Purpose: Women entrepreneurship has grown significantly all over the world, and it is widely established that entrepreneurship is important for economic growth and wealth. Despite those facts, women’s participation in entrepreneurship is lower than men’s in almost all societies. Those phenomena get the attention of scholars from diverse disciplines, all of them interested in the behaviour and profile of female entrepreneurs and their business success rates. Several isolated factors were studied, with positive and negative effects on each stage of the entrepreneur process, for women entrepreneurs, so the purpose of this research is identify, classify by their impact and organise those factors in relation to the stages of the entrepreneur process. Design/methodology/approach: The literature on factors affecting female entrepreneurship produced since January 2010 until October 2015 is analysed to define entrepreneurial success, identify factors affecting success at each stage of the entrepreneurial process and propose and organise those factors at individual and environment levels. Findings: Several factors affecting female entrepreneurial success at each stage of the entrepreneurship process were found and organised at the internal (individual), micro, meso and macro environment level. In the literature reviewed, the most considered factors are: at the internal level, human capital, education and experience, with effects on the opportunity identification stage of the entrepreneurial process, and at the micro environment level, access to resources with effects on the opportunity recognition, acquiring resources and entrepreneurial performance stages, both with influence on quantitative and qualitative indicators of success. Originality/value: This paper proposes an integrated classification and an array for all those factors that have an influence on women’s entrepreneurship and its success, relating those to the entrepreneurship process.

Presently, in several parts of the world, water consumption is not measured or visualized in real time, in addition, water leaks are not detected in time and with high precision, generating unnecessary waste of water. That is why this article presents the implementation of a smart water measurement consumption system under an architecture design, with high decoupling and integration of various technologies, which allows real-time visualizing the consumptions, in addition, a leak detection algorithm is proposed based on rules, historical context, and user location that manages to cover 10 possible water consumption scenarios between normal and anomalous consumption. The system allows data to be collected by a smart meter, which is preprocessed by a local server (Gateway) and sent to the Cloud from time to time to be analyzed by the leak detection algorithm and, simultaneously, be viewed on a web interface. The results show that the algorithm has 100% Accuracy, Recall, Precision, and F1 score to detect leaks, far better than other procedures, and a margin of error of 4.63% recorded by the amount of water consumed.

Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference. However, no reviews of the literature have been found that chronicle their importance and development over time. The aim of this study is to provide a systematic review of the literature that details the evolution and advancement of DBNs, focusing in the period 1997–2019 that emphasize the aspects of modeling, learning and inference. While the literature presents temporal event networks, knowledge encapsulation, relational and time varying representations as the four predominant DBN modeling approaches, this work groups them as essential techniques within DBNs and help practitioners by associating each to various challenge that arise in pattern discovery and prediction in dynamic processes. Regarding learning, the predominant methods mainly focus on scoring with greedy search. Finally, our study suggests that the main methods used in DBN inference extend or adapt those used in static BNs, and are oriented to either optimize processing time or error rate.

Background: Healthcare fraud entails great financial and human losses; however, there is no consensus regarding its definition, nor is there an inventory of its manifestations and factors. The objective is to identify the definition, manifestations and factors that influence health insurance fraud (HIF).Methods: A scoping review on health insurance fraud published between 2006 and 2020 was conducted in ACM, EconPapers, PubMed, ScienceDirect, Scopus, Springer and WoS.Results: Sixty-seven studies were included, from which we identified 6 definitions, 22 manifestations (13 by the medical provider, 7 by the beneficiary and, 2 by the insurance company) and 47 factors (6 macroenvironmental, 15 mesoenvironmental, 20 microenvironmental, and 6 combined) associated with health insurance fraud. We recognized the elements of fraud and its dependence on the legal framework and health coverage. From this analysis, we propose the following definition: “Health insurance fraud is an act of deception or intentional misrepresentation to obtain illegal benefits concerning the coverage provided by a health insurance company”. Among the most relevant manifestations perpetuated by the provider are phantom billing, falsification of documents, and overutilization of services; the subscribers are identity fraud, misrepresentation of coverage and alteration of documents; and those perpetrated by the insurance company are false declarations of benefits and falsification of reimbursements. Of the 47 factors, 25 showed an experimental influence, including three in the macroenvironment: culture, regulations, and geography; five in the mesoenvironment: characteristics of provider, management policy, reputation, professional role and auditing; 12 in the microenvironment: sex, race, condition of insurance, language, treatments, chronic disease, future risk of disease, medications, morale, inequity, coinsurance, and the decisions of the claims-adjusters; and five combined factors: the relationships between beneficiary-provider, provider-insurance company, beneficiary-insurance company, managers and guānxi.Conclusions: The multifactorial nature of HIF and the characteristics of its manifestations depend on its definition; Identifying the influence of the factors will support subsequent attempts to combat HIF.

As cervical cancer is considered one of the leading causes of death for women globally, different screening techniques have emerged. As the Papanicolaou technique generates high numbers of false negatives due to only testing 20% of a sample, the liquid-based cytology technique was developed to test 100% of the sample and improve accuracy. However, as the larger sample size has made it difficult to detect the lesion images through a microscope, studies have looked for ways to intelligently analyze sample. The aim of this study is to develop an artificial intelligence image recognition system that detects the lesion level of cervical cancer of liquid-based Pap smears under the Bethesda classification of cancer (NI/LSIEL/HSIEL/SCC). For this purpose, six activities were carried out: dataset selection, data augmentation, optimization, model development, evaluation and system construction. A dataset built from publicly available Pap smear images and passed through data augmentation algorithms generated a total of 2,676 images. Two models, ResNet50V2 and ResNet101V2, were developed under Deep Learning and Transfer Learning protocols. The evaluation showed that the ResNet50V2 model obtained better performance, where the classification of HSIL and SCC type images obtained a precision of 0.98 and achieved an accuracy of 0.97. Finally, the system based on the ResNet50V2 model was built and its performance was validated.

The bruise dating can have important medicolegal implications in family violence and violence against women cases. However, studies show that the medical specialist has 50% accuracy in classifying a bruise by age, mainly due to the variability of the images and the color of the bruise. This research proposes a model, based on deep convolutional neural networks, for bruise dating using only images, by age ranges, ranging from 0–2 days to 17–30 days, and images of healthy skin. A 2140 experimental bruise photograph dataset was constructed, for which a data capture protocol and a preprocessing procedure are proposed. Similarly, 20 classification models were trained with the Inception V3, Resnet50, MobileNet, and MnasNet architectures, where combinations of learning transfer, cross‐validation, and data augmentation were used. Numerical experiments show that classification models based on MnasNet have better results, reaching 97.00% precision and sensitivity, and 99.50% specificity, exceeding 40% precision reported in the literature. Also, it was observed that the precision of the model decreases with the age of the bruise.