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  • Open Access


    Effect of N,N-Dimethylacetamide/lithium chloride modified microcrystalline cellulose (MCC) on the processing behaviour and properties of celluloserubber (NBR and EPDM) composites


    Journal of Polymer Materials, Vol.38, No.1-2, pp. 89-100, 2021, DOI:10.32381/JPM.2021.38.1-2.8

    Abstract Rubber composites of nitrile (NBR) and Ethylene-Propylene-Diene (EPDM) containing unmodified and modified microcrystalline cellulose(MCC) are evaluated for their processing behaviour. The used modified MCC (T-MCC) was treated by N,N-dimethylacetamide/lithium chloride (DMAc/ LiCl).ATR-FTIR spectra of NBR-MCC composites have indicated N-H stretching and bending vibrations and confirmed interactions between nitrile rubber and MCC. AFM studies have indicated that the average roughness of NBR-T-MCC was significantly reduced when compared to that of NBR-untreated MCC. Important processing parameters such as scorch time and cure time are found to decrease significantly for both NBR and EPDM composites withT-MCC. Mechanical properties of these composites are found… More >

  • Open Access


    Formation of Water Quality of Surface Water Bodies Used in the Material Processing

    Tatyana Lyubimova1,*, Anatoly Lepikhin2, Yanina Parshakova1, Irina Zayakina3, Alibek Issakhov4

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 815-828, 2024, DOI:10.32604/fdmp.2024.048463

    Abstract In the process of production or processing of materials by various methods, there is a need for a large volume of water of the required quality. Today in many regions of the world, there is an acute problem of providing industry with water of a required quality. Its solution is an urgent and difficult task. The water quality of surface water bodies is formed by a combination of a large number of both natural and anthropogenic factors, and is often significantly heterogeneous not only in the water area, but also in depth. As a rule, the water supply of large… More >

  • Open Access


    Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

    Bhawna Goyal1, Ayush Dogra2, Dawa Chyophel Lepcha1, Rajesh Singh3, Hemant Sharma4, Ahmed Alkhayyat5, Manob Jyoti Saikia6,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

    Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral… More >

  • Open Access


    Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

    Qazi Mazhar ul Haq1, Fahim Arif2,3, Khursheed Aurangzeb4, Noor ul Ain3, Javed Ali Khan5, Saddaf Rubab6, Muhammad Shahid Anwar7,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172

    Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to… More >

  • Open Access


    Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

    Yumin Jo1, Jongho Paik2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4153-4176, 2024, DOI:10.32604/cmc.2024.047046

    Abstract Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream. However, when the transmission environment is unstable, problems such as reduction in the lifespan of equipment due to frequent switching and interruption, delay, and stoppage of services may occur. Therefore, applying a machine learning (ML) method, which is possible to automatically judge and classify network-related service anomaly, and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as… More >

  • Open Access


    Design of Artificial Intelligence Companion Chatbot

    Xiaoying Chen1,*, Jie Kang1, Cong Hu2

    Journal of New Media, Vol.6, pp. 1-16, 2024, DOI:10.32604/jnm.2024.045833

    Abstract With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements.… More >

  • Open Access


    A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

    Peng Geng1,*, Annan Yang2, Yan Liu3

    Journal on Artificial Intelligence, Vol.6, pp. 43-52, 2024, DOI:10.32604/jai.2024.047642

    Abstract This article presents a real-time localization method for Unmanned Aerial Vehicles (UAVs) based on continuous image processing. The proposed method employs the Scale Invariant Feature Transform (SIFT) algorithm to identify key points in multi-scale space and generate descriptor vectors to match identical objects across multiple images. These corresponding points in the image provide pixel positions, which can be combined with transformation equations, allow for the calculation of the UAV’s actual ground position. Additionally, the physical coordinates of matching points in the image can be obtained, corresponding to the UAV’s physical coordinates. The method achieves real-time positioning and tracking during UAV… More >

  • Open Access


    Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

    M. L. Maruthi Prasad*, R. Santhosh

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 1-13, 2024, DOI:10.32604/iasc.2023.036628

    Abstract This research concentrates to model an efficient thyroid prediction approach, which is considered a baseline for significant problems faced by the women community. The major research problem is the lack of automated model to attain earlier prediction. Some existing model fails to give better prediction accuracy. Here, a novel clinical decision support system is framed to make the proper decision during a time of complexity. Multiple stages are followed in the proposed framework, which plays a substantial role in thyroid prediction. These steps include i) data acquisition, ii) outlier prediction, and iii) multi-stage weight-based ensemble learning process (MS-WEL). The weighted… More >

  • Open Access


    Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

    Yongcan Zhu1,2, Jiawen Wang1, Ye Zhang1,2, Long Zhao1, Botao Jiang1, Xinbo Huang1,*

    Energy Engineering, Vol.121, No.4, pp. 895-911, 2024, DOI:10.32604/ee.2023.041002

    Abstract The accumulation of snow and ice on PV modules can have a detrimental impact on power generation, leading to reduced efficiency for prolonged periods. Thus, it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules. To address this issue, the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images. Furthermore, the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules, allowing for the establishment of a residual ice and… More >

  • Open Access


    Classification and clustering of buildings for understanding urban dynamics

    A framework for processing spatiotemporal data

    Perez Joan1, Fusco Giovanni1, Sadahiro Yukio2

    Revue Internationale de Géomatique, Vol.31, No.2, pp. 303-327, 2022, DOI:10.3166/RIG.31.303-327© 2022

    Abstract This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and for each period is computed,… More >

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