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Machine Learning-Based Analytical Process for Predicting the Occurrence of Gender-Based Violence
... In Namibia, Africa, Support Vector Machine (SVM) has been implemented to predict the incidence of gender-based violence and to develop early prevention strategies. This approach is particularly valuable given that government programs in the region primarily focus on punishing perpetrators rather than preventing violence [19]. ...
Intimate partner violence (IPV) remains a critical issue that requires data-driven solutions to improve victim profiling and intervention strategies. This study introduces Mujer Segura, an innovative web application designed to collect structured data on IPV cases and predict their severity using machine learning models. The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. The RF model achieved an accuracy of 97%, with a precision of 1.00 for non-severe cases and 0.96 for severe cases, recall values of 0.93 and 1.00 respectively, and an ROC AUC of 0.9534. The GBC model demonstrated an accuracy of 89%, with a precision of 1.00 for non-severe cases and 0.98 for severe cases, recall values of 0.95 and 1.00 respectively, and an ROC AUC of 0.9891. The application also integrates geospatial visualization tools to identify high-risk areas in the State of Mexico, enabling real-time interventions. These findings confirm that machine learning can enhance the timely detection of IPV cases and support evidence-based decision-making for public safety agencies.
Recent times are witnessing rapid development in machine learning algorithm systems,
especially in reinforcement learning, natural language processing, computer and
robot vision, image processing, speech, and emotional processing and understanding.
In tune with the increasing importance and relevance of machine learning models,
algorithms, and their applications, and with the emergence of more innovative
uses–cases of deep learning and artificial intelligence, the current volume presents
a few innovative research works and their applications in real-world, such as stock
trading, medical and healthcare systems, and software automation. The chapters in
the book illustrate how machine learning and deep learning algorithms and models are
designed, optimized, and deployed. The volume will be useful for advanced graduate
and doctoral students, researchers, faculty members of universities, practicing data
scientists and data engineers, professionals, and consultants working on the broad
areas of machine learning, deep learning, and artificial intelligence.
Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.
Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world.
The aim of this article is to provide a brief outline of different research paradigms. It explores the philosophical underpinnings of three major paradigms: positivism, interpretivism, and critical theory. The article starts with a brief description of the four components of a research paradigm: ontology, epistemology, methodology, and methods. This is followed by a detailed discussion of the three paradigms and what characterizes the four components in each of the three paradigms. The article concludes by stating that researchers can investigate different phenomena under any of the three paradigms and they should not eschew the possibility of subscribing to different paradigms because of any concerns that they have to be mutually exclusive. Instead, the decision of choosing a philosophical outlook to investigate a phenomenon should be guided by the necessities and requirements of a research study rather than the obdurate insistence of adhering to one particular philosophical outlook to the exclusion of others.
In recent years, Gender-based violence (GBV) has increased in Namibia and has become a national priority. However, among the numerous national campaigns and research interventions, very little is done from a technology perspective. In 2016, we started exploring several avenues to determine how technology can help fight this phenomenon. GBV is no longer restricted to only physical abuse, but has taken to online forms of abuse as well. We are considering national information systems, tech solutions for ordinary users and institutions alike and social media interventions. We integrated HCI methods successfully in our interventions so far. One challenge is that GBV in Namibia is a stigmatized, sensitive subject and mostly associated with the Social Sciences. This makes it difficult to recruit students and researchers in the technology domains to join the projects due to fear of ridicule. However, we invite other researchers to join us in exploring what HCI can do to alleviate GBV.
Domestic violence is a silent crisis in the developing and underdeveloped countries, though developed countries also remain drowned in the curse of it. In developed countries, victims can easily report and ask help on the contrary in developing and underdeveloped countries victims hardly report the crimes and when it's noticed by the authority it's become too late to save or support the victim. If this kind of problems can be identified at the very beginning of the event and proper actions can be taken, it'll not only help the victim but also reduce the domestic violence crimes. This paper proposed a smart system which can extract victim's situation and provide help according to it. Among of the developing and underdeveloped countries Bangladesh has been chosen though the rate of reporting of domestic violence is low, the extreme report collected by authorities is too high. Case studies collected by different NGO's relating to domestic violence have been studied and applied to extract possible condition for the victims.
For at least the last two decades, the international community in general and the United Nations specifically have attempted to develop robust, accurate and effective conflict early warning system for conflict prevention. One potential and promising component of integrated early warning systems lies in the field of machine learning. This paper aims at giving conflict analysis a basic understanding of machine learning methodology as well as to test the feasibility and added value of such an approach. The paper finds that the selection of appropriate machine learning methodologies can offer substantial improvements in accuracy and performance. It also finds that even at this early stage in testing machine learning on conflict prediction, full models offer more predictive power than simply using a prior outbreak of violence as the leading indicator of current violence. This suggests that a refined data selection methodology combined with strategic use of machine learning algorithms could indeed offer a significant addition to the early warning toolkit. Finally, the paper suggests a number of steps moving forward to improve upon this initial test methodology.
Automatic recognition of activities of daily living (ADL) is an important component in understanding of energy balance, quality of life and other areas of health and well-being. In our previous work, we had proposed an insole based activity monitor - SmartStep, designed to be socially acceptable and comfortable. The goals of the current study were: first, validation of SmartStep in recognition of a broad set of ADL; second, comparison of the SmartStep to a wrist sensor and testing these in combination; third, evaluation of SmartStep accuracy in measuring wear non-compliance and a novel activity class (driving); fourth, performing the validation in free living against a well-studied criterion measure (ActivPAL, PAL Technologies); and fifth, quantitative evaluation of the perceived comfort of SmartStep. The activity classification models were developed from a laboratory study consisting of 13 different activities under controlled conditions. Leave-one-out cross validation showed 89% accuracy for the combined SmartStep and wrist sensor, 81% for the SmartStep alone, and 69% for the wrist sensor alone. When household activities were grouped together as one class, SmartStep performed equally well compared to the combination of SmartStep and wrist-worn sensor (90% vs 94%) whereas the accuracy of the wrist sensor increased marginally (73% from 69%). SmartStep achieved 92% accuracy in recognition of non-wear and 82% in recognition of driving. Participants then were studied for a day in free-living conditions. The overall agreement with ActivPAL was 82.5% (compared to 97% for the laboratory study). The SmartStep scored the best on the perceived comfort reported at the end of the study. These results suggest that insole-based activity sensors may present a compelling alternative or companion to commonly used wrist devices.
Arguably the most important decision at an arraignment is whether to release an offender until the date of his or her next scheduled court appearance. Under the Bail Reform Act of 1984, threats to public safety can be a key factor in that decision. Implicitly, a forecast of “future dangerousness” is required. In this article, we consider in particular whether usefully accurate forecasts of domestic violence can be obtained. We apply machine learning to data on over 28,000 arraignment cases from a major metropolitan area in which an offender faces domestic violence charges. One of three possible post-arraignment outcomes is forecasted within two years: (1) a domestic violence arrest associated with a physical injury, (2) a domestic violence arrest not associated with a physical injury, and (3) no arrests for domestic violence. We incorporate asymmetric costs for different kinds of forecasting errors so that very strong statistical evidence is required before an offender is forecasted to be a good risk. When an out-of-sample forecast of no post-arraignment domestic violence arrests within two years is made, it is correct about 90 percent of the time. Under current practice within the jurisdiction studied, approximately 20 percent of those released after an arraignment for domestic violence are arrested within two years for a new domestic violence offense. If magistrates used the methods we have developed and released only offenders forecasted not to be arrested for domestic violence within two years after an arraignment, as few as 10 percent might be arrested. The failure rate could be cut nearly in half. Over a typical 24-month period in the jurisdiction studied, well over 2,000 post-arraignment arrests for domestic violence perhaps could be averted.
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
Series: What Does That Mean? Gender-based Violence
- M Ott
What Is Machine Learning and Why Is It Important?
- E Burns
How to Perform Data Cleaning for Machine Learning with Python
- J Brownlee
Crowdsourcing city government: Using tournaments to improve inspection accuracy
- E L Glaeser
- A Hillis
- S D Kominers
- M Luca
Machine Learning to study the impact of gender-based violence in the news media
- bello
Gender Differences and Technology Usage amongst Postgraduate Students in a Christian University
- Ambrose A Azeta
- Mc Donald Van Der Merwe
The Organization For World Peace
- T Gellert
Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases
- J Grogger
- R Ivandic
- T Kirchmaier
Machine Learning to study the impact of gender-based violence in the news media
- H J Bello
- N Palomar
- E Gallego
- L Navascués
- C Lozano
Research methodology: Methods And Techniques
- C R Kothari