Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advanced ML and DA techniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours.

Cricket is a worldwide popular game where different technologies are being used to help the match umpires to make decisions. Often due to the human perception, deciding whether a bowled delivery is a no-ball or legal ball which causes controversy. As only a single ball can change the fate of the game, so it is obvious to make accurate decision regarding no ball. In this paper, we have divided the bowling crease into two regions and applied image subtraction method on both regions to find the change in pixel values. Later, we have applied our proposed method on real world video frames. Since our proposed method to detect the overstep no ball is grounded on pixel by pixel image subtraction, it eliminates the inadequacy of human perception.

Clustering is a technique for dividing a set of similar objects into same groups and dissimilar objects into different groups. Among different clustering algorithms, the K-means algorithm is considered as the most popular due to its simplicity. However, the outcome from the K-means algorithm is highly sensitive to the initial centroid selection. As a consequence, the selection of initial centroids in the K-means algorithm plays a crucial part in accuracy and efficiency. To select the initial centroids more effectively, in this paper, we propose a new method based on radial and angular coordinates. To check the feasibility of the proposed method, we compare our method with the standard K-means algorithm. For the comparison, we use synthetic data sets with different size of instances and number of clusters. The experiment shows that in most of the cases the proposed method clearly dominates over the standard K-means algorithm in terms of execution time and required number of iterations.

Recommender systems assist customers to make decisions; however, the modest adoption of digital technology in the water industry means no such system exists for household water users. Such a system for the water industry would suggest to consumers the most effective ways to conserve water based on their historical data from smart water meters. The advantage for water utilities in metropolitan areas is in managing demand, such as low pressure during peak hours or water shortages during drought. For customers, effective recommendations could save them money. This paper presents a novel vision of a recommender system prototype and discusses the benefits both for the consumers and the water utility companies. The success of this type of system would depend on the ability to anticipate the time of the next major water use so as to make useful, timely recommendations. Hence, the prototype is based on a long short-term memory (LSTM) neural network that predicts significant water consumption events (i.e., showers, baths, irrigation, etc.) for 83 households. The preliminary results show that LSTM is a useful method of prediction with an average root mean square error (RMSE) of 0.403. The analysis also provides indications of the scope of further research required for developing a commercially successful recommender system.

Prioritizing requirements before each iteration is a crucial part in the software development. The success of each internal or external release highly depends on properly prioritized requirements. However, requirements prioritization is considered as one of the most challenging phases in software development life cycle (SDLC) due to the absence of a simple, highly customizable, and time savings prioritization technique. Furthermore, as the existing prioritization techniques were not designed to handle issue starvation situation, so a new prioritization technique is required that can handle the issue starvation situation effectively and overcome the limitations of the existing techniques. In this paper, we have proposed a new requirements prioritization technique that is easy to use and understand, highly customizable, scalable, saves time and can handle the issue starvation scenario. Furthermore, we have shown the effectiveness of our proposed approach using a fictional scenario.