Dynamic Texture Classification Based on 3D ICA-Learned Filters and Fisher Vector Encoding in Big Data Environment

Xiong, Zenggang; Mo, Fangrong; Zhao, Xiangmo; Xu, Fang; Zhang, Xuemin; Wu, Yiyun

doi:10.1007/s11265-021-01737-0

Cited by 23 publications

(20 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, other machine learning techniques can be incorporated into the proposed approaches in the prediction of accident severity [69][70][71]. Deep learning models can also obtain more accurate results [72][73][74]. By understanding users' perceptions regarding the improvement of facilities, these methods, in conjunction with a survey analysis, can also improve work zone safety [75].…”

Section: Discussionmentioning

confidence: 99%

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Habibzadeh

Ameri

Haghighi

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Traffic accidents occur due to a combination of factors that lead to casualties and injuries. By identifying the most effective factors, it is possible for safety authorities to provide appropriate solutions for decreasing the accident severity and implementing preventive measures. The aim of this research was to present models to predict the accident severity on two-lane two-way (TLTW) rural highways in Iran over a one-year period from 2019 to 2020. Therefore, the occurrence probability of any type of accident was determined by artificial neural network (ANN)-based prediction models using nine independent variables affecting the accident severity. This study developed numerous ANN structures using back-propagation to model the potential nonlinear relationship between the accident severity and accident-related factors. Results indicated that among the models, the multilayer perceptron neural network (MLPNN) model with 6-2-2 partition had the best performance and prediction power. This model was developed using the standardized rescaling method for covariates and batch for training. Also, 9, 5, and 2 units were considered automatically for the input, hidden, and output layers, respectively, and the hyperbolic tangent and softmax were used as an activation function in the hidden and output layers, respectively. This model had the lowest cross-entropy error of 39.6 and the highest correct percentage of 82.5%, and the area under the receiver operating characteristic (ROC) curve was 0.852. Moreover, among all the effective variables, pavement condition index, roadside hazard, shoulder width, and passing zone ratio had the greatest impact on the accident severity. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Habibzadeh

Ameri

Haghighi

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The authors analyzed the degree of contribution of items to variables through the cross-loading between variables. The degree of loading reflects whether the variable is well explained (Yong & Pearce, 2013), while the cross-loading reflects the degree of loading of an item on other variables (Hair et al, 2011;Wu et al, 2018;Xiong et al, 2022). From Table 5, it can be seen that the relevant indicators have relatively high load degree values on the variables, which can indicate that the discriminant validity of the questionnaire is effective.…”

Section: Reliability and Validity Testmentioning

confidence: 98%

Platformance-Based Cross-Border Import Retail E-Commerce Service Quality Evaluation Using an Artificial Neural Network Analysis

Feng

Chen

2022

Journal of Global Information Management

View full text Add to dashboard Cite

The transaction scale of cross-border import e-commerce has grown rapidly around the world. Platform-style cross-border e-commerce does not control the quality, source and transaction process of goods strictly and comprehensively. In terms of customer service quality, the seller's customer service often ignores the customer's problems, and some customer service solutions cannot solve the customer's problems. Serving customers through the network has changed the traditional offline service form without distance, and the service process has a time and space distance. This paper constructs an evaluation index system based on the development of cross-border e-commerce. Through questionnaires, relevant data were obtained and analyzed. Analyze the results based on the collected data on the factors that affect the quality of cross-border import e-commerce services. Responsiveness is the most important factor found by artificial neural networks. The descending order of importance of other factors is fulfillment, diversity, privacy, reliability, compensation, and ease of use.

show abstract

“…Using ML for COVID-19 research is currently fraught with challenges (Wu et al 4 The IoT main applications for the COVID-19 pandemic 2020; Zheng et al 2022). The absence of standard data is one of the most significant obstacles to utilizing DL to diagnose COVID-19 (Chakraborty and Abougreen 2021;Xiong et al 2022). Another problematic issue is the dataset's sample imbalance.…”

Section: Challengesmentioning

confidence: 99%

Integration of cloud computing with the Internet of things for the treatment and management of the COVID-19 pandemic

Darbandi

Alrasheedi

Alnowibet

et al. 2022

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

The globe is now fighting the COVID-19 epidemic, and a cost-effective method to battle the outbreak is urgently required. In this era, the Internet of Things (IoT) offers healthcare systems or checks and discovers patients in epidemics to prevent the virus from spreading. In order to tackle the current COVID-19 pandemic, intelligent health care based on IoT is becoming increasingly vital in better digital technologies. This technology allows gadgets to link hospitals and other predefined sites to tackle this dilemma. The cognitive Internet of Medical Things (IoMT) is a potential technology for quick analysis, dynamic monitoring and control, improved therapy and management, and prevention of viral transmission. This technology can send a message to the hospital immediately in an emergency. Due to the importance of this subject, the influence of IoT-centered innovations in the management of COVID-19 is examined in this article. The three main areas, including early detection, viral prevention, and patient care, are considered in this paper. The results showed that using IoT, such as drones and robots, has been fruitful in reducing contact with patients and identifying disease symptoms. Also, they showed that the IoT wearable gadgets and the cloud platform to store the patient's information have helped doctors track the patient's condition. Keywords COVID-19 management • Internet of things • Coronavirus • Cloud computing • Internet of medical things * Danial Javaheri

show abstract

Dynamic Texture Classification Based on 3D ICA-Learned Filters and Fisher Vector Encoding in Big Data Environment

Cited by 23 publications

References 49 publications

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Platformance-Based Cross-Border Import Retail E-Commerce Service Quality Evaluation Using an Artificial Neural Network Analysis

Integration of cloud computing with the Internet of things for the treatment and management of the COVID-19 pandemic

Contact Info

Product

Resources

About