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  • Open Access

    ARTICLE

    Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

    Mehdi Jamei1, Nadjem Bailek2,*, Kada Bouchouicha3, Muhammed A. Hassan4, Ahmed Elbeltagi5, Alban Kuriqi6, Nadhir Al-Ansar7, Javier Almorox8, El-Sayed M. El-kenawy9,10

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1625-1640, 2023, DOI:10.32604/cmc.2023.031406

    Abstract Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR),… More >

  • Open Access

    ARTICLE

    Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

    Nitin Sharma1, Mohd Anul Haq2, Pawan Kumar Dahiya3, B. R. Marwah4, Reema Lalit5, Nitin Mittal6, Ismail Keshta7,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 881-895, 2023, DOI:10.32604/cmc.2023.027899

    Abstract Every developing country relies on transportation, and there has been an exponential expansion in the development of various sorts of vehicles with various configurations, which is a major component strengthening the automobile sector. India is a developing country with increasing road traffic, which has resulted in challenges such as increased road accidents and traffic oversight issues. In the lack of a parametric technique for accurate vehicle recognition, which is a major worry in terms of reliability, high traffic density also leads to mayhem at checkpoints and toll plazas. A system that combines an intelligent domain approach with more sustainability indices… More >

  • Open Access

    ARTICLE

    A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

    Hao Han, Wei Wang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494

    Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm… More >

  • Open Access

    ARTICLE

    Prediction of Alzheimer’s Using Random Forest with Radiomic Features

    Anuj Singh*, Raman Kumar, Arvind Kumar Tiwari

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 513-530, 2023, DOI:10.32604/csse.2023.029608

    Abstract Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are… More >

  • Open Access

    ARTICLE

    Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

    M. Valavan, S. Rita*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 231-245, 2023, DOI:10.32604/csse.2023.026508

    Abstract Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.… More >

  • Open Access

    ARTICLE

    Prediction Model for a Good Learning Environment Using an Ensemble Approach

    S. Subha1,*, S. Baghavathi Priya2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2081-2093, 2023, DOI:10.32604/csse.2023.028451

    Abstract This paper presents an efficient prediction model for a good learning environment using Random Forest (RF) classifier. It consists of a series of modules; data preprocessing, data normalization, data split and finally classification or prediction by the RF classifier. The preprocessed data is normalized using min-max normalization often used before model fitting. As the input data or variables are measured at different scales, it is necessary to normalize them to contribute equally to the model fitting. Then, the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation (k = 10) is used to validate the… More >

  • Open Access

    ARTICLE

    Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

    Abdullah A Asiri1, Akhtar Badshah2, Fazal Muhammad3,*, Hassan A Alshamrani1, Khalil Ullah4, Khalaf A Alshamrani1, Samar Alqhtani5, Muhammad Irfan6, Hanan Talal Halawani7, Khlood M Mehdar8

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5075-5089, 2022, DOI:10.32604/cmc.2022.031156

    Abstract Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response to audiovisual stimuli for the… More >

  • Open Access

    ARTICLE

    DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

    S. Raghavendra1, Vasudev Parvati2, R. Manjula3, Ashok Kumar Nanda4, Ruby Singh5, D. Lakshmi6, S. Velmurugan7,*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1353-1368, 2023, DOI:10.32604/iasc.2023.027977

    Abstract In contemporary medicine, cardiovascular disease is a major public health concern. Cardiovascular diseases are one of the leading causes of death worldwide. They are classified as vascular, ischemic, or hypertensive. Clinical information contained in patients’ Electronic Health Records (EHR) enables clinicians to identify and monitor heart illness. Heart failure rates have risen dramatically in recent years as a result of changes in modern lifestyles. Heart diseases are becoming more prevalent in today’s medical setting. Each year, a substantial number of people die as a result of cardiac pain. The primary cause of these deaths is the improper use of pharmaceuticals… More >

  • Open Access

    ARTICLE

    An ISSA-RF Algorithm for Prediction Model of Drug Compound Molecules Antagonizing ERα Gene Activity

    Minxi Rong1, Yong Li1,*, Xiaoli Guo1,*, Tao Zong2, Zhiyuan Ma2, Penglei Li2

    Oncologie, Vol.24, No.2, pp. 309-327, 2022, DOI:10.32604/oncologie.2022.021256

    Abstract Objectives: The ERα biological activity prediction model is constructed by the compound molecular data of the anti-breast cancer therapeutic target ERα and its biological activity data, which improves the screening efficiency of anti-breast cancer drug candidates and saves the time and cost of drug development. Methods: In this paper, Ridge model is used to screen out molecular descriptors with a high degree of influence on the biological activity of Erα and divide datasets with different numbers of the molecular descriptors by screening results. Random Forest (RF) is trained by Root Mean Square Error (RMSE) and Coefficient of determination (R2) to… More >

  • Open Access

    ARTICLE

    Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

    Wen Yee Wong1, Ayman Khallel Ibrahim Al-Ani1, Khairunnisa Hasikin1,*, Anis Salwa Mohd Khairuddin2, Sarah Abdul Razak3, Hanee Farzana Hizaddin4, Mohd Istajib Mokhtar5, Muhammad Mokhzaini Azizan6

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 1011-1038, 2022, DOI:10.32604/cmes.2022.019244

    Abstract Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal–polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features… More >

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