Online Doctor Appointment App
Authors: G Vamsi Krishna, K Akhila, V Lakshmi, Ch Samyuktha, Ch Navya Sri
Abstract
Long waiting times for registration to see a doctor is problematic in India, especially in tertiary hospitals. To address this issue, a web-based appointment system, we developed a mobile app for hospital. The aim of this study was to investigate the efficacy of the mobile based appointment system in the registration service for outpatients
Dynamic Pricing for Revenue Maximization in Mobile Social Data Market with Network Effects
Authors: V Divya Vani, Ch Prathyusha, N Taraka Ram, R Sharadha, K Nava Kishore
Abstract
Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods sequentially and repeatedly. The proposed scheme exploits the network effects in the mobile users’ behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator offers the data price simultaneously. We analytically demonstrate that the proposed dynamic pricing scheme can help the operator gain greater revenue and users achieve higher total utilities than those of the baseline static pricing scheme. We construct the social graph using Erd˝os- R´enyi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator
Cloud Computing Approaches for Information Retrieval
Authors: A.Ramesh, Polavarapu Bhargavi, Kancharla Manasa, Ramisetty sowmya, Dondapati sai krishna
Abstract
One of the greatest challenges of an enterprise’s service center is to ensure that their engineers and customers are provided with the right information in a timely fashion. For this purpose, modern organizations operate a wide range of information support systems to assist customers with critical service requests and to provide proactive monitoring, where possible, to prevent service requests from occurring in the first place. It is often the case that relevant information is scattered over the Internet and/or maintained on disparate systems, buried in large amount of noisy data, and in heterogeneous formats, thereby complicating the access to reusable knowledge and extending the response time to reach a resolution. To address these challenges, in this paper we propose an effective knowledge mining solution to improve the quality of service request resolution. We model the service resolution problem as an online search and classification problem, and use domain knowledge in the form of ontology to guide effective machine learning. Our proposed solution has been extensively evaluated with experiments and has been used in a real enterprise customer center