LSH-aware multitype health data prediction with privacy preservation in edge environment

Kong, Lingzhen; Wang, Lina; Gong, Wei; Yan, Chao; Duan, Yucong; Qi, Lianyong

doi:10.1007/s11280-021-00941-z

Cited by 79 publications

(22 citation statements)

References 44 publications

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“…In addition, Mahalanobis Distance requires additional time cost to compute the covariance matrix of different dimensions; therefore, its time complexity is not very low. While time cost is critical for real world applications especially for the big data scenario [29][30][31][32][33][34][35] . Therefore, we would continuously refine our algorithm to further reduce its time costs so as to meet the quick response requirements from users.…”

Section: Discussionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a correlation-aware player training score evaluation method with trust (abbreviated as CPEMD) based on Mahalanobis Distance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPEMD in this paper.

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Section: Discussionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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“…More broadly, how do we realize a good tradeoff between the availability of data and privacy preservation for data in several fields in course of data processing? Prof. Qi proposed some illuminating approaches 43 – 45 , providing great insights into the above question.…”

Section: Comparisons Between This Work and Related Onesmentioning

confidence: 99%

A novel approach for audible acoustic quick response codes

Zhu

Gao

Wang

2022

Sci Rep

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Compared to image-based quick response (QR) codes, acoustic QR codes have some advantages. However, an acoustic QR scanner cannot recognize an acoustic QR code at a distance of more than two meters from an acoustic QR announcer. To this end, we propose a new sort of acoustic QR code, called an audible acoustic QR code (AAQRC), which employs humanly audible sound to carry users’ information directly. First, a user’s string of characters is translated into a string of pitches. Then, the related algorithms convert the string of pitches into a playable audio file. As a result, an AAQRC is generated, consisting of the audio itself. AAQRC recognition is the opposite process of AAQRC generation. Compared with the existing approach for acoustic QR codes, the new method can recognize acoustic QR codes at a longer distance, even if there are obstacles between the AAQRC announcer and AAQRC scanner.

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“…ML and deep learning, which is a more specialized version of ML typically consisting of neural networks, have also been used to solve important prediction problems within various fields. ML algorithms such as the decision tree (DT), random forest (RF), support vector machine (SVM) have been used for short-term water quality prediction to improve water management and pollution control, maize crop-yield prediction, and blockchain financial products earnings prediction to reduce concern of investors towards the risks and returns of financial products blockchain technology-based applications 12 – 14 ; while deep learning algorithms such as the artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU) have lately been utilized to solve more relatively complex problems such as the prediction of points-of-interest for purposes such as monitoring and maintaining public health following the coronavirus diseases (COVID-19), the prediction of greenhouse climate to ensure crop growth stability, and the prediction of health data with privacy reservation to combat the issue of missing data due to healthcare equipment failure and system updates 15 – 18 . In recent years, the artificial neural network (ANN) and support vector machine (SVM) algorithms have been shown to be among the most established and effective algorithms for application in the prediction of SSLs as shown by numerous existing literature 3 , 6 , 11 , 19 – 30 .…”

Section: Introductionmentioning

confidence: 99%

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Essam

Huang

Birima

et al. 2022

Sci Rep

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High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia.

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LSH-aware multitype health data prediction with privacy preservation in edge environment

Cited by 79 publications

References 44 publications

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

A novel approach for audible acoustic quick response codes

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

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