Deep learning based velocity prediction with consideration of road structure

Fu, Pengyu; Chu, Liang; Hou, Zhuoran; Xing, Jiaming; Gao, Jianbing; Guo, Chong

doi:10.1109/cvci54083.2021.9661118

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…With the expansion in data volume and computational capacity, neural networks with more complicated topologies have received attention from academia to industry. Application-wise, deep learning has made significant strides in voice and image categorization, advancing artificial intelligence and human-computer interaction [ 36 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

Kibriya

Amin²,

Kim

et al. 2023

Sensors

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One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient’s health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method’s discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.

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Section: Proposed Methodologymentioning

confidence: 99%

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

Kibriya

Amin²,

Kim

et al. 2023

Sensors

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Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data

Alhudhaif,

Polat

2024

PeerJ Computer Science

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Currently, most traffic simulations require residents’ travel plans as input data; however, in real scenarios, it is difficult to obtain real residents’ travel behavior data for various reasons, such as a large amount of data and the protection of residents’ privacy. This study proposes a method combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) for analyzing and compensating spatiotemporal features in residents’ travel data. By exploiting the spatial feature extraction capability of CNNs and the advantages of LSTMs in processing time-series data, the aim is to achieve a traffic simulation close to a real scenario using limited data by modeling travel time and space. The experimental results show that the method proposed in this article is closer to the real data in terms of the average traveling distance compared with the use of the modulation method and the statistical estimation method. The new strategy we propose can significantly reduce the deviation of the model from the original data, thereby significantly reducing the basic error rate by about 50%.

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Deep learning based velocity prediction with consideration of road structure

Cited by 2 publications

References 11 publications

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data

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