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  • Open Access

    ARTICLE

    Benchmarking Performance of Document Level Classification and Topic Modeling

    Muhammad Shahid Bhatti1,*, Azmat Ullah1, Rohaya Latip2, Abid Sohail1, Anum Riaz1, Rohail Hassan3

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 125-141, 2022, DOI:10.32604/cmc.2022.020083 - 03 November 2021

    Abstract Text classification of low resource language is always a trivial and challenging problem. This paper discusses the process of Urdu news classification and Urdu documents similarity. Urdu is one of the most famous spoken languages in Asia. The implementation of computational methodologies for text classification has increased over time. However, Urdu language has not much experimented with research, it does not have readily available datasets, which turn out to be the primary reason behind limited research and applying the latest methodologies to the Urdu. To overcome these obstacles, a medium-sized dataset having six categories is… More >

  • Open Access

    ARTICLE

    Stochastic Gradient Boosting Model for Twitter Spam Detection

    K. Kiruthika Devi1,*, G. A. Sathish Kumar2

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 849-859, 2022, DOI:10.32604/csse.2022.020836 - 25 October 2021

    Abstract

    In today’s world of connectivity there is a huge amount of data than we could imagine. The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time. These social networks give the complete independence to the user to post the data either political, commercial or entertainment value. Some data may be sensitive and have a greater impact on the society as a result. The trustworthiness of data is important when it comes to public social networking sites like facebook and

    More >

  • Open Access

    ARTICLE

    Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

    R. Sakunthala Jenni*, S. Shankar

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 525-538, 2022, DOI:10.32604/csse.2022.019300 - 25 October 2021

    Abstract The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable. Phishing websites also create traffic in the entire network. Another phishing issue is the broadening malware of the entire network, thus highlighting the demand for their detection while massive datasets (i.e., big data) are processed. Despite the application of boosting mechanisms in phishing detection, these methods are prone to significant errors in their output, specifically due to the combination of all website features in the training state. The upcoming big data… More >

  • Open Access

    ARTICLE

    Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques

    A. Arunkumar1,*, D. Surendran2

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 389-404, 2022, DOI:10.32604/csse.2022.020256 - 08 October 2021

    Abstract Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder (ASD) diseases. These diseases can affect the nerves at any stage of the human being in childhood, adolescence, and adulthood. ASD is known as a behavioral disease due to the appearances of symptoms over the first two years that continue until adulthood. Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD. The detection of ASD is a very challenging task among various researchers. Machine learning… More >

  • Open Access

    ARTICLE

    Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

    Prince Waqas Khan, Yung-Cheol Byun*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1893-1913, 2021, DOI:10.32604/cmc.2021.018523 - 21 July 2021

    Abstract Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s… More >

  • Open Access

    ARTICLE

    Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers

    Mingdao Lu, Peng Wei, Mingshu He*, Yinglei Teng

    Journal of Quantum Computing, Vol.3, No.1, pp. 1-12, 2021, DOI:10.32604/jqc.2021.016315 - 20 May 2021

    Abstract With the increasing of civil aviation business, flight delay has become a key problem in civil aviation field in recent years, which has brought a considerable economic impact to airlines and related industries. The delay prediction of specific flights is very important for airlines’ plan, airport resource allocation, insurance company strategy and personal arrangement. The influence factors of flight delay have high complexity and non-linear relationship. The different situations of various regions and airports, and even the deviation of airport or airline arrangement all have certain influence on flight delay, which makes the prediction more… More >

  • Open Access

    ARTICLE

    Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model

    Mohamed F. El-Amin1,5, Abdulhamit Subasi2, Mahmoud M. Selim3,*, Awad Mousa4

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2349-2362, 2021, DOI:10.32604/cmc.2021.017102 - 13 April 2021

    Abstract In the process of oil recovery, experiments are usually carried out on core samples to evaluate the recovery of oil, so the numerical data are fitted into a non-dimensional equation called scaling-law. This will be essential for determining the behavior of actual reservoirs. The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data. This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity, which dynamics of the process. One of… More >

  • Open Access

    ARTICLE

    Click through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting

    AlAli Moneera, AlQahtani Maram, AlJuried Azizah, Taghareed AlOnizan, Dalia Alboqaytah, Nida Aslam*, Irfan Ullah Khan

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1681-1696, 2021, DOI:10.32604/cmc.2020.013466 - 26 November 2020

    Abstract Online advertisements have a significant influence over the success or failure of your business. Therefore, it is important to somehow measure the impact of your advertisement before uploading it online, and this is can be done by calculating the Click Through Rate (CTR). Unfortunately, this method is not eco-friendly, since you have to gather the clicks from users then compute the CTR. This is where CTR prediction come in handy. Advertisement CTR prediction relies on the users’ log regarding click information data. Accurate prediction of CTR is a challenging and critical process for e-advertising platforms… More >

  • Open Access

    ARTICLE

    Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

    Abdu Gumaei1,2,*, Mabrook Al-Rakhami1, Mohamad Mahmoud Al Rahhal3, Fahad Raddah H. Albogamy3, Eslam Al Maghayreh3, Hussain AlSalman1

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 315-329, 2021, DOI:10.32604/cmc.2020.012045 - 30 October 2020

    Abstract The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function More >

  • Open Access

    ARTICLE

    Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

    Xianghui Lu1, Junliang Fan2, Lifeng Wu1,*, Jianhua Dong3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004 - 12 October 2020

    Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET0) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme… More >

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