Applying deep learning algorithm to maintain social distance in public place through drone technology

2020 ◽  
Vol 16 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Lalitha Ramadass ◽  
Sushanth Arunachalam ◽  
Sagayasree Z.
Keyword(s):  
Social Distance ◽  
Learning Algorithm ◽  
Real Data ◽  
Public Place ◽  
Efficient Manner ◽  
Data Set ◽  
Content Type ◽  
Social Distancing ◽  
The Public ◽  
The People

Purpose The purpose of this paper is to inspect whether the people in a public place maintain social distancing. It also checks whether every individual is wearing face mask. If both are not done, the drone sends alarm signal to nearby police station and also give alarm to the public. In addition, it also carries masks and drop them to the needed people. Nearby, traffic police will also be identified and deliver water packet and mask to them if needed. Design/methodology/approach The proposed system uses an automated drone which is used to perform the inspection process. First, the drone is being constructed by considering the parameters such as components selection, payload calculation and then assembling the drone components and connecting the drone with the mission planner software for calibrating the drone for its stability. The trained yolov3 algorithm with the custom data set is being embedded in the drone’s camera. The drone camera runs the yolov3 algorithm and detects the social distance is maintained or not and whether the people in public is wearing masks. This process is carried out by the drone automatically. Findings The proposed system delivers masks to people who are not wearing masks and tells importance of masks and social distancing. Thus, this proposed system would work in an efficient manner after the lockdown period ends and helps in easy social distance inspection in an automatic manner. The algorithm can be embedded in public cameras and then details can be fetched to the camera unit same as the drone unit which receives details from the drone location details and store it in database. Thus, the proposed system favours the society by saving time and helps in lowering the spread of corona virus. Practical implications It can be implemented practically after lockdown to inspect people in public gatherings, shopping malls, etc. Social implications Automated inspection reduces manpower to inspect the public and also can be used in any place. Originality/value This is the original project done with the help of under graduate students of third year B.E. CSE. The system was tested and validated for accuracy with real data.

Electing the Senate

2017 ◽  
Author(s):  
Wendy J. Schiller ◽  
Charles Stewart III
Keyword(s):  
Original Data ◽  
State Legislators ◽  
Internal Conflict ◽  
Data Set ◽  
Senate Elections ◽  
Political Actors ◽  
The Public ◽  
Seventeenth Amendment ◽  
The People ◽  
Election Process

From 1789 to 1913, U.S. senators were not directly elected by the people—instead the Constitution mandated that they be chosen by state legislators. This radically changed in 1913, when the Seventeenth Amendment to the Constitution was ratified, giving the public a direct vote. This book investigates the electoral connections among constituents, state legislators, political parties, and U.S. senators during the age of indirect elections. The book finds that even though parties controlled the partisan affiliation of the winning candidate for Senate, they had much less control over the universe of candidates who competed for votes in Senate elections and the parties did not always succeed in resolving internal conflict among their rank and file. Party politics, money, and personal ambition dominated the election process, in a system originally designed to insulate the Senate from public pressure. The book uses an original data set of all the roll call votes cast by state legislators for U.S. senators from 1871 to 1913 and all state legislators who served during this time. Newspaper and biographical accounts uncover vivid stories of the political maneuvering, corruption, and partisanship—played out by elite political actors, from elected officials, to party machine bosses, to wealthy business owners—that dominated the indirect Senate elections process. The book raises important questions about the effectiveness of Constitutional reforms, such as the Seventeenth Amendment, that promised to produce a more responsive and accountable government.

Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeepkumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Chronic Disease ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Experimental Result ◽  
Data Set ◽  
Content Type ◽  
Learning Classifier ◽  
Feature Selection Approach

Purpose According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease. Design/methodology/approach A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases. Findings The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6. Originality/value The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(nk+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

scholarly journals Bayesian inversion of magnetotelluric data considering dimensionality discrepancies

2020 ◽  
Vol 223 (3) ◽  
pp. 1565-1583
Author(s):  
Hoël Seillé ◽  
Gerhard Visser
Keyword(s):  
Learning Algorithm ◽  
Synthetic Data ◽  
Real Data ◽  
Error Model ◽  
Bayesian Inversion ◽  
Magnetotelluric Data ◽  
Data Set ◽  
Likelihood Functions ◽  
Training Images ◽  
Phase Tensor

SUMMARY Bayesian inversion of magnetotelluric (MT) data is a powerful but computationally expensive approach to estimate the subsurface electrical conductivity distribution and associated uncertainty. Approximating the Earth subsurface with 1-D physics considerably speeds-up calculation of the forward problem, making the Bayesian approach tractable, but can lead to biased results when the assumption is violated. We propose a methodology to quantitatively compensate for the bias caused by the 1-D Earth assumption within a 1-D trans-dimensional Markov chain Monte Carlo sampler. Our approach determines site-specific likelihood functions which are calculated using a dimensionality discrepancy error model derived by a machine learning algorithm trained on a set of synthetic 3-D conductivity training images. This is achieved by exploiting known geometrical dimensional properties of the MT phase tensor. A complex synthetic model which mimics a sedimentary basin environment is used to illustrate the ability of our workflow to reliably estimate uncertainty in the inversion results, even in presence of strong 2-D and 3-D effects. Using this dimensionality discrepancy error model we demonstrate that on this synthetic data set the use of our workflow performs better in 80 per cent of the cases compared to the existing practice of using constant errors. Finally, our workflow is benchmarked against real data acquired in Queensland, Australia, and shows its ability to detect the depth to basement accurately.

scholarly journals Detecting anomalies in financial statements using machine learning algorithm

2019 ◽  
Vol 4 (2) ◽  
pp. 181-201 ◽  
Author(s):  
Mark Lokanan ◽  
Vincent Tran ◽  
Nam Hoai Vuong
Keyword(s):  
Anomaly Detection ◽  
Learning Algorithm ◽  
Financial Statements ◽  
Data Set ◽  
Financial Reports ◽  
Accounting Data ◽  
Content Type ◽  
Listed Firms ◽  
Detection Model ◽  
Credit Worthiness

Purpose The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method. Design/methodology/approach The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly. Findings The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable. Research limitations/implications The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels. Practical implications The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud. Originality/value This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

Knowledge-driven business model innovation through the introduction of equity investment: evidence from China’s primary market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiali Zheng ◽  
Han Qiao ◽  
Xiumei Zhu ◽  
Shouyang Wang
Keyword(s):  
Business Model ◽  
Business Models ◽  
Learning Algorithm ◽  
Empirical Studies ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Business Model Innovation ◽  
Data Set ◽  
Equity Investment ◽  
Content Type

Purpose This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market. Design/methodology/approach Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation. Findings The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI Research limitations/implications The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data. Practical implications This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes. Social implications From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation. Originality/value The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

Hedonic value and crowdfunding project performance: a propensity score matching-based analysis

2017 ◽  
Vol 9 (2) ◽  
pp. 169-186 ◽  
Author(s):  
Liang Zhao ◽  
Tsvi Vinig
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Real Data ◽  
Project Performance ◽  
Probit Regression ◽  
Data Set ◽  
Content Type ◽  
Hedonic Value ◽  
Utilitarian Value ◽  
The Impact

Purpose In the existing literature on crowdfunding project performance, previous studies have given little attention to the impact of investors’ hedonic value and utilitarian value on project results. In a crowdfunding setting, utilitarian value is somehow hard to satisfy due to information asymmetry and adverse selection problem. Therefore, the projects with more hedonic value can be more attractive for potential investors. Lucky draw is a method to increase consumer hedonic value, and it can influence investors’ behavior as a result. The authors hypothesize that projects with hedonic treatment (lucky draw) may have higher probability to win their campaign than others. The paper aims to discuss these issues. Design/methodology/approach A unique self-extracted two-year Chinese crowdfunding platform real data set has been applied as the analysis sample. The authors first employ propensity score matching methods to control for the endogeneity of hedonic treatment adoption (lucky draw). The authors then run OLS regression and probit regression in order to test the hypotheses. Findings The analysis suggests a significant positive relationship not only between project lottery adoption and project results but also between project lottery adoption and project popularity. Originality/value The results suggest that an often ignored factor – hedonic treatment (lucky draw) – can play an important role in crowdfunding project performance.

Race and attitudes toward police: the mediating effect of social distance

2015 ◽  
Vol 38 (2) ◽  
pp. 314-332 ◽  
Author(s):  
Joongyeup Lee ◽  
Jennifer C. Gibbs
Keyword(s):  
Community Policing ◽  
Social Distance ◽  
Online Survey ◽  
Minority Groups ◽  
Mediating Effect ◽  
Consistent Finding ◽  
Content Type ◽  
The Public ◽  
The Social ◽  
Attitudes Toward The Police

Purpose – Given the consistent finding in the literature that members of minority groups hold less favorable views of the police than white citizens, social distance may be an important, yet untested, mediator. The purpose of this paper is to examine the mediating effect of social distance net of other established correlates. Design/methodology/approach – A sample of students attending a university in the northeastern USA completed an online survey in 2013. The survey was about their contact with the police, attitudes toward the police, and lifestyles, among others. Findings – Race, along with other predictors, significantly influenced confidence in police. However, race is the only factor that turns nonsignificant when social distance is included in the model. Mediation tests confirmed that social distance mediates the relationship between race and confidence in the police. Research limitations/implications – To maximize confidence in the police, administrators should focus on closing the social distance between the public and the police through initiatives like community policing. Originality/value – While there is extensive research on public attitudes toward the police, social distance has been neglected as a determinant, despite movements like community policing that promote citizens’ relational closeness to the police – that is, to decrease the social distance between police and the public. The current study would be an exploratory study and reference for future studies.

scholarly journals Overlooked and undervalued: the caring contribution of older people

2014 ◽  
Vol 41 (5) ◽  
pp. 397-419 ◽  
Author(s):  
Fiona Carmichael ◽  
Marco G. Ercolani
Keyword(s):  
Health Care ◽  
Older People ◽  
Personal Care ◽  
Data Set ◽  
Content Type ◽  
The People ◽  
Ordered Logistic Regression ◽  
Allocation Of Time ◽  
Nationally Representative

Purpose – Older people are often perceived to be a drain on health care resources. This ignores their caring contribution to the health care sector. The purpose of this paper is to address this imbalance and highlight the role of older people as carers. Design/methodology/approach – The study uses a unique data set supplied by a charity. It covers 1,985 caregivers, their characteristics, type and amount of care provided and the characteristics and needs of those cared-for. Binary and ordered logistic regression is used to examine determinates of the supply of care. Fairlie-Oaxaca-Blinder decompositions are used to disentangle the extent to which differences in the supply of care by age are due to observable endowment effects or coefficient effects. Nationally representative British Household Panel Survey data provide contextualization. Findings – Older caregivers are more intensive carers, caring for longer hours, providing more co-residential and personal care. They are therefore more likely to be in greater need of assistance. The decompositions show that their more intensive caring contribution is partly explained by the largely exogenous characteristics and needs of the people they care for. Research limitations/implications – The data are regional and constrained by the supplier's design. Social implications – Older carers make a significant contribution to health care provision. Their allocation of time to caregiving is not a free choice, it is constrained by the needs of those cared-for. Originality/value – If the burden of care and caring contribution are measured by hours supplied and provision of intimate personal care, then a case is made that older carers experience the greatest burden and contribute the most to the community.

Empirical study on the effect of using synthetic attributes on classification algorithms

2017 ◽  
Vol 10 (2) ◽  
pp. 111-129 ◽  
Author(s):  
Ali Hasan Alsaffar
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Student Performance ◽  
Software Tool ◽  
Real Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Past Performance ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

scholarly journals Harnessing the potential of tobacco cessation programme amidst COVID-19 pandemic in India

2020 ◽  
Vol 32 (4) ◽  
pp. 740-742
Author(s):  
Garima Bhatt ◽  
Sonu Goel
Keyword(s):  
Supply Chain ◽  
Health Services ◽  
Health Systems ◽  
21St Century ◽  
Tobacco Cessation ◽  
Public Place ◽  
Tobacco Products ◽  
Social Distancing ◽  
The Public ◽  
Indian Health

The COVID-19 pandemic of the 21st Century continues to spread, and tobacco users are at a higher risk of contracting the disease. As a measure to contain its spread, many nations have called for various measures like maintaining social distancing norms, the prohibition of spitting in the public place, partial or complete lockdown, and many more. This shutdown episode has disrupted the entire supply chain in our country, and it is quite natural that tobacco users are also experiencing a scarcity of tobacco products, like others. This adverse situation is an opportune moment for the Indian health systems to target tobacco users to motivate, facilitate, and support the cessation process. Additionally, social distancing can be achieved by utilizing and optimizing our existing health services. In our country, we have dedicated regional & national quitlines and m-Cessation facilities for tobacco users who are willing to quit. These initiatives could reduce the risk of COVID among tobacco users, facilitate the tobacco cessation movement, and provide credence to the advocacy for increasing taxes on tobacco products in the country.

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