Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (50)
  • Open Access

    ARTICLE

    Classification of COVID-19 CT Scans via Extreme Learning Machine

    Muhammad Attique Khan1, Abdul Majid1, Tallha Akram2, Nazar Hussain1, Yunyoung Nam3,*, Seifedine Kadry4, Shui-Hua Wang5, Majed Alhaisoni6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1003-1019, 2021, DOI:10.32604/cmc.2021.015541 - 22 March 2021

    Abstract Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans. The scheme operates in four steps. Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans. These images were retrieved from the Radiopaedia COVID-19 website. The images were divided into training and test sets in a ratio of 70:30. Then, multiple features were extracted from the training data. We used canonical correlation analysis to fuse the features into single vectors; this enhanced the predictive capacity. We next implemented a genetic algorithm (GA) in which an More >

  • Open Access

    ARTICLE

    Detection of COVID-19 Enhanced by a Deep Extreme Learning Machine

    Aaqib Inam1,*, Zhuli1, Ayesha Sarwar1, Salah-ud-din2, Ayesha Atta3, Iftikhar Naaseer4, Shahan Yamin Siddiqui5,6, Muhammad Adnan Khan7

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 701-712, 2021, DOI:10.32604/iasc.2021.014235 - 01 March 2021

    Abstract The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for… More >

  • Open Access

    ARTICLE

    Intelligent Software-Defined Network for Cognitive Routing Optimization Using Deep Extreme Learning Machine Approach

    Fahd Alhaidari1, Sultan H. Almotiri2, Mohammed A.Al Ghamdi2, Muhammad Adnan Khan3,*, Abdur Rehman4, Sagheer Abbas4, Khalid Masood Khan3, Atta-ur-Rahman5

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1269-1285, 2021, DOI:10.32604/cmc.2021.013303 - 12 January 2021

    Abstract In recent years, the infrastructure, instruments, and resources of network systems are becoming more complex and heterogeneous, with the rapid development of current internet and mobile communication technologies. In order to efficaciously prepare, control, hold and optimize networking systems, greater intelligence needs to be deployed. However, due to the inherently dispensed characteristic of conventional networks, Machine Learning (ML) techniques are hard to implement and deployed to govern and operate networks. Software-Defined Networking (SDN) brings us new possibilities to offer intelligence in the networks. SDN’s characteristics (e.g., logically centralized control, global network view, software-based site visitor… More >

  • Open Access

    ARTICLE

    A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System

    Amir Haider1, Muhammad Adnan Khan2, Abdur Rehman3, Muhib Ur Rahman4, Hyung Seok Kim1,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1785-1798, 2021, DOI:10.32604/cmc.2020.013910 - 26 November 2020

    Abstract In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in More >

  • Open Access

    ARTICLE

    Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

    Muhammad Adnan Khan1,*, Abdur Rehman2, Khalid Masood Khan1, Mohammed A. Al Ghamdi3, Sultan H. Almotiri3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 467-480, 2021, DOI:10.32604/cmc.2020.013121 - 30 October 2020

    Abstract Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations… More >

  • Open Access

    ARTICLE

    Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

    Dildar Hussain1, Muhammad Adnan Khan2,*, Sagheer Abbas3, Rizwan Ali Naqvi4, Muhammad Faheem Mushtaq5, Abdur Rehman3, Afrozah Nadeem2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 141-156, 2021, DOI:10.32604/cmc.2020.013458 - 30 October 2020

    Abstract The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for… More >

  • Open Access

    ARTICLE

    Extreme Learning Machine with Elastic Net Regularization

    Lihua Guo*

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 421-427, 2020, DOI:10.32604/iasc.2020.013918

    Abstract Compared with deep neural learning, the extreme learning machine (ELM) can be quickly converged without iteratively tuning hidden nodes. Inspired by this merit, an extreme learning machine with elastic net regularization (ELM-EN) is proposed in this paper. The elastic net is a regularization method that combines LASSO and ridge penalties. This regularization can keep a balance between system stability and solution's sparsity. Moreover, an excellent optimization method, i.e., accelerated proximal gradient, is used to find the minimum of the system optimization function. Various datasets from UCI repository and two facial expression image datasets are used More >

  • Open Access

    ARTICLE

    Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

    Xiangchun Liu1, Jing Yu2,Wei Song1, 3, *, Xinping Zhang1, Lizhi Zhao1, Antai Wang4

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1385-1395, 2020, DOI:10.32604/cmc.2020.011308 - 20 August 2020

    Abstract With the development of satellite technology, the satellite imagery of the earth’s surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low consumption. As an important tool for satellite remote sensing image processing, remote sensing image classification has become a hot topic. According to the natural texture characteristics of remote sensing images, this paper combines different texture features with the Extreme Learning Machine, and proposes a new remote sensing image classification algorithm. The experimental tests are carried out through the More >

  • Open Access

    ARTICLE

    Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine)

    Iftikhar Ahmad1, *, Rayan Atteah Alsemmeari1

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1097-1111, 2020, DOI:10.32604/cmc.2020.011732 - 20 August 2020

    Abstract An IDS (intrusion detection system) provides a foremost front line mechanism to guard networks, systems, data, and information. That’s why intrusion detection has grown as an active study area and provides significant contribution to cyber-security techniques. Multiple techniques have been in use but major concern in their implementation is variation in their detection performance. The performance of IDS lies in the accurate detection of attacks, and this accuracy can be raised by improving the recognition rate and significant reduction in the false alarms rate. To overcome this problem many researchers have used different machine learning… More >

  • Open Access

    ARTICLE

    Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

    Linguo Li1, 2, Lijuan Sun1, Jian Guo1, Shujing Li2, *, Ping Jiang3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 761-775, 2020, DOI:10.32604/cmc.2020.010158 - 23 July 2020

    Abstract As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, More >

Displaying 31-40 on page 4 of 50. Per Page