T. Arunkumar

Bio: T. Arunkumar is an academic researcher from VIT University. The author has contributed to research in topics: Optimized Link State Routing Protocol & Destination-Sequenced Distance Vector routing. The author has an hindex of 5, co-authored 21 publications receiving 99 citations. Previous affiliations of T. Arunkumar include Kidwai Memorial Institute of Oncology.

A Comprehensive Survey on IoT Technologies in Health Care System

Abstract: The present technology evolution of the traditional medical model toward the participatory medicine. It can boost the Internet of Things (IoT) paradigm involving sensors (environmental, wearable and implanted) spread inside domestic environments with the purpose to monitor the user's health and activate remote assistance. IoT can build deployment on things which can make them interact with each other and provide efficient service. Technically IoT is useful in processing full information,and tracking of the viable things also save time and cost. Nevertheless, IoT is well-off in transforming the lives of patients to new medical advancements. Therefore, the usage of IoT in healthcare has increased sharply. Example one can get the information to access, control and maintain the smart objects through the internet. Hence this comprehensive review discusses some of the recent technology used in healthcare domain.

Classification and prediction of routing nodes behavior in MANET using Fuzzy proximity relation and ordering with Bayesian classifier

Abstract: Mobile Ad-hoc Network (MANET) is a technology that has been developed for real-world applications. The routing information system of MANET can be said to be the back bone for routing process which also represents the characteristics or behaviours of routing nodes. The performance of MANET can be improved if the routing is done based on nodes routing behaviours. Thus, classification and prediction of routing nodes behaviour could lead to proper data analysis and decision making through which an effective routing model can be developed. Association among the routing attributes could help us to predict the behaviour of routing nodes based on the similarity but it is also possible that some of the associations may be hidden due to uncertain behaviour of the routing nodes. Absence of unseen associations may possess some necessary information which should not be ignored when we build an effective routing model. Keeping this in view, we have proposed a prediction model based on the Bayesian approach with fuzzy proximity relation and ordering to predict the link-failure and hidden associations using routing information system of MANET. Since the values in the routing information system are almost identical, we have considered the almost indiscernibility relation to characterize the routing nodes based on fuzzy proximity relation. This result induces the almost equivalence class of routing nodes. On imposing order relation on this equivalence class, we have obtained ordered categorical classes of routing nodes through which we can compute the link-failure possibilities of each routing node. Finally, we use the Bayesian approach to predict the hidden associations of routing attributes which can provide useful information to build an effective routing model for MANET.

A novel Cp-Tree-based co-located classifier for big data analysis

Abstract: The processing capacity, architecture and algorithms of traditional database system are not coping with big data analysis. Big data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this paper is proposing novel co-located classifier to handle complex spatial landslide big data. Co-located classification primarily aims at predicting the class labels of the unknown data from the class co-located rules. The main focus is on building a co-located classifier which utilises Cp-Tree algorithm for co-located rule generation to analyse landslide data. The performance of proposed classifier is validated and compared with various data mining classifier.

A Mobile Application Based Smart System for Supporting Yoga Activities and Health Monitoring

Abstract: ‘Health is wealth’ which is now changed to ‘Losing health for gaining wealth’ in the modern society. People are having busy schedules and they are not concerned about their health. Studies shows that a lion share of people all over the world undergoes mental stress due to the circumstances and pressures from both family and office. This mental stress factors can physically and mentally deteriorate the creativity and productivity of a person. Yoga can be considered as one of the finest solutions in this case though it refreshes both physically and mentally. Yoga transforms a person balanced with mental, physical and spiritual elements in the right composition. In this technology era, it will be good to integrate yoga with the trends of technology. This work proposes a scheme of integrating and monitoring yoga activities using the concept of Internet of Things (IoT) and a smart application. The body sensors (Pressure, temperature, humidity) attached with the person doing yoga senses relevant data and is processed using a central processor (Smart phone or smart devices) to provide necessary suggestions or feed backs to the user. This work will provide a platform to the yoga practice person to monitor and review their yoga activities by themselves. Our research results claims that the various parameters like blood pressure and heart beat rate is improvised a lot after practicing yoga and our system is much helpful for the yoga persons to practice yoga in an effective manner.

Big data and disaster management: a systematic review and agenda for future research

Abstract: The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

A study on various data mining techniques for crop yield prediction

Abstract: India is a country where agriculture and agriculture related industries are the major source of living for the people Agriculture is a major source of economy of the country It is also one of the country which suffer from major natural calamities like drought or flood which damages the crop This leads to huge financial loss for the farmers thus leading to the suicide Predicting the crop yield well in advance prior to its harvest can help the farmers and Government organizations to make appropriate planning like storing, selling, fixing minimum support price, importing/exporting etc Predicting a crop well in advance requires a systematic study of huge data coming from various variables like soil quality, pH, EC, N, P, K etc As Prediction of crop deals with large set of database thus making this prediction system a perfect candidate for application of data mining Through data mining we extract the knowledge from the huge size of data This paper presents the study about the various data mining techniques used for predicting the crop yield The success of any crop yield prediction system heavily relies on how accurately the features have been extracted and how appropriately classifiers have been employed This paper summarizes the results obtained by various algorithms which are being used by various authors for crop yield prediction, with their accuracy and recommendation

Application of bee colony algorithm for FANET routing

Abstract: FANET are wireless ad hoc networks on unmanned aerial vehicles, and are characterized by high nodes mobility, dynamically changing topology and movement in 3D-space. FANET routing is an extremely complicated problem. The article describes the bee algorithm and the routing process based on the mentioned algorithm in ad hoc networks. The classification of FANET routing methods is given. The overview of the routing protocols based on the bee colony algorithms is provided. Owing to the experimental analysis, bio-inspired algorithms based on the bee colony were proved to show good results, having better efficiency than traditional FANET routing algorithms in most cases.