Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Crop protection from animals based on machine learning

This paper will discuss the present level of research and development on human-wildlife conflicts, there are bad interactions between humans and wild animals that are bad for both the resources of the human population as well as the ecosystems of wildlife. Animal detection has an impact on both human food security and animal welfare because it arises from the conflict between humans and wildlife over natural resources. As the population has grown and many communities' land use patterns have changed, these disputes have become more prevalent in recent years. The Mobile Net SSD type is used in this automatic intrusion and deterrent system for enhanced performance. When a dangerous animal is discovered, the system emits an alarm sound and notifies the relevant authorities, alerting them to the discovery. It is more human- friendly due to the quick detection process, and it is more animal-friendly due to the gentle repulsive process

A text segmentation approach for automated annotation of online customer reviews, based on topic modeling

Online customer review classification and analysis have been recognized as an important problem in many domains, such as business intelligence, marketing, and e-governance. To solve this problem, a variety of machine learning methods was developed in the past decade. Existing methods, however, either rely on human labeling or have high computing cost, or both. This makes them a poor fit to deal with dynamic and ever-growing collections of short but semantically noisy texts of customer reviews. In the present study, the problem of multi-topic online review clustering is addressed by generating high quality bronze-standard labeled sets for training efficient classifier models. A novel unsupervised algorithm is developed to break reviews into sequential semantically homogeneous segments. Segment data is then used to fine-tune a Latent Dirichlet Allocation (LDA) model obtained for the reviews, and to classify them along categories detected through topic modeling. After testing the segmentation algorithm on a benchmark text collection, it was successfully applied in a case study of tourism review classification. In all experiments conducted, the proposed approach produced results similar to or better than baseline methods. The paper critically discusses the main findings and paves ways for future work

Development of a Common Framework for Analysing Public Transport Smart Card Data

The data generated in public transport systems have proven to be of great importance in improving knowledge of public transport systems, being very valuable in promoting the sustainability of public transport through rational management. However, the analysis of this data involves numerous tasks, so that when the value of analysing the data is finally verified, the effort has already been very great. The management and analysis of the collected data face some difficulties. This is the case of the data collected by the current automated fare collection systems. These systems do not follow any open standards and are not usually designed with a multipurpose nature, so they do not facilitate the data analysis workflow (i.e., acquisition, storage, quality control, integration and quantitative analysis). Intending to reduce this workload, we propose a conceptual framework for analysing data from automated fare collection systems in mobility studies. The main components of this framework are (1) a simple data model, (2) scripts for creating and querying the database and (3) a system for reusing the most useful queries. This framework has been tested in a real public transport consortium in a Spanish region shaped by tourism. The outcomes of this research work could be reused and applied, with a lower initial effort, in other areas that have data recorded by an automated fare collection system but are not sure if it is worth investing in exploiting the data. After this experience, we consider that, even with the legal limitations applicable to the analysis of this type of data, the use of open standards by automated fare collection systems would facilitate the use of this type of data to its full potential. Meanwhile, the use of a common framework may be enough to start analysing the data

The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition

Machine learning competitions such as those organized by Kaggle or KDD represent a useful benchmark for data science research. In this work, we present our winning solution to the Game Data Mining competition hosted at the 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The contest consisted of two tracks, and participants (more than 250, belonging to both industry and academia) were to predict which players would stop playing the game, as well as their remaining lifetime. The data were provided by a major worldwide video game company, NCSoft, and came from their successful massively multiplayer online game Blade and Soul. Here, we describe the long short-term memory approach and conditional inference survival ensemble model that made us win both tracks of the contest, as well as the validation procedure that we followed in order to prevent overfitting. In particular, choosing a survival method able to deal with censored data was crucial to accurately predict the moment in which each player would leave the game, as censoring is inherent in churn. The selected models proved to be robust against evolving conditions---since there was a change in the business model of the game (from subscription-based to free-to-play) between the two sample datasets provided---and efficient in terms of time cost. Thanks to these features and also to their a ability to scale to large datasets, our models could be readily implemented in real business settings

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Corrección de una afiliación en Sensors 2023, 23, 16. https://doi.org/10.3390/s23010016An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.This work was developed within the framework of the Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic Sea-Floor Infrastructure for Benthopelagic Monitoring; MarTERA ERA-Net Cofound) and RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades)

Exploring mobile network data for tourism statistics: the collaboration between Istat and Vodafone Business Italia

The paper describes the collaboration between Istat and Vodafone Business Italia to innovate and enhance tourism statistics. The common goal is to evaluate the potential uses of mobile phone data in current surveys and to investigate new outputs for official statistics, such as visiting routes and means of transport. The analysis concerned inbound tourism (foreigners in Italy), domestic tourism (Italians in Italy), and outbound tourism (Italians abroad). The work presents analyses and results for the Province of Rimini and the Municipality of Roma, referred to August 2019/2020 and April 2020, and a trial of the use of the “Welcome SMS” for the estimate of the residents in Italy who travel to foreign countries. Phone data required specific treatments to meet the definitions of official statistics. Some aspects related to the location and definition of overnight stays will require further investigation

Exploring mobile network data for tourism statistics: the collaboration between Istat and Vodafone Business Italia

The paper describes the collaboration between Istat and Vodafone Business Italia to innovate and enhance tourism statistics. The common goal is to evaluate the potential uses of mobile phone data in current surveys and to investigate new outputs for official statistics, such as visiting routes and means of transport. The analysis concerned inbound tourism (foreigners in Italy), domestic tourism (Italians in Italy), and outbound tourism (Italians abroad). The work presents analyses and results for the Province of Rimini and the Municipality of Roma, referred to August 2019/2020 and April 2020, and a trial of the use of the “Welcome SMS” for the estimate of the residents in Italy who travel to foreign countries. Phone data required specific treatments to meet the definitions of official statistics. Some aspects related to the location and definition of overnight stays will require further investigation