To propose a model where match outcome is predicted ball by ball at the start of the second inning. Our methodology not only incorporates the dynamically updating game context as the game progresses, but also includes the relative strength between the two teams playing the match. We used 692 matches from all seasons (2008–2018) to train our model, and we used all 59 matches from the current season (2019) to test its performance. Here we have engineered 11 players and 10 bowlers, and all their metrics are tracked as a function of each ball of each over throughout the match during the second inning, also keeping in the consideration of dynamically changing target score as one of the attributes. Initially, we tried Logistic Regression, Naive Bayes, K-Nearest Neighbour (KNN), Support Vector Machine, Decision Tree, Random Forest, Boosting, Bagging, and Gradient Boosting with an accuracy of 76.47%(+/–3.77%). With deep learning, we tried the various flavours of LSTM and GRU like vanilla, Bidirectional and stacked to train our models and the results found are very impressive with an accuracy of 76.13%(+/–2.59%). All of these flavors were tested using various approaches such as one-to-one sequencing, one-to-many sequencing, many-to-one sequencing, and many-to-many sequencing, which are discussed in this paper. An accurate prediction of how many runs a batsman is likely to score and how many wickets a bowler is likely to take in a match will help the team management select the best players for each match.
Aim:
To comparatively evaluate in vitro the cyclic fatigue resistance of different file systems based on different manufacturing technologies after exposure to NaOCl and multiple sterilization cycles.
Materials and Methods:
Sixty new Nickel − Titanium (NiTi) rotary files were selected and divided into five groups (n = 12) based on different manufacturing technology. These groups were: Protaper Universal (GPT) (DentsplyMaillefer, Ballaigues, Switzerland) Protaper Next (GPTN) (Dentsply, Tulsa, OK, USA), Twisted files (GTF) (Sybron Endo, Orange, CA, USA), Neoniti (GNL) (Neolix, France), and Hyflex CM (GHCM) (Coltene/whaledent inc., 235 Ascort Parkway, Cuyahoga falls, OH, USA). Files were sterilized in an autoclave at 121°C at 15 psi for 15 min and exposed to 5.0% NaOCl solution for 30 s before cyclic fatigue testing till instrument fracture using a standardized grooved block assembly with the artificial canal at 90° angle of curvature using a 16:1 reduction hand-piece powered by a torque-controlled motor. This whole procedure of autoclaving, immersion in sodium hypochlorite solution and rotating in a stainless steel assembly was repeated again and again until the file fractured.
Results:
The highest mean for the number of cycles before fracture was observed with Neoniti (GNL) and lowest for Hyflex CM (GHCM). The difference between GPT vs GTF, GPTvs GNL, GNLvs GHCM; GPTNvs GTF, GPTNvs GNL, GPTNvs GHCM; GTFvs GNL, GTFvs GHCM and GNL VS GHCM was statistically significant, whereas the difference between GPTvs GPTN was statistically nonsignificant.
Conclusion:
Neoniti and twisted file systems resisted the maximum number of cycles to fracture at 90° angle of curvature after exposure to 5% sodium hypochlorite solution and multiple autoclaving cycles. Hyflex CM showed the least resistance in the similar conditions.
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