Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,083)
  • Open Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, Faisal Bin Ubaid3, Ahmed Ali4, Muhammad Irfan5, Abdullah A. Asiri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475

    Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

  • Open Access

    ARTICLE

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618

    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More > Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open Access

    CORRECTION

    Correction: Computational Linguistics Based Arabic Poem Classification and Dictarization Model

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Najm Alotaibi3, Mohamed K. Nour4, Mahmoud Othman5, Gouse Pasha Mohammed1, Mohammed Rizwanullah1, Mohamed I. Eldesouki6

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 869-870, 2024, DOI:10.32604/csse.2024.053660

    Abstract This article has no abstract. More >

  • Open Access

    CORRECTION

    Correction: An Effective Diagnosis System for Brain Tumor Detection and Classification

    Ahmed A. Alsheikhy1, Ahmad S. Azzahrani1, A. Khuzaim Alzahrani2, Tawfeeq Shawly3

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 853-853, 2024, DOI:10.32604/csse.2024.051630

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

    Abhishek Singhal*, Devendra Kumar Sharma

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 609-625, 2024, DOI:10.32604/csse.2023.046730

    Abstract This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions, employing a deep learning classification algorithm for speech signal analysis. In this study, speech samples are categorized for both training and testing purposes based on their geographical origin. Category 1 comprises speech samples from speakers outside of India, whereas Category 2 comprises live-recorded speech samples from Indian speakers. Testing speech samples are likewise classified into four distinct sets, taking into consideration both geographical origin and the language spoken by the speakers. Significantly, the results indicate a noticeable difference… More >

  • Open Access

    ARTICLE

    Analyzing COVID-19 Discourse on Twitter: Text Clustering and Classification Models for Public Health Surveillance

    Pakorn Santakij1, Samai Srisuay2,*, Pongporn Punpeng1

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066

    Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >

  • Open Access

    ARTICLE

    Multimodal Deep Neural Networks for Digitized Document Classification

    Aigerim Baimakhanova1,*, Ainur Zhumadillayeva2, Bigul Mukhametzhanova3, Natalya Glazyrina2, Rozamgul Niyazova2, Nurseit Zhunissov1, Aizhan Sambetbayeva4

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 793-811, 2024, DOI:10.32604/csse.2024.043273

    Abstract As digital technologies have advanced more rapidly, the number of paper documents recently converted into a digital format has exponentially increased. To respond to the urgent need to categorize the growing number of digitized documents, the classification of digitized documents in real time has been identified as the primary goal of our study. A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits, which were not… More >

  • Open Access

    ARTICLE

    A Hybrid Machine Learning Framework for Security Intrusion Detection

    Fatimah Mudhhi Alanazi*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 835-851, 2024, DOI:10.32604/csse.2024.042401

    Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the… More >

  • Open Access

    ARTICLE

    A New Malicious Code Classification Method for the Security of Financial Software

    Xiaonan Li1,2, Qiang Wang1, Conglai Fan2,3, Wei Zhan1, Mingliang Zhang4,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 773-792, 2024, DOI:10.32604/csse.2024.039849

    Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the… More >

  • Open Access

    ARTICLE

    Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

    Fadl Dahan*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921

    Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… More >

Displaying 1-10 on page 1 of 1083. Per Page