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.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

Abstract: In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,†for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

CRY — An improved crop yield prediction model using bee hive clustering approach for agricultural data sets

Abstract: Agricultural researchers over the world insist on the need for an efficient mechanism to predict and improve the crop growth. The need for an integrated crop growth control with accurate predictive yield management methodology is highly felt among farming community. The complexity of predicting the crop yield is highly due to multi dimensional variable metrics and unavailability of predictive modeling approach, which leads to loss in crop yield. This research paper suggests a crop yield prediction model (CRY) which works on an adaptive cluster approach over dynamically updated historical crop data set to predict the crop yield and improve the decision making in precision agriculture. CRY uses bee hive modeling approach to analyze and classify the crop based on crop growth pattern, yield. CRY classified dataset had been tested using Clementine over existing crop domain knowledge. The results and performance shows comparison of CRY over with other cluster approaches.

Cluster Based Multipath Dynamic Routing (CBDR) Protocol for Wireless Sensor Networks

Abstract: QoS of WSN routing protocols are measured in terms of energy-efficiency, end-to-end delay and packet delivery ratio Multi-path routing provides an easy mechanism to distribute traffic, balance networks load and fault tolerance However disadvantage of employing multipath routing is delay in path switching and every node has to maintain information of every other node and has to update the whole information periodically which consumes lot of energy So to overcome this drawback we employ clustering mechanism which divides the entire network in to clusters and multipaths are restricted to these clusters by which traffic will be distributed only to the cluster without propagating entire network and does not cause delay, energy wastage and increases delivery ratio between nodes Performance is compared between proposed protocol and EQSR protocol by simulating in NS2

Electron beam characteristics at extended source-to-surface distances for irregular cut-outs.

Abstract: Electron beam therapy is widely used in the management of cancers. The rapid dose fall-off and the short range of an electron beam enable the treatment of lesions close to the surface, while sparing the underlying tissues. In an extended source-to-surface (SSD) treatment with irregular field sizes defined by cerrobend cutouts, underdosage of the lateral tissue may occur due to reduced beam flatness and uniformity. To study the changes in the beam characteristics, the depth dose, beam profile, and isodose distributions were measured at different SSDs for regular 10 Χ 10 cm 2 and 15 Χ 15 cm 2 cone, and for irregular cutouts of field size 6.5 Χ 9 cm 2 and 11.5 Χ 15 cm 2 for beam energies ranging from 6 to 20 MeV. The PDD, beam flatness, symmetry and uniformity index were compared. For lower energy (6 MeV), there was no change in the depth of maximum dose (R100) as SSD increased, but for higher energy (20 MeV), the R100 depth increased from 2 cm to 3 cm as SSD increased. This shows that as SSD increases there is an increase in the depth of the maximum dose for higher energy beams. There is a +7 mm shift in the R100 depth when compared with regular and irregular field sizes. The symmetry was found to be within limits for all the field sizes as the treatment distance extended as per International Electro technical Commision (IEC) protocol. There was a loss of beam flatness for irregular fields and it was more pronounced for lower energies as compared with higher energies, so that the clinically useful isodose level (80% and 90%) width decreases with increase in SSD. This suggests that target coverage at extended SSD with irregular cut-outs may be inadequate unless relatively large fields are used.

Data mining approach for subscription-fraud detection in telecommunication sector

Abstract: This paper implements a probability based method for fraud detection in telecommunication sector. We used Naïve-Bayesian classification to calculate the probability and an adapted version of KL-divergence to identify the fraudulent customers on the basis of subscription. Each user’s data corresponds to one record in the database. Since, the data involves continuous numerical values, the NaïveBayesian classification for continuous values is used. This methodology overcomes the problem of existing system, which classifies the best customer as fraudulent customers, as it works on a threshold based method.

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.