Autonomous Behavioral Decision for Vehicular Agents Based on Cyber-Physical Social Intelligence

Guo, Zhiwei; Meng, Dian; Chakraborty, Chinmay; Fan, Xing-Rong; Bhardwaj, Arpit; Yu, Keping

doi:10.1109/tcss.2022.3212864

Cited by 62 publications

(12 citation statements)

References 31 publications

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“…Brain signal contain number of feature in it, which can be distinguished out by making use of feature extraction technique [29] like, Fast Fourier Transform (FFT), Time-Frequency Distribution (TFD), Principle Component Analysis (PCA), Eigen Vector Method (EVM), Independent. Component Analysis (ICA), Auto Regression Method (ARM), Wavelet Transform (WT) and Linear Discriminant Analysis (LDA).…”

Section: Feature Extractionmentioning

confidence: 99%

Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Iqbal,

Bhardwaj,

Suhag

et al. 2024

Preprint

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Electroencephalogram (EEG) signals are produced by neurons of human brain and contain frequencies and electrical properties. It is easy for a Brain to Computer Interface (BCI) system to record EEG signals by using non-invasive methods. Speech imagery (SI) can be used to convert speech imaging into text, researches done so far on SI has made use of multichannel devices. In this work, we propose EEG signal dataset for imagined a/e/i/o/u vowels collected from 5 participants using NeuroSky Mindwave Mobile2 single channel device. Decision Tree (DT), Random Forest (RF), Genetic Algorithm (GA) Machine Learning (ML) classifiers are trained with proposed dataset. For the proposed dataset, the average classification accuracy of DT is found lower in comparison to RF and GA. GA shows better performance for vowel e/o/u resulting accuracy of 80.8%, 82.36%, 81.8% for 70 − 30 data partition, 80.2%, 81.9%, 80.6% for 60 − 40 partition data and 79.8%, 81.12%, 78.36% for 50–50 data partition. Whereas RF shows improved classification accuracy for a/i which is 83.44%, 81.6% for 70 − 30 data partition, 82.2%, 81.2% for 60 − 40 data partition and 81.4%, 80.2% for 50–50 data partition. Some other performance parameters like min. value, max. value of accuracy, standard deviation, sensitivity, specificity, precision, F1 score, false positive rate and receiver operating characteristics are also evaluated and anal- ysed. Research has proven that brain functions remains normal in patients with vocal disorders. Completely disabled patients can be equipped with such technol- ogy as this may be one of the best way for them to have access over the essential day to day basic requirement.

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Section: Feature Extractionmentioning

confidence: 99%

Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Iqbal,

Bhardwaj,

Suhag

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In practical applications, deep neural networks often incorporate a variety of known structures [12]. It can be understood that a deep neural network can be understood as a network containing multiple hidden layer structures [13].…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Educational Evaluation System Using Deep Learning

Yan

Lu²

2023

IEEE Access

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Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario.

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“…With the diversified development of the financial market and the increasing financing needs of small, medium, and micro enterprises, many Internet financial enterprises have emerged [7]. These enterprises mainly include consumer finance companies, online micro-loan companies, and P2P platforms [8], [9].…”

Section: Introductionmentioning

confidence: 99%

A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

Zhao¹,

Wang

2023

IEEE Access

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In the big data era, traditional auditing methods are facing challenge such as limited audit scope, uneven distribution of audit power, and insufficient audit analysis. To pursue high efficiency, the utilization of big data analysis technique in financial auditing has been a novel tendency in this area. The deep learning has been popular in many areas due to its high freedom degree. Thus, this paper employs a typical deep learning model convolution neural network (CNN), and proposes a big data-driven financial auditing method using CNN. Specifically, the strong ability of feature abstraction of CNN is leveraged to extract multi-level features in materials, such as visual features, textual features, etc. Then, the multi-source features from auditing materials can be well fused for final discrimination. Some simulation experiments are conducted on real-world financial auditing scenes for assessment. And the results show that the designed the proposed financial auditing method possesses relatively high auditing accuracy.

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Autonomous Behavioral Decision for Vehicular Agents Based on Cyber-Physical Social Intelligence

Cited by 62 publications

References 31 publications

Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Design of an Intelligent Educational Evaluation System Using Deep Learning

A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

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