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  • Open Access


    Statistical Time Series Forecasting Models for Pandemic Prediction

    Ahmed ElShafee1, Walid El-Shafai2,3, Abeer D. Algarni4,*, Naglaa F. Soliman4, Moustafa H. Aly5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 349-374, 2023, DOI:10.32604/csse.2023.037408

    Abstract COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are Logistic Regression, Lasso, Ridge, ElasticNet,… More >

  • Open Access


    Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

    Ramya Nemani1, G. Jose Moses2, Fayadh Alenezi3, K. Vijaya Kumar4, Seifedine Kadry5,6,7,*, Jungeun Kim8, Keejun Han9

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 919-935, 2023, DOI:10.32604/csse.2023.034213

    Abstract Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning… More >

  • Open Access


    Estimation of Higher Heating Value for MSW Using DSVM and BSOA

    Jithina Jose*, T. Sasipraba

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 573-588, 2023, DOI:10.32604/iasc.2023.030479

    Abstract In recent decades, the generation of Municipal Solid Waste (MSW) is steadily increasing due to urbanization and technological advancement. The collection and disposal of municipal solid waste cause considerable environmental degradation, making MSW management a global priority. Waste-to-energy (WTE) using thermochemical process has been identified as the key solution in this area. After evaluating many automated Higher Heating Value (HHV) prediction approaches, an Optimal Deep Learning-based HHV Prediction (ODL-HHVP) model for MSW management has been developed. The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste, based on its oxygen, water, hydrogen, carbon, nitrogen, sulphur… More >

  • Open Access


    Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

    G. Indra1,*, N. Duraipandian2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1455-1470, 2023, DOI:10.32604/iasc.2023.027703

    Abstract A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of Tweets, it is difficult to… More >

  • Open Access


    Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

    Ashit Kumar Dutta1, S. Srinivasan2, S. N. Kumar3, T. S. Balaji4,5, Won Il Lee6, Gyanendra Prasad Joshi7, Sung Won Kim8,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5563-5576, 2022, DOI:10.32604/cmc.2022.027707

    Abstract Due to the advances of intelligent transportation system (ITSs), traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control, navigation, route mapping, etc. The traffic prediction model aims to predict the traffic conditions based on the past traffic data. For more accurate traffic prediction, this study proposes an optimal deep learning-enabled statistical analysis model. This study offers the design of optimal convolutional neural network with attention long short term memory (OCNN-ALSTM) model for traffic prediction. The proposed OCNN-ALSTM technique primarily pre-processes the traffic data by the use of… More >

  • Open Access


    Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

    Fahd N. Al-Wesabi1,2,*, Hadeel Alsolai3, Anwer Mustafa Hilal4, Manar Ahmed Hamza4, Mesfer Al Duhayyim5, Noha Negm6,7

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5803-5820, 2022, DOI:10.32604/cmc.2022.021195

    Abstract Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread, not only affected the economic status of a number of countries, but it also resulted in increased levels of Depression, Anxiety, and Stress (DAS) among people. Therefore, there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear; with tremendously-limiting measures… More >

  • Open Access


    Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

    Fareeha Sameen1, Abdul Momin Kazi2, Majida Kazmi1,*, Munir A Abbasi3, Saad Ahmed Qazi1,4, Lampros K Stergioulas3,5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 375-395, 2022, DOI:10.32604/cmc.2022.019167

    Abstract Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to… More >

  • Open Access


    Predictive Models for Cumulative Confirmed COVID-19 Cases by Day in Southeast Asia

    Yupaporn Areepong1, Rapin Sunthornwat2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 927-942, 2020, DOI:10.32604/cmes.2020.012323

    Abstract Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019. This situation has been causing a lot of problems of human beings such as economic problems, health problems. The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak. This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries. A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based… More >

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