Semantic Pattern Detection in COVID-19 Using Contextual Clustering and Intelligent Topic Modeling

The Covid-19 pandemic is the deadliest outbreak in our living memory. So, it is need of hour, to prepare the world with strategies to prevent and control the impact of the epidemics. In this paper, a novel semantic pattern detection approach in the Covid-19 literature using contextual clustering and intelligent topic modeling is presented. For contextual clustering, three level weights at term level, document level, and corpus level are used with latent semantic analysis. For intelligent topic modeling, semantic collocations using pointwise mutual information(PMI) and log frequency biased mutual dependency(LBMD) are selected and latent dirichlet allocation is applied. Contextual clustering with latent semantic analysis presents semantic spaces with high correlation in terms at corpus level. Through intelligent topic modeling, topics are improved in the form of lower perplexity and highly coherent. This research helps in finding the knowledge gap in the area of Covid-19 research and offered direction for future research.

Semantic Pattern Detection in Covid-19 using Contextual Clustering and Intelligent Topic Modeling

The Covid-19 pandemic is the deadliest outbreak in our living memory. So, it is need of hour, to prepare the world with strategies to prevent and control the impact of the epidemics. In this paper, a novel semantic pattern detection approach in the Covid-19 literature using contextual clustering and intelligent topic modeling is presented. For contextual clustering, three level weights at term level, document level, and corpus level are used with latent semantic analysis. For intelligent topic modeling, semantic collocations using pointwise mutual information(PMI) and log frequency biased mutual dependency(LBMD) are selected and latent dirichlet allocation is applied. Contextual clustering with latent semantic analysis presents semantic spaces with high correlation in terms at corpus level. Through intelligent topic modeling, topics are improved in the form of lower perplexity and highly coherent. This research helps in finding the knowledge gap in the area of Covid-19 research and offered direction for future research.

Cross-Layer Learning

Deep learning-based investigation mechanisms are available for conventional forensics, but not for IoT forensics. Dividing the system into different layers according to their functionalities, collecting data from each layer, finding the correlating factor, and using it for pattern detection is the fundamental concept behind the proposed intelligent system. The authors utilize this notion for embedding intelligence in forensics and speed up the investigation process by providing hints to the examiner. They propose a novel cross-layer learning architecture (CCLA) for IoT forensics. To the best of their knowledge, this is the first attempt to incorporate deep learning into the forensics of the IoT ecosystem.

Patterns of Injury in the Spanish Football League Players

Background: The study of football injuries is a subject that concerns the scientific community. The problem of most of the available research is that it is mainly descriptive. The objective of this study is to discover and analyse the patterns of injury in the Spanish Football League (2016–2017 season). Methods: The sample data consisted of 136 given injuries identified by the official physicians of the football clubs. The analysis was performed by using traditional statistic tests, T-pattern detection and polar coordinate analysis. Results: The analysis revealed several patterns of injury: (a) The defender suffered a rupture of the hamstring muscles after a sprint, (b) knee sprains happened due to a received tackle, (c) fibrillar adductor rupture appeared mostly among defenders and (d) fibrillar ruptures took place mostly throughout the first part. Conclusions: There is a marked shift in the tendency regarding the player who gets more injured, from the midfielder to the defender. The most common injury was fibrillar rupture. The most common scenario in which this injury occurred was that in which the player injured himself after a sprint (24%). A week without competing seems to be insufficient as a prevention mechanism for injuries.

Development of Goal Sensor in Futsal Goal Using Pattern Detection

Development of Goal Sensor Tools in Futsal Sports is a tool in matches and training to detect goals in futsal games. This tool can help teams, coaches and referees in detecting goals in matches and training. This tool was made with the aim of assisting the coach in applying the game system to defend and attack in order to get goals and so as not to concede. This tool serves to detect goals without any doubt in goal decision making.This study aims to develop a goal sensor to assist in matches and training. The population in this study were 30 athletes from the Joint Club FS and Tibor FC as well as 6 experts in each field. The form of the tool that has been made in advance is validated by 3 experts, namely 1 sports expert from the University who has a sports education background, 1 futsal coach expert and 1 electronics expert. The small group test involved 10 athletes and 3 experts, then the results were validated with an average value of the third validity of 77%. The results of the large group trial involved 20 people and 6 experts, then the results were validated with an average value of the sixth validity of 84%. Based on the results of validation by experts, it can be concluded that the development of a goal sensor in the futsal goal using 2020 pattern detection is valid and can be used. However, this tool cannot be disseminated because there are still many shortcomings in this tool.

T-Pattern Detection and Analysis of Football Players’ Tactical and Technical Defensive Behaviour Interactions: Insights for Training and Coaching Team Coordination

This article aims to study the coordination of the defenders’ tactical and technical behaviour of successful teams to recover the ball according to contextual variables. A total of 15,369 (480.28 ± 112.37) events and 49 to 12,398 different patterns in 32 games of the 2014 FIFA World Cup’s play-offs were detected and analysed. Results evidenced a T-pattern of the first defender pressuring the ball carrier and his teammates concentrating at the same zone to cover him or space, leading to ball recovery. Field zones, first defender tactical and technical behaviours, and ball carrier first touch constituted opportunities for defenders to coordinate themselves. Moreover, the third defender had a predominant role in his teammates’ temporisation and covering zone behaviours. In the draw, first half, second-tier quality of opponent and play-offs excluding third place and final matches, the ball regularly shifted from upper to lower field zones in short periods, resulting in ball recovery or shot on goal conceded. Defenders performed behaviours farther from the ball carrier, and player-marking were most recurrent to an effective defence. This study’s findings could help coaches give specific tips to players regarding interpersonal coordination in defence and set strategies to make tactical behaviour emerge globally.