Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (40)
  • Open Access

    ARTICLE

    Deep Learning Model for Big Data Classification in Apache Spark Environment

    T. M. Nithya1,*, R. Umanesan2, T. Kalavathidevi3, C. Selvarathi4, A. Kavitha5

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2537-2547, 2023, DOI:10.32604/iasc.2022.028804

    Abstract Big data analytics is a popular research topic due to its applicability in various real time applications. The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance. Since big data involves numerous features and necessitates high computational time, feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance. This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit (SBOA-OGRU) model for big data classification in Apache Spark. The SBOA-OGRU technique involves the… More >

  • Open Access

    REVIEW

    A Survey on Acute Leukemia Expression Data Classification Using Ensembles

    Abdel Nasser H. Zaied1, Ehab Rushdy2, Mona Gamal3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1349-1364, 2023, DOI:10.32604/csse.2023.033596

    Abstract Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to classify new samples. Ensemble classifiers… More >

  • Open Access

    ARTICLE

    Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

    Abeer D. Algarni*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3399-3412, 2023, DOI:10.32604/cmc.2023.026687

    Abstract In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named… More >

  • Open Access

    ARTICLE

    Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

    V. Gowri1,*, V. Vijaya Chamundeeswari2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3675-3690, 2023, DOI:10.32604/iasc.2023.035817

    Abstract Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical data. But, the traditional decision forest (DF) algorithms have lower classification accuracy and cannot handle high-dimensional feature space effectively. In this work, we propose a bootstrap decision forest using penalizing attributes (BFPA) algorithm to predict heart disease with higher accuracy. This work integrates a significance-based attribute selection (SAS) algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac illness. The proposed SAS algorithm is used to determine the correlation among attributes and to select the optimum… More >

  • Open Access

    ARTICLE

    Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

    Hussein Ibrahim Hussein1, Said Amirul Anwar2,*, Muhammad Imran Ahmad2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 547-564, 2023, DOI:10.32604/cmc.2023.036025

    Abstract Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which… More >

  • Open Access

    ARTICLE

    Data Augmentation and Random Multi-Model Deep Learning for Data Classification

    Fatma Harby1, Adel Thaljaoui1, Durre Nayab2, Suliman Aladhadh3,*, Salim EL Khediri3,4, Rehan Ullah Khan3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420

    Abstract In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches. In this… More >

  • Open Access

    ARTICLE

    Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

    R. Brindha1, S. Nandagopal2, H. Azath3, V. Sathana4, Gyanendra Prasad Joshi5, Sung Won Kim6,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5901-5914, 2023, DOI:10.32604/cmc.2023.030784

    Abstract Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’ sensitive data. E-mails, instant messages and phone calls are some of the common modes used in cyberattacks. Though the security models are continuously upgraded to prevent cyberattacks, hackers find innovative ways to target the victims. In this background, there is a drastic increase observed in the number of phishing emails sent to potential targets. This scenario necessitates the importance of designing an effective classification model. Though numerous conventional models are available in the literature for proficient classification of phishing emails,… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

    Mashael M Asiri1, Heba G. Mohamed2, Mohamed K Nour3, Mesfer Al Duhayyim4,*, Amira Sayed A. Aziz5, Abdelwahed Motwakel6, Abu Sarwar Zamani6, Mohamed I. Eldesouki7

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1679-1694, 2023, DOI:10.32604/csse.2023.031063

    Abstract Due to exponential increase in smart resource limited devices and high speed communication technologies, Internet of Things (IoT) have received significant attention in different application areas. However, IoT environment is highly susceptible to cyber-attacks because of memory, processing, and communication restrictions. Since traditional models are not adequate for accomplishing security in the IoT environment, the recent developments of deep learning (DL) models find beneficial. This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection (HMFS-SDLCAD) model. The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment. At… More >

  • Open Access

    ARTICLE

    Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

    Marwa Obayya1, Nadhem NEMRI2, Lubna A. Alharbi3, Mohamed K. Nour4, Mrim M. Alnfiai5, Mohammed Abdullah Al-Hagery6, Nermin M. Salem7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3151-3166, 2023, DOI:10.32604/cmc.2023.032765

    Abstract With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study… More >

  • Open Access

    ARTICLE

    Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

    S. Rama Sree1, Inderjeet Kaur2, Alexey Tikhonov3, E. Laxmi Lydia4, Ahmed A. Thabit5, Zahraa H. Kareem6, Yousif Kerrar Yousif7, Ahmed Alkhayyat8,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2195-2209, 2023, DOI:10.32604/cmc.2023.032586

    Abstract Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and… More >

Displaying 1-10 on page 1 of 40. Per Page