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Authors:Gateri; Judy, Rimiru, Richard M., Kimwele, Michael Pages: 1 - 11 Abstract: Convolutional neural networks (CNNs) are deep learning methods that are utilized in image processing such as image classification and recognition. It has achieved excellent results in various sectors; however, it still lacks rotation invariant and spatial information. To establish whether two images are rotational versions of one other, one can rotate them exhaustively to see if they compare favorably at some angle. Due to the failure of current algorithms to rotate images and provide spatial information, the study proposes to transform color spaces and use the Gabor filter to address the issue. To gather spatial information, the HSV and CieLab color spaces are used, and Gabor is used to orient images at various orientation. The experiments show that HSV and CieLab color spaces and Gabor convolutional neural network (GCNN) improves image retrieval with an accuracy of 98.72% and 98.67% on the CIFAR-10 dataset. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 14, Issue: 1 (2023) Pages: 1-11 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJACI.323798 Issue No:Vol. 14, No. 1 (2023)
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Authors:Gaol; Ford Lumban, Alam, Pramasiwo, Franklyn, Muhammad Bio, Angke, Kevins, Matsuo, Tokuro Pages: 1 - 13 Abstract: Artificial intelligence traffic controllers are being designed with the primary goal of enabling them to adapt to the most recent sensor data in order to perform ongoing optimizations on the signal timing plan for intersections in a network in order to reduce traffic congestions, the most pressing issue in traffic flow control at present. The authors are employing an intelligent traffic redirection technology to reduce traffic and road congestion. This would operate utilizing sensors to determine weight, with the result communicated to a traffic light PLC to control the detour. The result of experiments reduced the number of automobiles in a given time interval by 51%. There is improvement as well as on the average speed of automobiles that increase within the system by 49%. The authors also found a reduction of the average time a vehicle must wait in a system by 58%. Moreover, the implementation shows that the average wait times at junctions have been reduced by 34%. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 14, Issue: 1 (2023) Pages: 1-13 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/IJACI.323196 Issue No:Vol. 14, No. 1 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Tang; Yufeng Pages: 1 - 15 Abstract: Aiming at the low accuracy of network intrusion detection (In-De) in the traditional network communication strategy of new energy vehicles (NEVs), this paper proposes an electronic control (E-C) strategy for network communication of NEVs based on cloud platform in the internet of things (IoT) environment. First, based on the cloud platform and deep learning (D-L) algorithm, the E-C system model including sensor, actuator, gateway, and cloud platform is constructed, and on this basis, the edge computing model is introduced to efficiently handle information interaction and computing tasks. Then, by using Bi-LSTM neural network to train historical data in the cloud center layer of the system, a D-L method combining cloud and edge nodes is proposed. Finally, by introducing the AlexNet network into the model, the problem of gradient vanishing when the network is deep is solved and the training speed is accelerated. Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 14, Issue: 1 (2023) Pages: 1-15 PubDate: 2023-01-01T05:00:00Z DOI: 10.4018/ijaci.318135 Issue No:Vol. 14, No. 1 (2023)
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