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  • Open Access

    ARTICLE

    Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

    S M Hasan Mahmud1,2, Md Mamun Ali3, Mohammad Fahim Shahriar1, Fahad Ahmed Al-Zahrani4, Kawsar Ahmed5,6,*, Dip Nandi1, Francis M. Bui5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020

    Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build… More >

  • Open Access

    ARTICLE

    OPT-BAG Model for Predicting Student Employability

    Minh-Thanh Vo1, Trang Nguyen2, Tuong Le3,4,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1555-1568, 2023, DOI:10.32604/cmc.2023.039334

    Abstract The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job. Based on the results of this type of analysis, university managers can improve the employability of their students, which can help in attracting students in the future. In addition, learners can focus on the essential skills identified through this analysis during their studies, to increase their employability. An effective method called OPT-BAG (OPTimisation of BAGging classifiers) was therefore developed to model the problem of predicting the employability of students. This model can help predict the employability of students… More >

  • Open Access

    ARTICLE

    Priority Detector and Classifier Techniques Based on ML for the IoMT

    Rayan A. Alsemmeari1,*, Mohamed Yehia Dahab2, Badraddin Alturki1, Abdulaziz A. Alsulami3

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1853-1870, 2023, DOI:10.32604/cmc.2023.038589

    Abstract Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients’ health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees’ stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative… More >

  • Open Access

    ARTICLE

    Pre-Locator Incorporating Swin-Transformer Refined Classifier for Traffic Sign Recognition

    Qiang Luo1, Wenbin Zheng1,2,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2227-2246, 2023, DOI:10.32604/iasc.2023.040195

    Abstract In the field of traffic sign recognition, traffic signs usually occupy very small areas in the input image. Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process, which leads to the loss of small object information. Additionally, classification tasks are more sensitive to information loss than localization tasks. This paper proposes a novel traffic sign recognition approach, in which a lightweight pre-locator network and a refined classification network are incorporated. The pre-locator network locates the sub-regions of the traffic signs from the original image, and the refined classification… More >

  • Open Access

    ARTICLE

    Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

    P. Sabitha*, G. Meeragandhi

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402

    Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet) to classify the H&E stained… More >

  • Open Access

    ARTICLE

    Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption

    Chen Wang1, Jian Xu1,*, Jiarun Li1, Yan Dong1, Nitin Naik2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1421-1436, 2023, DOI:10.32604/iasc.2023.034123

    Abstract Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models. In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE). We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners. Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison. It supports the computation of encrypted data with different public keys. At… More >

  • Open Access

    ARTICLE

    Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

    A. Manju1, R. kaladevi2, Shanmugasundaram Hariharan3, Shih-Yu Chen4,5,*, Vinay Kukreja6, Pradip Kumar Sharma7, Fayez Alqahtani8, Amr Tolba9, Jin Wang10

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 993-1007, 2023, DOI:10.32604/cmc.2023.039567

    Abstract The medical community has more concern on lung cancer analysis. Medical experts’ physical segmentation of lung cancers is time-consuming and needs to be automated. The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques. Computer-Aided Diagnostic (CAD) system aids in the diagnosis and shortens the time necessary to detect the tumor detected. The application of Deep Neural Networks (DNN) has also been exhibited as an excellent and effective method in classification and segmentation tasks. This research aims to separate lung cancers from images of Magnetic Resonance Imaging… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

    Sundas Rukhsar1, Mazhar Javed Awan1, Usman Naseem2, Dilovan Asaad Zebari3, Mazin Abed Mohammed4,*, Marwan Ali Albahar5, Mohammed Thanoon5, Amena Mahmoud6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 791-807, 2023, DOI:10.32604/csse.2023.038384

    Abstract Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for… More >

  • Open Access

    ARTICLE

    Ensemble Learning for Fetal Health Classification

    Mesfer Al Duhayyim1,*, Sidra Abbas2, Abdullah Al Hejaili3, Natalia Kryvinska4,*, Ahmad Almadhor5, Huma Mughal6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 823-842, 2023, DOI:10.32604/csse.2023.037488

    Abstract : Cardiotocography (CTG) represents the fetus’s health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective:: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method:: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories:… More >

  • Open Access

    ARTICLE

    BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

    A. Richard William1,*, J. Senthilkumar2, Y. Suresh2, V. Mohanraj2

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753

    Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection (BFS) in the… More >

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