Xinyao Liu¹, Baojiang Cui^1,*, Lantao Xing²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 295-305, 2022, DOI:10.32604/iasc.2022.020032

Abstract With the rise of mobile network, user location information plays an increasingly important role in various mobile services. The analysis of mobile users’ trajectories can help develop many novel services or applications, such as targeted advertising recommendations, location-based social networks, and intelligent navigation. However, privacy issues limit the sharing of such data. The release of location data resulted in disclosing users’ privacy, such as home addresses, medical records, and other living habits. That promotes the development of trajectory generators, which create synthetic trajectory data by simulating moving objects. At current, there are some disadvantages in the process of generation. The… More >

Muhammad Zulqarnain¹, Rozaida Ghazali^1,*, Habib Shah², Lokman Hakim Ismail¹, Abdullah Alsheddy³, Maqsood Mahmud⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3051-3068, 2022, DOI:10.32604/cmc.2022.021629

Abstract Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM_2.5) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM_2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced… More >

Wei Fang^1,2,*, Yupeng Chen¹, Qiongying Xue¹

Journal on Big Data, Vol.3, No.3, pp. 97-110, 2021, DOI:10.32604/jbd.2021.016993

Abstract In the past few years, deep learning has developed rapidly, and many researchers try to combine their subjects with deep learning. The algorithm based on Recurrent Neural Network (RNN) has been successfully applied in the fields of weather forecasting, stock forecasting, action recognition, etc. because of its excellent performance in processing Spatio-temporal sequence data. Among them, algorithms based on LSTM and GRU have developed most rapidly because of their good design. This paper reviews the RNN-based Spatiotemporal sequence prediction algorithm, introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction, and includes precipitation nowcasting… More >

R. Krishnaswamy^1,*, B. Sivakumar², B. Viswanathan³, Fahd N. Al-Wesabi^4,5, Marwa Obayya⁶, Anwer Mustafa Hilal⁷

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 255-268, 2022, DOI:10.32604/cmc.2022.021995

Abstract Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis. The proposed IBECG-SP technique examines… More >

Pijush Kanti Dutta Pramanik¹, Nilanjan Sinhababu², Anand Nayyar^3,4,*, Mehedi Masud⁵, Prasenjit Choudhury¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5199-5212, 2022, DOI:10.32604/cmc.2022.019630

Abstract In mobile crowd computing (MCC), people’s smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today’s SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users’ mobility poses a serious issue in the QoS of MCC. To address this, before submitting a… More >

Tongming Qu¹, Shaocheng Di², Y. T. Feng^1,3,*, Min Wang⁴, Tingting Zhao³, Mengqi Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 129-144, 2021, DOI:10.32604/cmes.2021.016172

Abstract This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling (DEM) and deep learning. A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models. The low-dimensional principal stress-strain sequence pairs, measured from discrete element modelling of triaxial testing, are used to train recurrent neural networks, and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation. Through detailed hyperparameter investigations, it is found that long short-term memory (LSTM) and gated recurrent… More >

Norah Alsaaran^*, Maha Alrabiah

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 471-485, 2021, DOI:10.32604/cmc.2021.016054

Abstract Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models… More >

Qi Yue, Xiang Li, Dan Li^*

Computer Systems Science and Engineering, Vol.37, No.3, pp. 423-442, 2021, DOI:10.32604/csse.2021.014448

Abstract Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and understand forestry knowledge. Relation Extraction (RE) is a crucial task of KG construction and information retrieval. Previous researches on relation extraction have proved the performance of using the attention mechanism. However, these methods focused on the representation of the entire sentence and ignored the loss of information. The lack of analysis of words and syntactic features contributes to sentences, especially in Chinese relation extraction, resulting in poor performance.… More >

Yonghua Huo¹, Yi Cao², Zhihao Wang¹, Yu Yan³, Zhongdi Ge³, Yang Yang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1053-1091, 2021, DOI:10.32604/cmes.2021.013045

Abstract In recent years, with the continuous development of information technology and the rapid growth of network scale, network monitoring and management become more and more important. Network traffic is an important part of network state. In order to ensure the normal operation of the network, improve the availability of the network, find network faults in time and deal with network attacks; it is necessary to detect the abnormal traffic in the network. Abnormal traffic detection is of great significance in the actual network management. Therefore, in order to improve the accuracy and efficiency of network traffic anomaly detection, this paper… More >

Aymen Rhouma^1,∗, Sami Hafsi^2,†, Faouzi Bouani³

Computer Systems Science and Engineering, Vol.34, No.5, pp. 305-313, 2019, DOI:10.32604/csse.2019.34.305

Abstract This paper provides an application of Fractional Model Predictive Control (FMPC) and fractional-order Proportional Integral controller (P I^λ) on a thermal system with time delay.The first controller is based on Grünwald-Letnikov’s method to predict the future dynamic behavior of the system. This method consists in replacing the non-integer derivation operator of the adopted system representation by a discrete approximation. Therefore, this controller is developed on the basis of a fractional order model. However, the second controller is founded on an extended version of Hermite-Biehler theorem to determine the complete set stabilizing P I^λ parameters Experiment results onto a time delay… More >