Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (18)
  • Open Access

    ARTICLE

    TGAIN: Geospatial Data Recovery Algorithm Based on GAIN-LSTM

    Lechan Yang1,*, Li Li2, Shouming Ma3

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1471-1489, 2024, DOI:10.32604/cmc.2024.056379 - 15 October 2024

    Abstract Accurate geospatial data are essential for geographic information systems (GIS), environmental monitoring, and urban planning. The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data. In this paper, we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery. Existing geospatial data recovery methods require complete datasets for training, resulting in time-consuming data recovery and lack of generalization. To address these issues, we propose a GAIN-LSTM-based geospatial data recovery method (TGAIN), which consists of two main works:… More >

  • Open Access

    ARTICLE

    A Comprehensive Analysis of the Thermo-Chemical Properties of Sudanese Biomass for Sustainable Applications

    Wadah Mohammed1,2, Zeinab Osman2, Salah Elarabi3, Bertrand Charrier1,*

    Journal of Renewable Materials, Vol.12, No.4, pp. 721-736, 2024, DOI:10.32604/jrm.2024.031050 - 12 June 2024

    Abstract The chemical composition and thermal properties of natural fibers are the most critical variables that determine the overall properties of the fibers and influence their processing and use in different sustainable applications, such as their conversion into bioenergy and biocomposites. Their thermal and mechanical properties can be estimated by evaluating the content of cellulose, lignin, and other extractives in the fibers. In this research work, the chemical composition and thermal properties of three fibers, namely bagasse, kenaf bast fibers, and cotton stalks, were evaluated to assess their potential utilization in producing biocomposites and bioenergy materials.… More >

  • Open Access

    ARTICLE

    Surface Morphology and Thermo-Electrical Energy Analysis of Polyaniline (PANI) Incorporated Cotton Fabric

    Md. Shohan Parvez1,2, Md. Mustafizur Rahman1,3,*, Mahendran Samykano1, Mohammad Yeakub Ali4

    Energy Engineering, Vol.121, No.1, pp. 1-12, 2024, DOI:10.32604/ee.2023.027472 - 27 December 2023

    Abstract With the exponential development in wearable electronics, a significant paradigm shift is observed from rigid electronics to flexible wearable devices. Polyaniline (PANI) is considered as a dominant material in this sector, as it is endowed with the optical properties of both metal and semiconductors. However, its widespread application got delineated because of its irregular rigid form, level of conductivity, and precise choice of solvents. Incorporating PANI in textile materials can generate promising functionality for wearable applications. This research work employed a straightforward in-situ chemical oxidative polymerization to synthesize PANI on Cotton fabric surfaces with varying dopant… More >

  • Open Access

    ARTICLE

    Synthesis and Characterization of Bisphenol-C Epoxy Crotonate and Its Fiber-Reinforced Composites

    PARSOTAM H. PARSANIA1,*, JIGNESH V. PATEL2, JIGNESH P. PATEL3

    Journal of Polymer Materials, Vol.40, No.3-4, pp. 271-284, 2023, DOI:10.32381/JPM.2023.40.3-4.9

    Abstract Bisphenol-C epoxy crotonate resin was synthesized by reacting 8.09g epoxy resin of bisphenolC, and 2.15g crotonic acid using 25 mL 1,4-dioxane as a solvent, and 1 mL triethylamine as a catalyst at reflux temperature for 1-6 h. Solid epoxy crotonate (ECCR) is highly soluble in common organic solvents. ECCR was characterized by its acid (24.5-1.5 mg KOH/g) and hydroxyl (504.5-678.4 mg KOH/g) values. The structure of ECCR is supported by FTIR and 1 HNMR spectroscopic methods. A DSC endothermic transition at 229o C indicated melting followed by thermal polymerization of ECCR. ECCR is thermally stable… More >

  • Open Access

    ARTICLE

    Flexible Biofoams Based on Furanics and Fatty Acids Esterified Tannin

    Elham Azadeh1, Ummi Hani Abdullah2,3, Christine Gerardin1,*, Antonio Pizzi1,*, Philippe Gerardin1, Cesar Segovia4

    Journal of Renewable Materials, Vol.11, No.10, pp. 3625-3645, 2023, DOI:10.32604/jrm.2023.030373 - 10 August 2023

    Abstract Water repellant, flexible biofoams using tannin esterified with various fatty acid chains, namely lauric, palmitic and oleic acids, by reaction with lauryl chloride, palmitoyl chloride, and oleyl chloride were developed and their characteristics compared with the equivalently esterified rigid biofoams. Glycerol, while initially added to control the reaction temperature, was used as a plasticizer yielding flexible biofoams presenting the same water repellant character that the equivalent rigid foams. Acetaldehyde was used as the cross-linking agent instead of formaldehyde, as it showed a better performance with the esterified tannin. The compression results showed a significant decrease… More >

  • Open Access

    ARTICLE

    A Model Training Method for DDoS Detection Using CTGAN under 5GC Traffic

    Yea-Sul Kim1, Ye-Eun Kim1, Hwankuk Kim2,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550 - 26 May 2023

    Abstract With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer… More >

  • Open Access

    ARTICLE

    Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

    Subhajit Chatterjee1, Yung-Cheol Byun2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5507-5525, 2023, DOI:10.32604/cmc.2023.032843 - 28 December 2022

    Abstract The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with… More >

  • Open Access

    ARTICLE

    Developing a Secure Framework Using Feature Selection and Attack Detection Technique

    Mahima Dahiya*, Nitin Nitin

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430 - 31 October 2022

    Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature… More >

  • Open Access

    ARTICLE

    Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

    Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143 - 27 September 2021

    Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a… More >

  • Open Access

    ARTICLE

    Identification of PtGai (a DELLA protein) in trifoliate orange and expression patterns in response to drought stress

    XIAOFEN CHENG1, ABEER HASHEM2,3, ELSAYED FATHI ABD_ALLAH4, QIANGSHENG WU1,5,*, KAMIL KUČA5,*

    BIOCELL, Vol.45, No.6, pp. 1687-1694, 2021, DOI:10.32604/biocell.2021.017581 - 01 September 2021

    Abstract Gibberellins (GAs) are an important hormone in regulating plant growth and development, and DELLA protein is an essential negative regulator of GA signal transduction. The aim of the study was to clone a GA-inhibiting protein DELLA from trifoliate orange (Poncirus trifoliata L. Raf.) and to analyze the bioinformations and expression patterns of the protein gene in tissues and in response to drought stress. A DELLA protein was isolated from trifoliate orange and named as PtGai (Genebank number: MZ170959). The PtGai protein had 1731 bp open reading frames, along with 576 amino acid codes, and also grouped with… More >

Displaying 1-10 on page 1 of 18. Per Page