Enhancing the wine tasting experience using greedy clustering wine recommender system

Katarya, Rahul; Saini, Rajat

doi:10.1007/s11042-021-11300-5

Cited by 13 publications

(3 citation statements)

References 78 publications

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“…Finally, it has frequently been observed ( Table 2 ) that the optimal number of groups evaluated by the Calinski–Harabasz index is higher than for the two other criteria, especially for hard clustering algorithms. This could mainly be attributed to the major drawback of this criterion, which is generally higher for convex globular clusters, namely k -means [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

Intersubject Variability in Cerebrovascular Hemodynamics and Systemic Physiology during a Verbal Fluency Task under Colored Light Exposure: Clustering of Subjects by Unsupervised Machine Learning

et al. 2022

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There is large intersubject variability in cerebrovascular hemodynamic and systemic physiological responses induced by a verbal fluency task (VFT) under colored light exposure (CLE). We hypothesized that machine learning would enable us to classify the response patterns and provide new insights into the common response patterns between subjects. In total, 32 healthy subjects (15 men and 17 women, age: 25.5 ± 4.3 years) were exposed to two different light colors (red vs. blue) in a randomized cross-over study design for 9 min while performing a VFT. We used the systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) approach to measure cerebrovascular hemodynamics and oxygenation at the prefrontal cortex (PFC) and visual cortex (VC) concurrently with systemic physiological parameters. We found that subjects were suitably classified by unsupervised machine learning into different groups according to the changes in the following parameters: end-tidal carbon dioxide, arterial oxygen saturation, skin conductance, oxygenated hemoglobin in the VC, and deoxygenated hemoglobin in the PFC. With hard clustering methods, three and five different groups of subjects were found for the blue and red light exposure, respectively. Our results highlight the fact that humans show specific reactivity types to the CLE-VFT experimental paradigm.

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Section: Discussionmentioning

confidence: 99%

Intersubject Variability in Cerebrovascular Hemodynamics and Systemic Physiology during a Verbal Fluency Task under Colored Light Exposure: Clustering of Subjects by Unsupervised Machine Learning

et al. 2022

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“…As a result, we suggest that, in addition to the usual face recognition task, we extract additional information from the face-masked photos of individuals and use them to predict their identities. The fact that these applications, particularly in the law enforcement domain, require high-recall results leads us to frame this problem as a recommendation task (Katarya and Saini 2022 ; Gupta and Katarya 2021c , d ; Katarya and Arora 2020 ; Katarya et al. 2013 ), where potential identities are ranked and returned, rather than returning only one candidate as followed by the traditional face recognition protocol.…”

Section: Background and Related Workmentioning

confidence: 99%

A deep feature-level fusion model for masked face identity recommendation system

Thaipisutikul

Tatiyamaneekul

Lin

et al. 2022

J Ambient Intell Human Comput

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The widespread occurrences of airborne outbreaks (e.g., COVID-19) and pollution (e.g., PM2.5) have urged people in the affected regions to protect themselves by wearing face masks. In certain areas, wearing masks amidst such health-endangering times is even enforced by law. While most people wear masks to guard themselves against airborne substances, some exploit such excuses and use face masks to conceal their identity for criminal purposes such as shoplifting, robbery, drug transport, and assault. While automatic face recognition models have been proposed, most of these models aim to identify clear, unobstructed faces for authentication purposes and cannot effectively handle cases where masks cover most facial areas. To mitigate such a problem, this paper proposes a deep-learning-based feature-fusion framework, FIREC , that combines additional demographic-estimated features such as age, gender, and race into the underlying facial representation to compensate for the information lost due to mask obstruction. Given an image of a masked face, our system recommends a ranked list of potential identities of the person behind the mask. Empirical results show that the best configuration of our proposed framework can recognize bare faces and masked faces with the accuracy of 99.34% and 97.65% in terms of Hit@10, respectively. The proposed framework could greatly benefit high-recall facial identity recognition applications such as identifying potential suspects from CCTV or passers-by’s cameras, especially during crisis times when people commonly cover their faces with protective masks.

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“…To determine individuals taste expectations and recommending a desired wine to such individuals was considered a challenging task. To fulfill this, an adaptive recommendation system was developed in Reference 27, which integrates PCA (Principal component analysis), k‐means based clustering and greedy based ranking approach. Moreover, it also introduces elbow method, which helps to determine the ideal cluster number.…”

Section: Related Workmentioning

confidence: 99%

An efficient framework for the similarity prediction with query recommendation in E‐learning system

Prasad

Muthappa²

2022

Concurrency and Computation

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Summary A novel model of data similarity estimation and clustering method is proposed in this article to retrieve the relevant data with the best matching in big data processing. An advanced model of graph distance pattern (GDP) method with lexical subgroup (LS) system is used to estimate the similarity between the query data and the entire database. With the help of neural network, the relevancy of feature attributes in the database are predicted and matching index is sorted to provide the recommended data for given query data. This was achieved by using the correlated sim‐neural network (CSNN). This is an enhanced model of neural network technology to find the relevancy based on the correlation factor of feature set. The training process of CSNN classifier is carried by estimating the correlation factor of the attributes of dataset. These are forms as the clusters and paged with proper indexing based on the LS parameter of similarity metric. The results obtained by the proposed system for recall, precision, accuracy, error rate, F‐measure, kappa coefficient, specificity, and MCC are 0.98, 0.98, 0.97, 0.03, 0.99, 0.991, 0.986, and 0.984, respectively.

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Enhancing the wine tasting experience using greedy clustering wine recommender system

Cited by 13 publications

References 78 publications

Intersubject Variability in Cerebrovascular Hemodynamics and Systemic Physiology during a Verbal Fluency Task under Colored Light Exposure: Clustering of Subjects by Unsupervised Machine Learning

Intersubject Variability in Cerebrovascular Hemodynamics and Systemic Physiology during a Verbal Fluency Task under Colored Light Exposure: Clustering of Subjects by Unsupervised Machine Learning

A deep feature-level fusion model for masked face identity recommendation system

An efficient framework for the similarity prediction with query recommendation in E‐learning system

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