Lingsheng Li¹, Weiqing Bai², Chong Han^2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1613-1640, 2024, DOI:10.32604/cmc.2024.056073 - 15 October 2024

Abstract Gestures are one of the most natural and intuitive approach for human-computer interaction. Compared with traditional camera-based or wearable sensors-based solutions, gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free, privacy-preserving and less environment-dependence. Although there have been many recent studies on hand gesture recognition, the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in short-range applications. In this paper, we present a hand gesture recognition method named multiscale feature fusion (MSFF) to accurately identify micro hand gestures. In MSFF, not only the More >

Yusuf Olanrewaju Busari^1,2,*, Issah Sadiq Ibrahim¹, Kabir S. Ajao¹, Norliana Mohd Abbas², James Obafemi Adegbola¹, Hassan Kobe Ibrahim³, Abdallah Reghioua⁴, Yusuf Lanre Shuaib-Babata¹, Rachael Oluwatoyosi Idowu¹

Journal of Polymer Materials, Vol.41, No.3, pp. 117-129, 2024, DOI:10.32604/jpm.2024.055895 - 30 September 2024

Abstract The research paper focuses on manufacturing composite materials from waste polyethylene terephthalate (PET) reinforced with soda-lime glass to provide a lightweight, less brittle, and high rust resistance when exposed to hazardous environment. In developing nations such as Nigeria, there is a significant surge in the volume of bottled water and other packaging materials used in households, leading to a rapid accumulation of biodegradable waste, that presents concerns such as the creation of landfills and health issues. PET are thermoplastic polymer that can be melted and shaped into various objects. This study involves the incorporation of… More >

Aniqua Nusrat Zereen¹, Abir Das², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886 - 12 September 2024

Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

Rafael Ariza-Flores¹, Rafael Ambriz-Cervantes², Pedro Cadena-Iñiguez³, Luis A. Gálvez-Marroquín⁴, Miguel A. Cano-García^4,*

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 1805-1818, 2024, DOI:10.32604/phyton.2024.050093 - 30 August 2024

Abstract The objective of this study consisted of evaluating the effect of the application of chitosan (Q), 1-methylcyclopropene (1-MCP) under two controlled temperatures on some physical, physiological, and chemical parameters contributing to the quality of Persian lime Citrus latifolia fruits. Eight treatments were evaluated, resulting from the combination of four senescence retardant applications (Q, 1-MCP, 1-MCP + Q, and without application) on fruits stored at two temperatures (12/20°C). Epidermis color (luminosity, chromaticity, and hue), fruit appearance, respiration, weight loss, total juice content, total soluble solids, and titratable acidity were registered during the study. The results indicate that More >

Yonghui Lin¹, Zhengyu Zhu^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 663-677, 2024, DOI:10.32604/cmes.2023.028584 - 22 September 2023

Abstract In this paper, we investigate IRS-aided user cooperation (UC) scheme in millimeter wave (mmWave) wireless-powered sensor networks (WPSN), where two single-antenna users are wireless powered in the wireless energy transfer (WET) phase first and then cooperatively transmit information to a hybrid access point (AP) in the wireless information transmission (WIT) phase, following which the IRS is deployed to enhance the system performance of the WET and WIT. We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots, power allocations, and the phase shifts of the IRS. Due to the non-convexity of the More >

Wenbo Zhu¹, Neng Liu¹, Zhengjun Zhu^2,*, Haibing Li¹, Weijie Fu¹, Zhongbo Zhang¹, Xinghao Zhang¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 259-273, 2023, DOI:10.32604/iasc.2023.041860 - 27 February 2024

Abstract The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam, impurities, and changing lighting conditions that disrupt the collection of tailings images. To address this challenge, we present a method for ash content detection in coal slime flotation tailings. This method utilizes chromatographic filter paper sampling and a multi-scale residual network, which we refer to as MRCN. Initially, tailings are sampled using chromatographic filter paper to obtain static tailings images, effectively isolating interference factors at the flotation site. Subsequently, the MRCN, consisting of… More >

Fayaz Hassan^1,*, Jianguo Yu¹, Zafi Sherhan Syed², Arif Hussain Magsi³, Nadeem Ahmed⁴

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3185-3208, 2023, DOI:10.32604/cmc.2023.044650 - 26 December 2023

Abstract Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100%… More >

Ying Ji^*, Xi Liu^*

Journal of Renewable Materials, Vol.11, No.11, pp. 3977-3989, 2023, DOI:10.32604/jrm.2023.028439 - 31 October 2023

Abstract The problem of low disposal and utilization rate of bulk industrial solid waste needs to be solved. In this paper, a high-activity admixture composed of steel slag-phosphate slag-limestone powder was proposed for most of the solid waste with low activity and a negative impact on concrete workability, combining the characteristics of each solid waste. The paper demonstrates the feasibility and explains the principle of the composite system in terms of water requirement of standard consistency, setting time, workability, and mechanical properties, combined with the composition of the phases, hydration temperature, and microscopic morphology. The results… More > Graphic Abstract

Maoxia Chen^1,2,*, Qixuan Zhou¹, Jiayue Zhang¹, Jiaoyang Li¹, Wei Zhang¹, Huan Liu¹

Journal of Renewable Materials, Vol.11, No.10, pp. 3711-3723, 2023, DOI:10.32604/jrm.2023.027826 - 10 August 2023

Abstract Thermal alkaline hydrolysis is a common pretreatment method for the utilization of excess activated sludge (EAS). Owing to strict environment laws and need for better energy utilization, new methods were developed in this study to improve the efficiency of pretreatment method. Direct thermal hydrolysis (TH), pasteurized thermal hydrolysis (PTH), and alkaline pasteurized thermal hydrolysis (PTH + CaO and PTH + NaOH) methods were used to treat EAS. Each method was compared and analyzed in terms of dissolution in ammonium nitrogen (NH₄ ⁺ -N) and soluble COD (SCOD) in EAS. Furthermore, the removal of tetracycline resistance genes… More >

Umar Abdulkadir Isa^*, Anil Fernando^*

Journal on Artificial Intelligence, Vol.5, pp. 31-42, 2023, DOI:10.32604/jai.2023.040213 - 11 August 2023

Abstract Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%–98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating… More >