PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones

Shen, Xiangyu; Jiang, Hongbo; Liu, Daibo; Yang, Kehua; Deng, Feiyang; Lui, John C. S.; Liu, Jiangchuan; Dustdar, Schahram; Luo, Jun

doi:10.1109/jiot.2022.3181607

Cited by 50 publications

(11 citation statements)

References 38 publications

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“…The incorporation of state-of-the-art image processing methods holds the promise of significantly enhancing the accuracy of results in facial expression analysis. The continual evolution of image processing techniques enables researchers to employ advanced methodologies that can capture nuanced details and improve the overall precision of emotion recognition systems [34][35][36][37][38][39].…”

Section: Discussionmentioning

confidence: 99%

Exploring cross-cultural and gender differences in facial expressions: a skin tone analysis using RGB Values

Ali,

Khan,

Khan

et al. 2023

J Cloud Comp

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Facial expressions serve as crucial indicators of an individual's psychological state, playing a pivotal role in face-to-face communication. This research focuses on advancing collaboration between machines and humans by undertaking a thorough investigation into facial expressions. Specifically, we delve into the analysis of emotional variations related to changes in skin tone across different genders and cultural backgrounds (Black and white). The research methodology is structured across three phases. In Phase I, image data is acquired and meticulously processed from the Chicago face dataset, resulting in 12,402 augmented images across five classes (Normal case, Benign case, Adenocarcinoma, Squamous-cell-carcinoma, Large-cell-carcinoma). Phase II involves the identification of Regions of Interest (ROI) and the extraction of RGB values as features from these ROIs. Various methods, including those proposed by Kovac, Swift, and Saleh, are employed for precise skin identification. The final phase, Phase III, centers on the in-depth analysis of emotions and presents the research findings. Statistical techniques, such as Descriptive statistics, independent sample T-tests for gender and cross-cultural comparisons, and two-way ANOVA, are applied to RED, BLUE, and GREEN pixel values as response variables, with gender and emotions as explanatory variables. The rejection of null hypotheses prompts a Post Hoc test to discern significant pairs of means. The results indicate that both cross-cultural backgrounds and gender significantly influence pixel colors, underscoring the impact of different localities on pixel coloration. Across various expressions, our results exhibit a minimal 0.05% error rate in all classifications. Notably, the study reveals that green pixel color does not exhibit a significant difference between Anger and Neutral emotions, suggesting a near-identical appearance for green pixels in these emotional states. These findings contribute to a nuanced understanding of the intricate relationship between facial expressions, gender, and cultural backgrounds, providing valuable insights for future research in human–machine interaction and emotion recognition.

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Section: Discussionmentioning

confidence: 99%

Exploring cross-cultural and gender differences in facial expressions: a skin tone analysis using RGB Values

Ali,

Khan,

Khan

et al. 2023

J Cloud Comp

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“…According to the report, understanding supply chain financing, bonded warehouses, and foreign warehouses is essential for developing new business models. An intelligent agent framework was created by Shen et al 16 to choose providers in a multi-product context that offer combinatorial advantages.…”

Section: Related Researchmentioning

confidence: 99%

Predicting sales and cross-border e-commerce supply chain management using artificial neural networks and the Capuchin search algorithm

Xie,

Liu,

Wang

2024

Sci Rep

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E-commerce provides a large selection of goods for sale and purchase, which promotes regular transactions and commodity flows. Efficient distribution of goods and precise estimation of customer wants are essential for cost reduction. In order to improve supply chain efficiency in the context of cross-border e-commerce, this article combines machine learning approaches with the Internet of Things. The suggested approach consists of two main stages. Order prediction is done in the first step to determine how many orders each merchant is expected to get in the future. In the second phase, allocation operations are conducted and resources required for each retailer are supplied depending on their needs and inventory, taking into account each store’s inventory as well as the anticipated sales level. This suggested approach makes use of a weighted mixture of neural networks to anticipate sales orders. The Capuchin Search Algorithm (CapSA) is used in this weighted combination to concurrently enhance the learning and ensemble performance of models. This indicates that an effort is made to reduce the local error of the learning model at the model level via model weight adjustments and neural network configuration. To guarantee more accurate output from the ensemble model, the best weight for each individual component is found at the ensemble model level using the CapSA method. This method yields the ensemble model’s final output in the form of weighted averages by choosing suitable weight values. With a Root Mean Squared Error of 2.27, the suggested technique has successfully predicted sales based on the acquired findings, showing a minimum decrease of 2.4 in comparison to the comparing methodologies. Additionally, the suggested method’s strong performance is shown by the fact that it was able to minimize the Mean Absolute Percentage Error by 14.67 when compared to other comparison approaches.

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“…Continuous authentication refers to the ongoing process of verifying the identity or authenticity of a user in real-time ( Kong et al, 2020 ). o collect pupil morphology data, the system would require access to the smartphone’s front-facing camera or specialized eye-tracking technology ( Shen et al, 2022 ). The objective of the research framework is to provide a comprehensive and practical solution for destination prediction in LBSs that considers both utility and privacy ( Jiang et al, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Angamuthu,

Trojovský

2023

PeerJ Computer Science

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Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users’ preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users’ opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using ‘TripAdvisor reviews’, ‘TMDB 5000 movies’, and an ‘Amazon dataset’. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

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PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones

Cited by 50 publications

References 38 publications

Exploring cross-cultural and gender differences in facial expressions: a skin tone analysis using RGB Values

Exploring cross-cultural and gender differences in facial expressions: a skin tone analysis using RGB Values

Predicting sales and cross-border e-commerce supply chain management using artificial neural networks and the Capuchin search algorithm

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

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