Yizhi Liu^1,3, Rutian Qing^1,3, Yijiang Zhao^1,3,*, Xuesong Wang^1,3, Zhuhua Liao^1,3, Qinghua Li^1,2, Buqing Cao^1,3

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2505-2519, 2023, DOI:10.32604/csse.2023.021615

Abstract Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads. But how to alleviate sparsity and further enhance accuracy is still challenging. Addressing at these issues, we propose to fuse spatio-temporal contexts into deep factorization machine (STC_DeepFM) offline for pick-up area recommendation, and within the area to recommend pick-up points online using factorization machine (FM). Firstly, we divide the urban area into several grids with equal size. Spatio-temporal contexts are destilled from pick-up points or points-of-interest (POIs) belonged to the preceding grids. Secondly, the contexts are integrated into deep factorization machine (DeepFM) to mine… More >

Yingnan Zhao^1,*, Peiyuan Ji¹, Fei Chen¹, Guanlan Ji¹, Sunil Kumar Jha²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710

Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >

Yingnan Zhao^1,*, Guanlan Ji¹, Fei Chen¹, Peiyuan Ji¹, Yi Cao²

Computer Systems Science and Engineering, Vol.43, No.2, pp. 719-735, 2022, DOI:10.32604/csse.2022.027288

Abstract Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers. This paper proposes a new variational mode decomposition (VMD)-attention-based spatio-temporal network (VASTN) method that takes advantage of both temporal and spatial correlations of wind speed. First, VASTN is a hybrid wind speed prediction model that combines VMD, squeeze-and-excitation network (SENet), and attention mechanism (AM)-based bidirectional long short-term memory (BiLSTM). VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions (IMF). Then, to extract the spatial features at the bottom of the model, each IMF employs an improved convolutional… More >

Rami Sameer Ahmad Al Kloub^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5151-5165, 2022, DOI:10.32604/cmc.2022.024309

Abstract Flood disasters can be reliably monitored using remote sensing photos with great spatiotemporal resolution. However, satellite revisit periods and extreme weather limit the use of high spatial resolution images. As a result, this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring. Using the spatial and temporal adaptive reflectance fusion model (STARFM), the spatial and temporal reflectance unmixing model (STRUM), and three prominent algorithms of flexible spatiotemporal data fusion (FSDAF), Landsat fusion images are created by fusing MODIS and Landsat images. Then, to extract flood information, utilize a support vector… More >

Zhe Yu¹, Bangyong Sun^1,3,*, Di Liu², Vincent Whannou de Dravo¹, Margarita Khokhlova⁴, Siyuan Wu³

Journal of Renewable Materials, Vol.10, No.5, pp. 1381-1395, 2022, DOI:10.32604/jrm.2022.018262

Abstract In this paper, we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS (Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples) dehazing, it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles. The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples. Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples. The final dehazed image is… 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 >

Muneeb Ur Rehman¹, Fawad Ahmed¹, Muhammad Attique Khan², Usman Tariq³, Faisal Abdulaziz Alfouzan⁴, Nouf M. Alzahrani⁵, Jawad Ahmad^6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4675-4690, 2022, DOI:10.32604/cmc.2022.019586

Abstract Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to… More >

Imran Ashraf, Sadia Din, Soojung Hur, Yongwan Park^*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5213-5232, 2022, DOI:10.32604/cmc.2022.018707

Abstract Precise information on indoor positioning provides a foundation for position-related customer services. Despite the emergence of several indoor positioning technologies such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, Wi-Fi is one of the most widely used technologies. Predominantly, Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades. Wi-Fi positioning faces three core problems: device heterogeneity, robustness to signal changes caused by human mobility, and device attitude, i.e., varying orientations. The existing methods do not cover these aspects owing to the unavailability of publicly available datasets. This… More >

Nauman Ahmed^1,2, Umbreen Fatima¹, Shahzaib Iqbal¹, Ali Raza³, Muhammad Rafiq^4,*, Muhammad Aziz-ur-Rehman², Shehla Saeed¹, Ilyas Khan⁵, Kottakkaran Sooppy Nisar⁶

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 201-212, 2021, DOI:10.32604/cmc.2021.014171

Abstract The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered (SLBR) epidemic model. It describes the computer virus dynamics with vertical transmission via the internet. In these types of dynamics models, the absolute values of the state variables are the fundamental requirement that must be fulfilled by the numerical design. By taking into account this key property, the positivity preserving algorithm is designed to solve the underlying SLBR system. Since, the state variables associated with the phenomenon, represent the computer nodes, so they must take in absolute. Moreover, the continuous system (SLBR) acquires two steady states… More >

Naveed Shahid^1,2, Dumitru Baleanu^3,4,5, Nauman Ahmed^1,2, Tahira Sumbal Shaikh⁶, Ali Raza^7,*, Muhammad Sajid Iqbal¹, Muhammad Rafiq⁸, Muhammad Aziz-ur Rehman²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1713-1728, 2021, DOI:10.32604/cmc.2021.014191

Abstract The novel coronavirus disease, coined as COVID-19, is a murderous and infectious disease initiated from Wuhan, China. This killer disease has taken a large number of lives around the world and its dynamics could not be controlled so far. In this article, the spatio-temporal compartmental epidemic model of the novel disease with advection and diffusion process is projected and analyzed. To counteract these types of diseases or restrict their spread, mankind depends upon mathematical modeling and medicine to reduce, alleviate, and anticipate the behavior of disease dynamics. The existence and uniqueness of the solution for the proposed system are investigated.… More >