One of the main goals of customer relationship management is to reduce or eliminate “customer churn”, i.e. loss of existing customers. This paper introduces a prototype algorithm to estimate a continuously updated indicator of the probability of an existing customer to cease purchasing from a subscription commerce business. The investigation is focused on the case of repeat consumers of subscription commerce products which require regular replacement or replenishment. The motivation is to help marketers to make targeted proactive retention actions by categorizing regular customers into groups of similar estimated churn risk. The proposed algorithm re-computes the probability of churn for each customer at regular intervals using past purchase transaction data and incorporating subscription-based business logic. We describe the detailed process from data collection and feature engineering to the algorithm’s design. We also present evaluation results of the algorithm’s performance based on a pilot test that took place on a consumables e-commerce business. The results suggest a significant capability of the proposed algorithm in capturing the purchasing intentions of repeat customers, regardless of the risk group they belong to.

Human resource is valuable asset for an agency. The success of an institution is not only determined by the quality of its human resources, but also by the level of discipline. The discipline of an employee in an institution can be seen and measured by the level of attendance in doing a job, because the level of attendance is one of the factors that determine productivity. The current problem is the management level of the company that has difficulty in monitoring and controlling the employee attendance data. There needs to be a mapping and grouping to find out patterns of absence. Mapping or patterns that are obtained help management levels to monitor employees, take approaches and take action so as to improve employee discipline. In this study, it was used descriptive modeling with the implementation of the k-means clustering method. The results of the mapping obtained help the management level in controlling and monitoring as a reference for the next policy maker.

Inappropriate placement of methods causes Feature Envy (FE) code smell and makes classes coupled with each other. To achieve cohesion among classes, FE code smell can be removed using automated Move Method Refactoring (MMR) suggestions. However, challenges arise when existing techniques provide multiple MMR suggestions for a single FE instance. The developers need to manually find an appropriate target classes for applying MMR as an FE instance cannot be moved to multiple classes. In this paper, a technique is proposed named MultiMMRSReducer, to reduce multiple MMR suggestions by considering the Total Call-Frequencies of Distinct Entities (TCFDE). Experimental results show that TCFDE can reduce the multiple MMR suggestions of an FE instance and performs 77.92% better than an existing approach, namely, JDeodorant. Moreover, it can ensure minimum future changes in the dependent classes of an FE instance.

In this paper, the importance of blockchain technology have been discussed and the generations of blockchain (Bitcoin and Ethereum) have been compared provided different aspects. The blockchain is a technology which allows direct transaction without involving third party. Also, it offers many facilities like high translucency, high safety and security, improved trace-ability, greater proficient and transactions’ speed, and reduced costs. Moreover, the cryptocurrencies provide advance security level. The basic purpose of this study is to highlight different aspects of Blockchain, Bitcoin and Ethereum and to show which cryptocurrency is better approach. The research contributes to show the impact of this technology in different fields and a comparison of bitcoin and ethereum is presented to analyze and furnish a decision regarding the best among them.
The use of blochchain technology in government applications can bring a drastic change in the world because it is safer and faster. Also, the comparison shows that ethereum is better than bitcoin as it is efficient and has more applications as compared to bitcoin. It offers more advanced services such as smart contracts. All in all, the analysis has concluded with Ethereum as faster and securer approach.

Mobile telenursing is an emerging sub-field of mobile health that proposes remote nursing care provision with the aim of reducing stress on nurses by enabling them to spend more time on direct patient care rather than indirect care. Most existing mobile telenursing systems are limited to a simple phone call and Short Message Service (SMS) alert thereby rendering them inadequate to support indirect patient cares such as remote prescription based on consultant’s advice and monitoring of drug usage adherence. Hence, this research developed a Mobile Telenursing and Drug Administration System (MTS) characterized by support for the aforementioned indirect patient cares. Domain-Driven Design was employed in designing MTS client-server architecture and its framework. The framework was implemented by developing an SMS reminder system and client mobile application which maintains real-time communication with the webserver. The MTS, which supports real-time consultation between nurse-patient and nurse-consultant, was developed for mobile devices running the Android operating system using the framework implemented. The MTS was programmed using JavaScript and Pusher real-time messaging library of the Android development Kits. The performance of the MTS was evaluated through users’ assessment by administering a set of questionnaire on purposively selected 64 nurses; 53 patients chosen from four (4) purposively selected hospitals (General Hospital, New Bussa; Bowen University Teaching Hospital, Ogbomoso; Alimosho General Hospital, Lagos and Ifako-Ijaiye General Hospital, Lagos). The responses obtained from the questionnaires were statistically evaluated to determine MTS suitability for drug usage monitoring, remote prescription, palliative care, injection administration, patients’ clean-up and follow-up treatment using a correlation test. Also, mean effectiveness and mean acceptability of MTS were evaluated using a t-test at 0.05 level of significance. The MTS was deployed on mobile devices running the Android operating system. The results of performance evaluation revealed that MTS recorded suitability values of drug usage monitoring (r = 0.656), remote prescription (r = 0.829), palliative care (r = 0.925), injection administration (r = -0.772), patients’ clean-up (r = -0.841) and follow-up treatment (r = 0.868) of patients at (p < 0.05). Similarly, MTS recorded mean effectiveness and acceptability values of 3.596 and 3.32, 3.770 and 3.36 at (p < 0.05) for nurses and patients, respectively. The research developed MTS which is suitable for the provisioning of remote nursing care and drug administration without breaking basic medical ethics. The system can be adopted for mobile telenursing and drug administration system.