genomics of Drug Sensitivity in Cancer (GDSC) database was used in this investigation to obtain the mRNA expression dataset, genomic mutation profile, and drug sensitivity information of NSCLS. Machine Learning (ML) methods, including Random Forest… More >

  • Open Access


    A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

    Abdul Haseeb1, Muhammad Attique Khan2,*, Majed Alhaisoni3, Ghadah Aldehim4, Leila Jamel4, Usman Tariq5, Taerang Kim6, Jae-Hyuk Cha6

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3895-3920, 2023, DOI:10.32604/cmc.2023.045244

    Abstract Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve… More >

  • Open Access


    Machine Learning for Data Fusion: A Fuzzy AHP Approach for Open Issues

    Vinay Kukreja1, Asha Abraham2, K. Kalaiselvi3, K. Deepa Thilak3, Shanmugasundaram Hariharan4, Shih-Yu Chen5,6,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2899-2914, 2023, DOI:10.32604/cmc.2023.045136

    Abstract Data fusion generates fused data by combining multiple sources, resulting in information that is more consistent, accurate, and useful than any individual source and more reliable and consistent than the raw original data, which are often imperfect, inconsistent, complex, and uncertain. Traditional data fusion methods like probabilistic fusion, set-based fusion, and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw data. Data fusion is the process of integrating multiple data sources. Data filtering means examining a dataset to exclude, rearrange, or apportion data according to the criteria. Different sensors generate a large… More >

  • Open Access


    Developing Transparent IDS for VANETs Using LIME and SHAP: An Empirical Study

    Fayaz Hassan1,*, Jianguo Yu1, Zafi Sherhan Syed2, Arif Hussain Magsi3, Nadeem Ahmed4

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3185-3208, 2023, DOI:10.32604/cmc.2023.044650

    Abstract Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100% for the binary classification task… More >

  • Open Access


    From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

    Asif Khan1, Nada Boudjellal2, Huaping Zhang1,*, Arshad Ahmad3, Maqbool Khan3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3037-3055, 2023, DOI:10.32604/cmc.2023.044403

    Abstract Predicting election outcomes is a crucial undertaking, and various methods are employed for this purpose, such as traditional opinion polling, and social media analysis. However, traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels, resulting in a limited depth of analysis and understanding. In light of this challenge, this study focuses on predicting elections at the state/regional level along with the country level, intending to offer a comprehensive analysis and deeper insights into the electoral process. To achieve this, the study introduces the Location-Based Election Prediction Model (LEPM), which utilizes social media data,… More >

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