Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955 - 20 May 2024

Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

Abdulwadood Alawadhi^1,*, Mohd. Hasbullah Omar¹, Abdullah Almogahed², Noradila Nordin³, Salman A. Alqahtani⁴, Atif M. Alamri⁵

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2851-2878, 2024, DOI:10.32604/cmc.2024.050154 - 15 May 2024

Abstract The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use of Body Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, including contention during finite backoff periods, association delays, and traffic channel access through clear channel assessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions, and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet delivery ratio, packet drop rate, and packet delay.… More >

Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914 - 08 October 2023

Abstract Predicting traffic flow is a crucial component of an intelligent transportation system. Precisely monitoring and predicting traffic flow remains a challenging endeavor. However, existing methods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes, resulting in the loss of essential information and lower forecast performance. On the other hand, the availability of spatiotemporal data is limited. This research offers alternative spatiotemporal data with three specific features as input, vehicle type (5 types), holidays (3 types), and weather (10 conditions). In this study, the proposed model… More >

He Gong^1,2,3,4, Ji Li¹, RuiWen Ni¹, Pei Xiao¹, Hang Ouyang¹, Ye Mu^1,*, Thobela Louis Tyasi⁵

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1049-1062, 2021, DOI:10.32604/iasc.2021.019730 - 20 August 2021

Abstract Presently, the adoption of Internet of things(IOT)-related technologies in the Smart Farming domain is rapidly emerging. The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. A low-cost, low-power, and low data-rate solution is proposed to fulfill the requirements of information monitoring for actual large-scale agricultural farms, which we will need pressingly in the future. This paper designs a heterogeneous data acquisition and control system for differentiated agricultural information monitoring terminal. Based on the IoT-CAN More >

Toshihiro Ashino^1,*, Nobutaka Nishikawa²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 137-137, 2019, DOI:10.32604/icces.2019.05454

Abstract Materials performance analysis process requires integration of many heterogeneous data and information resources, e.g. experimental data, empirical models and computational simulation. Virtual Research Environment (VRE) for materials science and engineering should support each data handling processes, data retrieval, conversion, statistical analysis, symbolic manipulation and visualization within single interactive and scripting environment.
Furthermore, in order to integrate heterogeneous data, it requires a common dictionary which describe semantic relationships among these data resources. It is required to identify corresponding data items from different data resources. It can be a flat structured table, but an ontology which describes More >