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  • Open Access

    ARTICLE

    Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

    R. Sujatha, K. Nimala*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

    Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for… More >

  • Open Access

    ARTICLE

    Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

    Ibrahim M. Alwayle1, Hala J. Alshahrani2, Saud S. Alotaibi3, Khaled M. Alalayah1, Amira Sayed A. Aziz4, Khadija M. Alaidarous1, Ibrahim Abdulrab Ahmed5, Manar Ahmed Hamza6,*

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 153-168, 2023, DOI:10.32604/iasc.2023.034718

    Abstract Abstractive text summarization is crucial to produce summaries of natural language with basic concepts from large text documents. Despite the achievement of English language-related abstractive text summarization models, the models that support Arabic language text summarization are fewer in number. Recent abstractive Arabic summarization models encounter different issues that need to be resolved. Syntax inconsistency is a crucial issue resulting in the low-accuracy summary. A new technique has achieved remarkable outcomes by adding topic awareness in the text summarization process that guides the module by imitating human awareness. The current research article presents Abstractive Arabic… More >

  • Open Access

    ARTICLE

    Credit Card Fraud Detection Using Improved Deep Learning Models

    Sumaya S. Sulaiman1,2,*, Ibraheem Nadher3, Sarab M. Hameed2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051

    Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network… More >

  • Open Access

    ARTICLE

    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.1, pp. 97-114, 2024, DOI:10.32604/csse.2023.034520

    Abstract Computational linguistics is the scientific and engineering discipline related to comprehending written and spoken language from a computational perspective and building artefacts that effectively process and produce language, either in bulk or in a dialogue setting. This paper develops a Chaotic Bird Swarm Optimization with deep ensemble learning based Arabic poem classification and dictarization (CBSOEDL-APCD) technique. The presented CBSOEDL-APCD technique involves the classification and dictarization of Arabic text into Arabic poetries and prose. Primarily, the CBSOEDL-APCD technique carries out data pre-processing to convert it into a useful format. Besides, the ensemble deep learning (EDL) model More >

  • Open Access

    ARTICLE

    Electroencephalography (EEG) Based Neonatal Sleep Staging and Detection Using Various Classification Algorithms

    Hafza Ayesha Siddiqa1, Muhammad Irfan1, Saadullah Farooq Abbasi2,*, Wei Chen1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970

    Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual… More >

  • Open Access

    ARTICLE

    Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

    Firas Abedi1, Hayder M. A. Ghanimi2, Abeer D. Algarni3, Naglaa F. Soliman3,*, Walid El-Shafai4,5, Ali Hashim Abbas6, Zahraa H. Kareem7, Hussein Muhi Hariz8, Ahmed Alkhayyat9

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2791-2814, 2023, DOI:10.32604/csse.2023.038762

    Abstract Data mining plays a crucial role in extracting meaningful knowledge from large-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp… More >

  • Open Access

    ARTICLE

    K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

    Rufus Gikera1,*, Jonathan Mwaura2, Elizaphan Muuro3, Shadrack Mambo3

    Journal on Artificial Intelligence, Vol.5, pp. 75-112, 2023, DOI:10.32604/jai.2023.043229

    Abstract k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this… More >

  • Open Access

    ARTICLE

    Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

    Ilyоs Abdullaev1, Natalia Prodanova2, K. Aruna Bhaskar3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Jungeun Kim8,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1463-1477, 2023, DOI:10.32604/cmc.2023.038417

    Abstract Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computation-intensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue More >

  • Open Access

    ARTICLE

    An Optimized Feature Selection and Hyperparameter Tuning Framework for Automated Heart Disease Diagnosis

    Saleh Ateeq Almutairi*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

    Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then,… More >

  • Open Access

    ARTICLE

    Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches

    Jayashree Shetty1, Nisha P. Shetty1,*, Hrushikesh Kothikar1, Saleh Mowla1, Aiswarya Anand1, Veeraj Hegde2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206

    Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as… More >

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