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  • Open Access

    ARTICLE

    Physicochemical Properties of Combustion Ashes of Some Trees (Urban Pruning) Present in the Neotropical Region

    John Freddy Gelves-Díaz1,*, Ludovic Dorkis2, Richard Monroy-Sepúlveda1, Sandra Rozo-Rincón1, Yebrail Alexis Romero-Arcos1

    Journal of Renewable Materials, Vol.11, No.10, pp. 3769-3787, 2023, DOI:10.32604/jrm.2023.029270 - 10 August 2023

    Abstract Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes. In accordance with the above, in this paper, we wanted to study the ash from urban pruning residues that are generated in cities in the Neotropics. Species such as Licania tomentosa, Azadirachta indica, Ficus benjamina, Terminalia catappa, Leucaena leucocephala, Prosopis juliflora and Pithecellobium dulce were selected because they have been previously studied and showed potential for thermal energy generation. These materials were calcined in an oxidizing atmosphere and characterized by X-ray diffraction and fluorescence, scanning electron microscopy with microchemistry, BET surface area,… More > Graphic Abstract

    Physicochemical Properties of Combustion Ashes of Some Trees (Urban Pruning) Present in the Neotropical Region

  • Open Access

    ARTICLE

    Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

    Huabin Wang1, Ruichao Mo2, Yuping Chen3, Weiwei Lin2,4,*, Minxian Xu5, Bo Liu3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2025-2047, 2023, DOI:10.32604/cmes.2023.026910 - 26 June 2023

    Abstract Nowadays, the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network, such as pedestrian and vehicle detection, to provide efficient intelligent services to mobile users. However, as the accuracy requirements continue to increase, the components of deep learning models for pedestrian and vehicle detection, such as YOLOv4, become more sophisticated and the computing resources required for model training are increasing dramatically, which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance. For… More >

  • Open Access

    ARTICLE

    Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-Based Models

    Feezan Ahmad1, Xiaowei Tang1, Jilei Hu2,*, Mahmood Ahmad3,4, Behrouz Gordan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 455-487, 2023, DOI:10.32604/cmes.2023.025993 - 23 April 2023

    Abstract Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This paper’s reduced error pruning (REP) tree and random tree (RT) models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering. The data set of this study includes five parameters, namely slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The available data is split into two categories: training (75%) and test (25%) sets. The output of the RT and REP tree models is evaluated using performance measures including accuracy (Acc), Matthews… More >

  • Open Access

    ARTICLE

    Single Point Cutting Tool Fault Diagnosis in Turning Operation Using Reduced Error Pruning Tree Classifier

    E. Akshay1, V. Sugumaran1,*, M. Elangovan2

    Structural Durability & Health Monitoring, Vol.16, No.3, pp. 255-270, 2022, DOI:10.32604/sdhm.2022.0271 - 18 July 2022

    Abstract Tool wear is inevitable in daily machining process since metal cutting process involves the chip rubbing the tool surface after it has been cut by the tool edge. Tool wear dominantly influences the deterioration of surface finish, geometric and dimensional tolerances of the workpiece. Moreover, for complete utilization of cutting tools and reduction of machine downtime during the machining process, it becomes necessary to understand the development of tool wear and predict its status before happening. In this study, tool condition monitoring system was used to monitor the behavior of a single point cutting tool… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Pruning Technique for Low Power Approximate Computing

    B. Sakthivel1,*, K. Jayaram2, N. Manikanda Devarajan3, S. Mahaboob Basha4, S. Rajapriya5

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 397-406, 2022, DOI:10.32604/csse.2022.021637 - 02 December 2021

    Abstract Approximate Computing is a low power achieving technique that offers an additional degree of freedom to design digital circuits. Pruning is one of the types of approximate circuit design technique which removes logic gates or wires in the circuit to reduce power consumption with minimal insertion of error. In this work, a novel machine learning (ML) -based pruning technique is introduced to design digital circuits. The machine-learning algorithm of the random forest decision tree is used to prune nodes selectively based on their input pattern. In addition, an error compensation value is added to the… More >

  • Open Access

    ARTICLE

    Weighted or Non-Weighted Negative Tree Pattern Discovery from SensorRich Environments

    Juryon Paik*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 193-204, 2020, DOI:10.31209/2019.100000140

    Abstract It seems to be sure that the IoT is one of promising potential topics today. Sensors are the one that lead the current IoT revolution. The advances of sensor-rich environments produce the massive volume of raw data that is enlarging faster than the rate at which it is being handled. JSON is a lightweight data-interchange format and preferred for IoT applications. Before JSON, XML was de factor standard format for interchanging data. The common point is that their structure scheme is the tree. Tree structure provides data exchangeability and heterogeneity, which encourages user-flexibilities. Therefore, JSON More >

  • Open Access

    ARTICLE

    Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis

    Kemal Akyol1, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 619-632, 2020, DOI:10.32604/cmes.2020.07632 - 01 February 2020

    Abstract Parkinson’s disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation More >

  • Open Access

    ARTICLE

    An Improved K-nearest Neighbor Algorithm Using Tree Structure and Pruning Technology

    Juan Li

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 35-48, 2019, DOI:10.31209/2018.100000003

    Abstract K-Nearest Neighbor algorithm (KNN) is a simple and mature classification method. However there are susceptible factors influencing the classification performance, such as k value determination, the overlarge search space, unbalanced and multi-class patterns, etc. To deal with the above problems, a new classification algorithm that absorbs tree structure, tree pruning and adaptive k value method was proposed. The proposed algorithm can overcome the shortcoming of KNN, improve the performance of multi-class and unbalanced classification, reduce the scale of dataset maintaining the comparable classification accuracy. The simulations are conducted and the proposed algorithm is compared with several existing More >

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