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  • Open Access

    ARTICLE

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

    Feisha Hu1, Qi Wang1,*, Haijian Shao1,2, Shang Gao1, Hualong Yu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732

    Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global More > Graphic Abstract

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

  • Open Access

    ARTICLE

    Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

    Nagaraju Jajam, Nagendra Panini Challa*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 977-997, 2023, DOI:10.32604/cmc.2023.036098

    Abstract In the insurance sector, a massive volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. The success of retention initiatives is determined not only by the accuracy of forecasting churners but also by the timing of the forecast. Previous works on churn forecast presented models for anticipating churn quarterly or monthly with an emphasis on customers’ static behavior. This paper’s objective is to calculate daily churn based on dynamic variations in client… More >

  • Open Access

    ARTICLE

    SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

    Yudong Zhang1, Muhammad Attique Khan2, Ziquan Zhu1, Shuihua Wang1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 13-26, 2023, DOI:10.32604/csse.2023.034172

    Abstract (Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due… More >

  • Open Access

    ARTICLE

    Spotted Hyena-Bat Optimized Extreme Learning Machine for Solar Power Extraction

    K. Madumathi1,*, S. Chandrasekar2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1821-1836, 2023, DOI:10.32604/csse.2023.029561

    Abstract Artificial intelligence, machine learning and deep learning algorithms have been widely used for Maximum Power Point Tracking (MPPT) in solar systems. In the traditional MPPT strategies, following of worldwide Global Maximum Power Point (GMPP) under incomplete concealing conditions stay overwhelming assignment and tracks different nearby greatest power focuses under halfway concealing conditions. The advent of artificial intelligence in MPPT has guaranteed of accurate following of GMPP while expanding the significant performance and efficiency of MPPT under Partial Shading Conditions (PSC). Still the selection of an efficient learning based MPPT is complex because each model has… More >

  • Open Access

    ARTICLE

    ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression

    M. Jamuna Rani1,*, C. Vasanthanayaki2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1953-1970, 2023, DOI:10.32604/csse.2023.028713

    Abstract Underwater imagery and transmission possess numerous challenges like lower signal bandwidth, slower data transmission bit rates, Noise, underwater blue/green light haze etc. These factors distort the estimation of Region of Interest and are prime hurdles in deploying efficient compression techniques. Due to the presence of blue/green light in underwater imagery, shape adaptive or block-wise compression techniques faces failures as it becomes very difficult to estimate the compression levels/coefficients for a particular region. This method is proposed to efficiently deploy an Extreme Learning Machine (ELM) model-based shape adaptive Discrete Cosine Transformation (DCT) for underwater images. Underwater More >

  • Open Access

    ARTICLE

    Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

    Xuhui Zhu1,3, Pingfan Xia1,3, Qizhi He2,4,*, Zhiwei Ni1,3, Liping Ni1,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 653-671, 2023, DOI:10.32604/cmes.2022.022985

    Abstract Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we More >

  • Open Access

    ARTICLE

    Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

    Atif Ikram1,2,*, Masita Abdul Jalil1, Amir Bin Ngah1, Saqib Raza6, Ahmad Salman Khan3, Yasir Mahmood3,4, Nazri Kama4, Azri Azmi4, Assad Alzayed5

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1871-1886, 2023, DOI:10.32604/cmc.2023.030818

    Abstract Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to… More >

  • Open Access

    ARTICLE

    Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

    Ashit Kumar Dutta1,*, Basit Qureshi2, Yasser Albagory3, Majed Alsanea4, Manal Al Faraj1, Abdul Rahaman Wahab Sait5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2395-2409, 2023, DOI:10.32604/csse.2023.027502

    Abstract Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input More >

  • Open Access

    ARTICLE

    Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

    Saud S. Alotaibi1, Amal Al-Rasheed2, Sami Althahabi3, Manar Ahmed Hamza4,*, Abdullah Mohamed5, Abu Sarwar Zamani4, Abdelwahed Motwakel4, Mohamed I. Eldesouki6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3305-3318, 2022, DOI:10.32604/cmc.2022.029385

    Abstract Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using… More >

  • Open Access

    ARTICLE

    Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

    R. Bhaskaran1,*, S. Saravanan1, M. Kavitha2, C. Jeyalakshmi3, Seifedine Kadry4, Hafiz Tayyab Rauf5, Reem Alkhammash6

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 235-247, 2023, DOI:10.32604/csse.2023.024399

    Abstract Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data More >

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