An Assistive Object Recognition System for Enhancing Seniors Quality of Life

AbstractThis paper presents an indoor object recognition system based on the histogram of oriented gradient and Machine Learning (ML) algorithms; such as Support Vector Machines (SVMs), Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms, for classifying different indoor objects to improve quality of elderly people's life. The proposed approach consists of three phases; namely segmentation, feature extraction, and classification phases. Datasets used for these experiments, are totally consisted of 347 images with different eight indoor objects used for both training and testing datasets. Training dataset is divided into eight classes representing the different eight indoor objects. Experimental results showed that RF classification algorithm outperformed both SVMs and LDA algorithms, where RF achieved 80.12%, SVMs and LDA achieved 77.81% and 78.76% respectively

Multimodal framework based on audio‐visual features for summarisation of cricket videos

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166171/1/ipr2bf02094.pd

Football analytics: a literature analysis from 2010 to 2020

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe overall goal for the current study is to present a literature review of analytics, precisely machine learning (ML) reference authors in terms of methods and applicable scopes of study, in football where is a field that historically there are empirical decisions and the usage of analytics has been growing intensely. The research aims to list relevant academic contributions published between 2010 and 2020, performing a comparable picture per authors across the following subsets: player individual technical skills and team performance. Furthermore, the approach will provide a summary of studies for machine learning methods applied in football. Such outcomes of this study would contribute to the discussion about football analytics. Regarding that these summaries can drive researchers to have a deep dive into the fields of interest straight to references preview studied in the thesis. Results indicate that football analytics has broadly vast opportunities in terms of research, regarding machine learning methods and a high potential to have a deep exploration of team and player perspective. This study can leverage and pavement new further in-depth and targeted investigation toward football analytics

Predicción de cambios en el rendimiento de futbolistas

Nuestro proyecto trata de predecir la mejora de rendimiento entre dos temporadas consecutivas de un jugador de fútbol a través del análisis de datos. El principal objetivo, es ser capaces de determinar si el jugador mejorará su rendimiento en al menos un tanto por ciento dado, y tener además alguna indicación de lo probable que resulta que esto suceda. Para esto último, proponemos utilizar métodos de aprendizaje automático como la regresión logística, que indican qué probabilidad hay de que el elemento sea clasificado en una clase u en otra. Esta probabilidad se verá afectada, a la hora de hablar de su fiabilidad, por la precisión del propio modelo, el cual generaremos a partir de un conjunto de datos de jugadores cedido por la empresa Driblab, que se dedica al recuitring de jugadores y que ha mostrado su interés en este proyecto. El trabajo trata el preprocesamiento de los datos, la obtención de los ficheros con los que entrenamos y probamos el modelo, los clasificadores que hemos utilizado, y los motivos que nos encaminan a tomar ciertas decisiones. Cabe destacar que, a pesar de la complejidad del problema, los resultados obtenidos en clasificación son buenos y permiten detectar a jugadores que van a incrementar su rendimiento de forma notable la siguiente temporada