Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

Sulikowski, Piotr; Zdziebko, Tomasz

doi:10.3390/electronics9020266

Cited by 43 publications

(62 citation statements)

References 47 publications

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“…) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were given in these articles, and the results indicated that the performance of the improved deep learning methods could be higher than the performance of conventional machine learning methods [43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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This editorial introduces the Special Issue, entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries”, of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) “A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence” by Panapakidis et al.; (2) “Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting” by Wan et al.; (3) “Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors” by Xu et al.; (4) “An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform” by Zhang et al. Three papers on UAV and object tracking applications are as follows: (1) “Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints” by Zhou et al.; (2) “OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance” by Zhang et al.; (3) “Model Update Strategies about Object Tracking: A State of the Art Review” by Wang et al. Five papers on measurement and denoising techniques are as follows: (1) “Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation” by Wang et al.; (2) “Characterization and Correction of the Geometric Errors Using a Confocal Microscope for Extended Topography Measurement, Part II: Experimental Study and Uncertainty Evaluation” by Wang et al.; (3) “Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction” by Lin et al.; (4) “Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets” by Chang et al.; (5) “High-Resolution Image Inpainting Based on Multi-Scale Neural Network” by Sun et al. Two papers on recommendation systems and education systems are as follows: (1) “Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing” by Sulikowski et al. and (2) “Generative Adversarial Network Based Neural Audio Caption Model for Oral Evaluation” by Zhang et al.

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Section: Discussionmentioning

confidence: 99%

“…In the experiments, 15,922 fixation records were generated by eye-tracking devices from 52 participants. The results show that the accuracies of training dataset and testing dataset were 98.4% and 98.2%, respectively [55].…”

Section: Recommendation Systems and Education Systemsmentioning

confidence: 98%

Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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“…They have used K-nearest Neighbour (KNN) as a classification method for this purpose and tested the result on two real datasets of women's clothing and furniture. A recent study [55] provides a user interface for an e-commerce website based on users' behavior using a deep neural network method. Their analysis revealed the effect of a website layout to recommended items based on the user's behavior.…”

Section: Recommendation Systems In E-commercementioning

confidence: 99%

“…Application Reference e-commerce Items recommendations to buyers [4,5] Movie or video recommendations [43] Transportation Path Recommendation for transporting goodOr passengers [8,39,49] Recommendations to Tourists [50][51][52] Venue recommendation [53][54][55] e-health Medical advice or treatment plan recommendation [6,46,63,64] Recommending Personalized services to patients [44] Appointments recommendation to clinicians [45] Health recommendations in mobile systems [59] Healthy behavioral recommendations [61] Diet recommendation [62] Agriculture Fertilizer recommendation to farmers [7] Crops issue recommendation [47] Assisting farmers inquiries [48] Agricultural products recommendation [65] Crop cultivation suggestion [40,[66][67][68] Media Event recommendations [80] Museum recommendations [81,82] Multimedia recommendations [83][84][85] Open Social Networks recommendations [86][87][88][89][90]…”

Section: Areamentioning

confidence: 99%

Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

et al. 2020

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Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

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“…In turn, they can solve recognition, regression, semi-supervised and unsupervised problems [42][43][44]. Deep learning has proven its efficacy in medical imaging like in many other domains such as self-driving cars, natural language and image processing, predictive forecasting, eye tracking systems, object detection in space, finger print localization systems [45][46][47][48][49]. Vgg16 is one of the deep learning models [50] that is a successful feature extractor in multiple domains having lots of image data.…”

Section: Parallel Multi-parametric Feature Embeddingmentioning

confidence: 99%

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-Parametric Feature Embedded Siamese Network

2020

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Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

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Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

Cited by 43 publications

References 47 publications

Deep Learning Applications with Practical Measured Results in Electronics Industries

Deep Learning Applications with Practical Measured Results in Electronics Industries

Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-Parametric Feature Embedded Siamese Network

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