Big data analytics for preventive medicine

Razzak, Muhammad Imran; Imran, Muhammad; Xu, Guandong

doi:10.1007/s00521-019-04095-y

Cited by 135 publications

(50 citation statements)

References 163 publications

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“…[2] From system users’ perspective, mobile shoppers are mobile system users who typically interact with the mobile system to achieve all the possible functions, including searching and finding relevant product information, completing payment, and tracking product deliveries. Therefore, the usefulness and accessibility of mobile systems are the important issues in the research of online impulsive purchases [ 7 , 8 ]; all these factors are of great importance in determining consumers’ mobile impulsive purchases. In addition, existing literature has already examined the effect of emotional factors on consumers’ impulsive buying behavior.…”

Section: Introductionmentioning

confidence: 99%

Research on mobile impulse purchase intention in the perspective of system users during COVID-19

Zhang

Leng

Liu

2020

Pers Ubiquit Comput

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COVID-19 has caused a serious impact on the global economy. Effectively stimulating consumption has become a momentous mission in responding to the impact of the epidemic. The popularity of mobile shopping makes shopping behavior no longer limited by time and space, so impulse purchase is more commonly seen nowadays; it can effectively promote residents' consumption. However, consensus has not been reached regarding how impulse purchase emerges as a phenomenon, thus making it difficult to promote consumers' purchase behavior. This article aims to explore the generation process of consumers' impulsive purchase intention during the COVID-19 outbreak from the perspective of system users. For this purpose, the research proposes three mobile situation factors: personalized recommendation, visual appeal, and system usability. They have a positive impact on impulse purchase intention by influencing perceived arousal and perceived enjoyment. The experimental method is used for data collection and hypothesis testing. All the hypotheses are supported. And the theoretical value of the model of "mobile environment stimulation-consumer emotion-impulse purchase intention" is confirmed. Based on the conclusion, management suggestions are proposed for mobile shopping merchants from the perspective of improving consumers' shopping experience and expanding marketing.

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Section: Introductionmentioning

confidence: 99%

Research on mobile impulse purchase intention in the perspective of system users during COVID-19

Zhang

Leng

Liu

2020

Pers Ubiquit Comput

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“…Table 6 lists all the extracted features along with their statistical measures of mean and standard deviation (STD) for method I (DWT) and method II (EMD). We extracted time domain [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], spectral [ 46 , 47 ], fractal and chaos [ 48 , 49 ], chroma [ 50 , 51 ], cepstral [ 52 ], and texture features [ 53 ] and analyzed them statistically.…”

Section: Methodsmentioning

confidence: 99%

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Khan

Aziz

Akram

et al. 2021

Sensors

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Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

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“…The use of machine learning classifiers in a variety of different domains of science and business is becoming as fruitful as ever before. The applications involve the analytical computations for big data like concentric computing model [6], event detection for preventive medication [7], text classification [8] and data centric analysis [9]. Moreover, real-time anomaly detection is also beneficial by using clustering mechanism for big data repositories [10], [11].…”

Section: State-of-the-art In Ntlmentioning

confidence: 99%

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

et al. 2020

Self Cite

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With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

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Big data analytics for preventive medicine

Cited by 135 publications

References 163 publications

Research on mobile impulse purchase intention in the perspective of system users during COVID-19

Research on mobile impulse purchase intention in the perspective of system users during COVID-19

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

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