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Henderson Office Address Change Notification
Frontiers in Heat and Mass Transfer Welcomes Prof. Chun Yang as Editor-in-Chief
In Memoriam: Professor Kazuo Umezawa
Tech Science Press Collaborates with STM to Promote Open Access Publishing
Rising Talents in Engineering win CMES 2022 Young Researcher Award
Congenital Heart Disease and Children's Hospital of Nanjing Medical University Join Forces in Advancing...
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ARTICLE
Zhaoan Wang1, Shaoping Xiao1,*, Cheryl Reuben2, Qiyu Wang2, Jun Wang2
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 285-297, 2023, DOI:10.32604/cmc.2023.044366
Abstract This paper presents designing sequence-to-sequence recurrent neural network (RNN) architectures for a novel study to predict soil NOx emissions, driven by the imperative of understanding and mitigating environmental impact. The study utilizes data collected by the Environmental Protection Agency (EPA) to develop two distinct RNN predictive models: one built upon the long-short term memory (LSTM) and the other utilizing the gated recurrent unit (GRU). These models are fed with a combination of historical and anticipated air temperature, air moisture, and NOx emissions as inputs to forecast future NOx emissions. Both LSTM and GRU models can capture the intricate pulse patterns… More >
Muhammad Naeem Akbar1,*, Seemab Khan2, Muhammad Umar Farooq1, Majed Alhaisoni3, Usman Tariq4, Muhammad Usman Akram1
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3275-3295, 2023, DOI:10.32604/cmc.2023.039289
Abstract The combination of spatiotemporal videos and essential features can improve the performance of human action recognition (HAR); however, the individual type of features usually degrades the performance due to similar actions and complex backgrounds. The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information. This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net. On a few selected datasets, deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model. Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep… More >
Deng Lu1, Xujun Lang1, Bo Yang1, Ziyang Li1, Hang Geng2,*
Energy Engineering, Vol.120, No.10, pp. 2287-2306, 2023, DOI:10.32604/ee.2023.029140
Abstract The four-circuit parallel line on the same tower effectively solves the problems faced by the line reconstruction and construction under the condition of the increasing shortage of transmission corridors. Optimizing the conductor and phase sequence arrangement of multiple transmission lines is conducive to improving electromagnetic and electrostatic coupling caused by electromagnetic problems. This paper uses the ATP-EMTP simulation software to build a 500 kV multi-circuit transmission line on the same tower. It stimulates the induced voltage and current values of different line lengths, tower spacing, vertical and horizontal spacing between different circuits, phase sequence arrangement, and nominal tower height. Moreover,… More >
RETRACTION
Siti Julia Rosli1,2, Hasliza A Rahim1,2,*, Khairul Najmy Abdul Rani1,2, Ruzelita Ngadiran2,3, Wan Azani Mustafa3,4, Muzammil Jusoh1,2, Mohd Najib Mohd Yasin1,2, Thennarasan Sabapathy1,2, Mohamedfareq Abdulmalek5, Wan Suryani Firuz Wan Ariffin2, Ahmed Alkhayyat6
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2571-2571, 2023, DOI:10.32604/cmc.2023.045533
Abstract This article has no abstract. More >
XIAO-JUN ZHOU1,*, YU-WEI TIAN1, RUI-HAN LI2
BIOCELL, Vol.47, No.8, pp. 1907-1913, 2023, DOI:10.32604/biocell.2023.030191
Abstract Background: Acer griseum Pax is an endangered species endemic to China with both ornamental and economic value. However, the lack of information on its genome size and characteristics hinders further work at the genome level. Methods: This paper applied bioinformatics methods to predict the characteristics and patterns of the A. griseum genome, providing an important basis for formulating its whole-genome sequencing scheme. This study also characterized the simple sequence repeats (SSRs) of A. griseum, laying the foundation for the development and application of genome-wide SSR markers. In this study, PE150 sequencing was performed by the BGI MGISEQ platform, and the… More >
Safdar Khan1, Muhammad Attique Khan2, Jamal Hussain Shah1,*, Faheem Shehzad2, Taerang Kim3, Jae-Hyuk Cha3
Computer Systems Science and Engineering, Vol.47, No.2, pp. 2337-2360, 2023, DOI:10.32604/csse.2023.040410
Abstract Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for HSAR in video frames. The… More >
Xiao Han, Songnan Yang, Xueying Li, Qiulin Wu, Yongyi Xing, Jun Zhang*, Fenglou Ling*
Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2577-2589, 2023, DOI:10.32604/phyton.2023.029440
Abstract Peanut, with high oil content, has been a major oil and food crop globally. The compositions of the fatty acids are the common factors in determining the oil quality. In the present study, an F2 segregated population with 140 individuals derived from the cross of Weihua8 (a cultivar) and 12L49 (a line with high oleic acid concentration) was used to construct a genetic map and conduct QTL mapping analysis. A total of 103 polymorphic SSR primers were utilized for genotyping the RILs and finally generating the SSR loci. Within the 103 SSR loci, a genetic linkage map, covering a total… More >
Clément Moreau, Thomas Devogele, Laurent Etienne
Revue Internationale de Géomatique, Vol.29, No.1, pp. 107-128, 2019, DOI:10.3166/rig.2019.00077
Abstract Understanding mobility, whether physical in the spatial sense or virtual in the web navigation sense, raises many challenges in terms of monitoring individuals, spatial planning or activity recommendations. Having access today to many resources on the contextual nature of this mobility, one of the current concerns is to succeed in identifying groups of similar individuals with regard to their mobility. To do this, we propose a semantic trajectory model enriched by ontologies at the level of contextual data and allowing us to calculate the similarity between each episode of individual mobility. Subsequently, an editing distance is used to evaluate in… More >
E. Jenifer Sweetlin*, S. Saudia
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 343-367, 2023, DOI:10.32604/iasc.2023.036742
Abstract This paper proposes a hybrid feature selection sequence complemented with filter and wrapper concepts to improve the accuracy of Machine Learning (ML) based supervised classifiers for classifying the survivability of breast cancer patients into classes, living and deceased using METABRIC and Surveillance, Epidemiology and End Results (SEER) datasets. The ML-based classifiers used in the analysis are: Multiple Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The workflow of the proposed ML algorithm sequence comprises the following stages: data cleaning, data balancing, feature selection via a filter and wrapper sequence, cross validation-based training, testing and… More >
Neelam Mughees1,2, Mujtaba Hussain Jaffery1, Abdullah Mughees3, Anam Mughees4, Krzysztof Ejsmont5,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564
Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked, sequence-to-sequence autoencoder (S2SAE) forecasting model… More >