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  • Open Access

    ARTICLE

    Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

    Yang Yang1,2,*, Pengfei Zheng3,4, Fanru Zeng5, Peng Xin6, Guoxi He1, Kexi Liao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 267-291, 2023, DOI:10.32604/cmes.2022.020220

    Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set with a 9:1 ratio. The… More >

  • Open Access

    ARTICLE

    Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

    R. Punithavathi1,*, S. Thenmozhi2, R. Jothilakshmi3, V. Ellappan4, Islam Md Tahzib Ul5

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 247-261, 2023, DOI:10.32604/csse.2023.025972

    Abstract During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse,… More >

  • Open Access

    ARTICLE

    Fault Diagnosis in Robot Manipulators Using SVM and KNN

    D. Maincer1,*, Y. Benmahamed2, M. Mansour1, Mosleh Alharthi3, Sherif S. M. Ghonein3

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1957-1969, 2023, DOI:10.32604/iasc.2023.029210

    Abstract In this paper, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based methods are to be applied on fault diagnosis in a robot manipulator. A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work. For both classifiers, the torque, the position and the speed of the manipulator have been employed as the input vector. However, it is to mention that a large database is needed and used for the training and testing phases. The SVM method used in this paper is based on the Gaussian… More >

  • Open Access

    ARTICLE

    Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

    Hussah Nasser AlEisa1, El-Sayed M. El-kenawy2,3, Amel Ali Alhussan1,*, Mohamed Saber4, Abdelaziz A. Abdelhamid5,6, Doaa Sami Khafaga1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2371-2387, 2022, DOI:10.32604/cmc.2022.030447

    Abstract Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then… More >

  • Open Access

    ARTICLE

    Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

    Hadil Shaiba1, Radwa Marzouk2, Mohamed K Nour3, Noha Negm4,5, Anwer Mustafa Hilal6,*, Abdullah Mohamed7, Abdelwahed Motwakel6, Ishfaq Yaseen6, Abu Sarwar Zamani6, Mohammed Rizwanullah6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3367-3382, 2022, DOI:10.32604/cmc.2022.030067

    Abstract The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues, this paper proposes an enhanced… More >

  • Open Access

    ARTICLE

    Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

    G. Ravikumar1, K. Venkatachalam2, Mohammed A. AlZain3, Mehedi Masud4, Mohamed Abouhawwash5,6,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 945-959, 2023, DOI:10.32604/csse.2023.024605

    Abstract Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes smart monitoring of a patient’s… More >

  • Open Access

    ARTICLE

    Movie Recommendation Algorithm Based on Ensemble Learning

    Wei Fang1,2,*, Yu Sha1, Meihan Qi1, Victor S. Sheng3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 609-622, 2022, DOI:10.32604/iasc.2022.027067

    Abstract With the rapid development of personalized services, major websites have launched a recommendation module in recent years. This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users. This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm. However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which will cause the recommended content… More >

  • Open Access

    ARTICLE

    LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

    Chengfan Li1,2, Lan Liu3,*, Junjuan Zhao1, Xuefeng Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 429-444, 2022, DOI:10.32604/cmes.2022.019202

    Abstract Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification… More >

  • Open Access

    ARTICLE

    SVM and KNN Based CNN Architectures for Plant Classification

    Sukanta Ghosh1, Amar Singh1, Kavita2,*, N. Z. Jhanjhi3, Mehedi Masud4, Sultan Aljahdali4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4257-4274, 2022, DOI:10.32604/cmc.2022.023414

    Abstract Automatic plant classification through plant leaf is a classical problem in Computer Vision. Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like. Many efforts are made to automate plant classification using plant leaf, plant flower, bark, or stem. After much effort, it has been proven that leaf is the most reliable source for plant classification. But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations, like sizes, textures, shapes, and venation. Therefore, it is required to normalize all plant leaves… More >

  • Open Access

    ARTICLE

    Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

    G. Geethamahalakshmi1,*, N. Kalaiarasi2, D. Nageswari1

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1667-1685, 2022, DOI:10.32604/iasc.2022.022728

    Abstract Solar energy is the radiant heat and light energy harvested by ultra violet rays to convert into electrical Direct Current (DC). The solar energy stood ahead of other renewable energy as it can produce a constant level of alternating current over the year with minimal harmonic distortions. The renewable energy attracts the energy harvesters as there is rise of deficiency of carbon and reduction of efficiency in thermal energy generation. The concerns associated with the solar power generation are the fluctuation in the generated direct current due to the displacement of sun and deviation in the quantity of solar rays… More >

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