Sports analytics have benefited immensely from the growth and popularity of Artificial Intelligence and Machine Learning. These techniques enable sports analysts to evaluate player performance more effectively. A literature review of player performance evaluation methods shows the need to develop a new performance evaluation index for Twenty 20 crickets. A novel framework was proposed to evaluate batsman strength based on individual performance, role in the team, and team interactions. Traditionally, proposed ranking systems are derived from static networks, that is, the aggregation of game results over time. However, the scores of the players (or teams) fluctuate over time. Intuitively, defeating a renowned player during peak performance is more rewarding than defeating the same player during other periods. To account for this, we propose a new method and apply it to the T20 format Indian Premier League. The method serves three main purposes: First, it creates a new performance index for players to rank them more accurately and effectively. Second, the players were clustered based on their expertise. In the third phase, a social network analysis approach was applied to visualize and analyze crickets as a network to gain better insights into players’ team interactions. This novel approach is a helpful index for sports coaches, analysts, cricket fans, and managers to evaluate player performance and rank for future aspects.

Compared with common intelligent service, full-scene intelligent service has its uniqueness in high integration, synergy, and technological spillover. However, the traditional service or business model theories cannot precisely elaborate its sociotechnical contextual nature and value creation logic. To fill this knowledge gap, we provide initial insights into the value co-creation logic in full-scene intelligent service by exploring the value co-creation elements using a data-driven text mining approach. We analyzed 171 business reports on the full-scene intelligent service by the topic modeling using the Latent Dirichlet Allocation (LDA). The findings reveal three main clusters: value proposition, participants, and connection platform. This study presents a theoretical framework for a further exploratory case study and quantitative research on full-scene intelligent service. This study also helps small and medium-sized enterprises to explore and exploit value co-creation opportunities.

DRASTIC is a very simple and common model used for the assessment of groundwater to contamination. This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment. The Ohio Water Well Association (OWWA) developed DRASTIC model in 1987. Over the years, several modifications have been made in this model as per the need of the regional assessment of groundwater to contamination. This model has fixed weights for its parameters and fixed ratings for the sub-parameters under the main parameters. The weights and ratings of DRASTIC parameters were fixed on the basis of Delphi network technique, which is the best technique for the consensus-building of experts, but it lacks scientific explanations. Over the years, several optimization techniques have been used to optimize these weights and ratings. This work intends to present a critical analysis of decision optimization techniques used to get the optimum values of weights and ratings. The inherent pros and cons and the optimization challenges associated with these techniques have also been discussed. The finding of this study is that the application of MCDA optimization techniques used to optimize the weights and ratings of DRASTIC model to assess the vulnerability of groundwater depend on the availability of hydrogeological data, the pilot study area and the level of required accuracy for earmarking the vulnerable regions. It is recommended that one must choose the appropriate MCDA technique for the particular region because unnecessary complex structure for optimization process takes more time, efforts, resources, and implementation costs.

Weather forecasting is an essential component of different hydrological studies. This article compares the weather prediction performance of various machine learning models like k-nearest neighbours (KNN), Soil and Water Assessment Tools (SWAT), and Representative Concentration Pathway (RCP). KNN is more resistant to noisy data set and provides more reliable performance than RCP and SWAT models. We simulate temperature, precipitation, and wind speed using KNN, SWAT and RCP weather generators, and we compare the results with observed data. The analyses compare WP-KNN with state-of-the-art classification and prediction models. We also suggest a systematic forecasting methodology that uses an updated version of the KNN classification. Our extensive experimental modelling findings show that the proposed technique is much more effective in a noisy dataset. • Generate synthetic weather sequences that could be used as inputs into hydrological models. • The predictions are performed by constructing relevant Machine Learning models based on stochastic process concepts. • Simulate weather data using three models: KNN, SWAT and, CCSM-RCP. • The generated weather data were then validated against observed data. • An RMSE criterion based on a confidence interval of 95% was used to determine which model was most efficient.

In recent years, when planning and determining a travel destination, residents often make the best of Internet techniques to access extensive travel information. Search engines undeniably reveal visitors' real-time preferences when planning to visit a destination. More and more researchers have adopted tourism-related search engine data in the field of tourism prediction. However, few studies use search engine data to conduct cluster analysis to identify residents' choice toward a tourism destination. In the present study, 146 keywords related to “Beijing tourism” are obtained from Baidu index and principal component analysis (PCA) is applied to reduce the dimensionality of keywords obtained by Baidu index. Modified affinity propagation (MAP) clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing. The result shows that residents in Hebei province are most likely to travel to Beijing. The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means, linkage, and Affinity Propogation (AP) in terms of silhouette coefficient and Calinski–Harabaz index. We also distinguish the difference of residents’ choice to travel to Beijing during the peak tourist season and off-season. The residents of Tianjing are inclined to travel to Beijing during the peak tourist season. The residents of Guangdong, Hebei, Henan, Jiangsu, Liaoning, Shanghai, Shandong, and Zhejiang have high attention to travel to Beijing during both seasons.