2023 Proceedings
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- ItemOpen AccessGeosemantic Surveillance and Profiling of Abduction Locations and Risk Hotspots Using Print Media Reports(MIRG, 2024-10-28) Ogunremi, T.; Adekanmbi, O.; Soronnadi, A.; Akanji, D.Kidnapping poses a significant social risk in Nigeria, often exacerbated by the lack of local crime data, underreporting of cases, and potential involvement of security operatives. Our research aims to combat this menace by developing a data-driven solution that offers comprehensive insights into crime locations and entities. We have generated a reliable dataset by geoparsing newspaper-reported crime locations and entities using Natural Language Processing (NLP) techniques and Google geocoder. Additionally, we implemented clustering and geospatial analysis to identify social risk hotspots. Our method involves designing an algorithm that can geoparse locations in unstructured raw text. The results of our research provide crucial insights and solutions for addressing the threat of kidnapping in Nigeria. We recommend the implementation of our data-driven approach as an intervention strategy to aid law enforcement and policy makers. Our study contributes to the understanding of the spatiotemporal dynamics of kidnapping cases in Nigeria.
- ItemOpen AccessGeo-semantic profiling of brand-specific customer experience using citizen-generated social media comments(MIRG, 2024-10-28) Ngele, E.A good customer experience is likely to influence a customer’s decision to buy positively and equally a negative customer experience will most likely make a customer decide not to buy or go elsewhere. About 73% of customers said that one negative customer experience is enough for them to leave a brand, and go to a competitor. This research work will use text mining and sentiment analysis techniques to identify and categorize brand-specific social media comments into different groups, including positive, negative, and neutral comments. Geo-profiling techniques will be used to identify patterns and trends in customer feedback from different locations. The results will help businesses to understand how to use social media platforms effectively to collect and analyze customer feedback to improve their customer experience and also aid in providing tailored services and products to their customers based on their geographic location. This research will also contribute to the field of customer relationship management by providing new insights into how businesses can use geo-profiling techniques to enhance customer loyalty and satisfaction.
- ItemOpen AccessDynamic Terrain Generation With Deep GANs(MIRG, 2024-10-28) Kathan Gabani; Het Pathak; Isitbhai Thakkar; Nilesh Jain; Senthurapandi Rajendran; Dipyaman MukherjeeExploring the realm of video game creation, procedural terrain generation has stood the test of time as a means to generate extensive new graphical content automatically. Conventional techniques often employ algorithms tailored for specific terrains, meticulously designed by human hands. This study delves into the innovative application of deep convolutional generative adversarial models (DC-GANs) for the dynamic fabrication of authentic terrain maps. Furthermore, we present an inventive methodology for feature extraction that facilitates the specification and manipulation of geographical attributes within the generated terrain.
- ItemOpen AccessCategorizing Approaches to Justify Recommendations(MIRG, 2024-10-28) Yacouba Kyelem; T. Frédéric Ouedraogo; K. Kisito KaboreRecommendation justification enables users to understand the reasons and motivation behind the recommendation of an item in a recommender system. It makes the recommendation model much more transparent, and improves user satisfaction. It is because of the important role assigned to the justification of recommendations that the present work aims to identify the approaches and methods for justifying recommendations that exist in the literature. The state of the art has enabled us to categorize the different approaches to recommendation justification. There are two approaches to recommendation justification: the linked model and the post-hoc models. The data used for justification are external or internal to the items.
- ItemOpen AccessEvaluating student’s performance using k- mean clustering(MIRG, 2024-10-28) Shahid Eqbal; Ayush Singh; Balkrishna Pandey; Deepa SinghThe analysis and evaluation of a student's academic achievement are now difficult tasks for the academic community to do. Classifying student achievement is a difficult scientific task in the actual world. Thus, a system to evaluate students’ performance utilizing a deterministic model and the k-means clustering algorithm is described in this study. The analysis findings will help academic planners determine how well students performed over a particular semester and what initiatives they need to take to raise students’ performance.