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  • Open Access

    ARTICLE

    Enrichment of Crop Yield Prophecy Using Machine Learning Algorithms

    R. Kingsy Grace*, K. Induja, M. Lincy

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 279-296, 2022, DOI:10.32604/iasc.2022.019947

    Abstract Strong associations exist between the crop productivity and the seasonal, biological, economical causes in natural ecosystems. The linkages like climatic conditions, health of a soil, growth of crop, irrigation, fertilizers, temperature, rainwater, pesticides desired to be preserved in comprehensively managed crop lands which impacts the crop potency. Crop yield prognosis plays a vibrant part in agricultural planning, administration and environs sustainability. Advancements in the field of Machine Learning have perceived novel expectations to improve the prediction performance in Agriculture. Highly gratifying prediction of crop yield helps the majority of agronomists for their rapid decision-making in the choice of crop to… More >

  • Open Access

    ARTICLE

    Acoustic Emission Characteristics of Different Bamboo and Wood Materials in Bending Failure Process

    Ting Wang1, Zhiqiang Wang1,*, Yin Yang1, Jianhui Zhou2,*

    Journal of Renewable Materials, Vol.10, No.2, pp. 527-540, 2022, DOI:10.32604/jrm.2022.017955

    Abstract The acoustic emission (AE) technique can perform non-destructive monitoring of the internal damage development of bamboo and wood materials. In this experiment, the mechanical properties of different bamboo and wood (bamboo scrimber, bamboo plywood and SPF (Spruce-pine-fir) dimension lumber) during four-point loading tests were compared. The AE activities caused by loadings were investigated through the single parameter analysis and K-means cluster analysis. Results showed that the bending strength of bamboo scrimber was 3.6 times that of bamboo plywood and 2.7 times that of SPF dimension lumber, respectively. Due to the high strength and toughness of bamboo, the AE signals of… More > Graphic Abstract

    Acoustic Emission Characteristics of Different Bamboo and Wood Materials in Bending Failure Process

  • Open Access

    ARTICLE

    YOLOv2PD: An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

    Chintakindi Balaram Murthy1, Mohammad Farukh Hashmi1, Ghulam Muhammad2,3,*, Salman A. AlQahtani2,3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781

    Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature extraction ability. In addition, one… More >

  • Open Access

    ARTICLE

    Performances of K-Means Clustering Algorithm with Different Distance Metrics

    Taher M. Ghazal1,2, Muhammad Zahid Hussain3, Raed A. Said5, Afrozah Nadeem6, Mohammad Kamrul Hasan1, Munir Ahmad7, Muhammad Adnan Khan3,4,*, Muhammad Tahir Naseem3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 735-742, 2021, DOI:10.32604/iasc.2021.019067

    Abstract Clustering is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where the similarity between elements in the same cluster must be smaller enough to the similarity between elements of different clusters. Hence, this similarity can be considered as a distance measure. One of the most popular clustering algorithms is K-means, where distance is measured between every point of the dataset and centroids of clusters to find similar data objects and assign them to the nearest… More >

  • Open Access

    ARTICLE

    Binaural Speech Separation Algorithm Based on Deep Clustering

    Lin Zhou1,*, Kun Feng1, Tianyi Wang1, Yue Xu1, Jingang Shi2

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 527-537, 2021, DOI:10.32604/iasc.2021.018414

    Abstract Neutral network (NN) and clustering are the two commonly used methods for speech separation based on supervised learning. Recently, deep clustering methods have shown promising performance. In our study, considering that the spectrum of the sound source has time correlation, and the spatial position of the sound source has short-term stability, we combine the spectral and spatial features for deep clustering. In this work, the logarithmic amplitude spectrum (LPS) and the interaural phase difference (IPD) function of each time frequency (TF) unit for the binaural speech signal are extracted as feature. Then, these features of consecutive frames construct feature map,… More >

  • Open Access

    ARTICLE

    Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

    Bin Xu1,2,3, Lu Zhang1, Zipeng Xu1, Yichuan Liu1, Jinming Chai1, Sichong Qin4, Yanfei Sun1,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1671-1686, 2021, DOI:10.32604/cmc.2021.018395

    Abstract In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the… More >

  • Open Access

    ARTICLE

    Safest Route Detection via Danger Index Calculation and K-Means Clustering

    Isha Puthige1, Kartikay Bansal1, Chahat Bindra1, Mahekk Kapur1, Dilbag Singh1, Vipul Kumar Mishra1, Apeksha Aggarwal1, Jinhee Lee2, Byeong-Gwon Kang2, Yunyoung Nam2,*, Reham R. Mostafa3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2761-2777, 2021, DOI:10.32604/cmc.2021.018128

    Abstract The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations. Using the New York City dataset, which provides us with location tagged crime statistics; we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one. The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results. Moreover, a comparative analysis has been performed among various clustering techniques to obtain best results. we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide… More >

  • Open Access

    ARTICLE

    Community Detection in Aviation Network Based on K-means and Complex Network

    Hang He1,*, Zhenhan Zhao1, Weiwei Luo1, Jinghui Zhang2

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 251-264, 2021, DOI:10.32604/csse.2021.017296

    Abstract With the increasing number of airports and the expansion of their scale, the aviation network has become complex and hierarchical. In order to investigate the complex network characteristics of aviation networks, this paper constructs a Chinese aviation network model and carries out related research based on complex network theory and K-means algorithm. Initially, the P-space model is employed to construct the Chinese aviation network model. Then, complex network indicators such as degree, clustering coefficient, average path length, betweenness and coreness are selected to investigate the complex characteristics and hierarchical features of aviation networks and explore their causes. Secondly, using K-means… More >

  • Open Access

    ARTICLE

    Oversampling Method Based on Gaussian Distribution and K-Means Clustering

    Masoud Muhammed Hassan1, Adel Sabry Eesa1,*, Ahmed Jameel Mohammed2, Wahab Kh. Arabo1

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 451-469, 2021, DOI:10.32604/cmc.2021.018280

    Abstract Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple… More >

  • Open Access

    ARTICLE

    Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

    Omer Ali1,2, Mohamad Khairi Ishak1,*, Muhammad Kamran Liaquat Bhatti2

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 747-766, 2021, DOI:10.32604/cmc.2021.018098

    Abstract The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which are worth investigating. Current supervised… More >

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