SreeRam Nimmagadda

Bio: SreeRam Nimmagadda is an academic researcher from K L University. The author has contributed to research in topics: Crash & Range (aeronautics). The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Predicting Airline Crash due to Birds Strike Using Machine Learning

Abstract: The objective of this proposed work is to predict whether the airline crash has occurred due to a bird strike or not by using data mining techniques. Risk and safety are not always guaranteed within the field of aircraft. Bird strikes are dangerous for aircraft due to the relative speed of the plane with reference to the bird. The characteristics of aircraft damage from bird strikes, which is critical enough to make a high risk to continue a safe flight, differs in step with the dimensions of aircraft. Data from the National Transportation Safety Board (NTSB), which records all the aircraft accidents, are used as a training data set for the proposed system. Machine learning is the most effective technology to harnessing the useful information and knowledge from big data. The proposed work intended at building a prediction model using machine learning techniques such as decision trees and Bayesian classifications, which can be very useful in the aviation safety system and is utilized to conjecture the air crafts mishaps ahead of time so that there is an extension to the reduction in aircraft crash rate. The prediction results are range between 80% to 90%. The proposed aircraft crash prediction model is also assessed by using synthetic data sets.

Design of a Bdp Tool Using Data Mining Techniques

Abstract: Now a day’s most of the financial organizations facing a major problem to recover the money from the borrowers, it becomes the frightening to banks in some situations. As a financial intermediary, one of its roles is to reduce lending risks. Bank lending is an art as well as a science. Success depends on techniques used, knowledge and on an aptitude to assess both credit-worthiness of a potential borrower and the merits of the proposition to be financed. In recent years, banks have increasingly used credit-scoring techniques to evaluate the loan applications they receive from consumers financial institutions always utilized the rules or principles built by the analysts to decide whom to give credit. In order to overcome these difficulties while recovering money the financial institutions and researchers have been developed various credit scoring models but they many not exactly fix in the situation like predicting the borrower attitude. Even though they are following rules and principles while lending money, they are unable to recover the loans from all the borrowers. In order to overcome these types of potential problems, as a precautionary measure, a software tool can be developed using Data Mining techniques aiming at giving qualitative and useful guidelines to the financial institutions while making the decision of money lending. This proposed work is aimed at designing a software tool to facilitate the effective money lending process by automating the prediction of customer attitude towards the money management and automation of decision making process. Key words: money lending, customer attitude, software tool, automation of decision making, data mining techniques

Three novel bird strike likelihood modelling techniques: The case of Brisbane Airport, Australia

Abstract: The risk posed by wildlife to air transportation is of great concern worldwide. In Australia alone, 17,336 bird-strike incidents and 401 animal-strike incidents were reported to the Air Transport Safety Board (ATSB) in the period 2010-2019. Moreover, when collisions do occur, the impact can be catastrophic (loss of life, loss of aircraft) and involve significant cost to the affected airline and airport operator (estimated at globally US$1.2 billion per year). On the other side of the coin, civil aviation, and airport operations have significantly affected bird populations. There has been an increasing number of bird strikes, generally fatal to individual birds involved, reported worldwide (annual average of 12,219 reported strikes between 2008-2015 being nearly double the annual average of 6,702 strikes reported 2001-2007) (ICAO, 2018). Airport operations including construction of airport infrastructure, frequent take-offs and landings, airport noise and lights, and wildlife hazard management practices aimed at reducing risk of birdstrike, e.g., spraying to remove weeds and invertebrates, drainage, and even direct killing of individual hazard species, may result in habitat fragmentation, population decline, and rare bird extinction adjacent to airports (Kelly T, 2006; Zhao B, 2019; Steele WK, 2021). Nevertheless, there remains an imperative to continually improve wildlife hazard management methods and strategies so as to reduce the risk to aircraft and to bird populations. Current approved wildlife risk assessment techniques in Australia are limited to ranking of identified hazard species, i.e., are ‘static’ and, as such, do not provide a day-to-day risk/collision likelihood. The purpose of this study is to move towards a dynamic, evidence-based risk assessment model of wildlife hazards at airports. Ideally, such a model should be sufficiently sensitive and responsive to changing environmental conditions to be able to inform both short and longer term risk mitigation decisions. Challenges include the identification and quantification of contributory risk factors, and the selection and configuration of modelling technique(s) that meet the aforementioned requirements. In this article we focus on likelihood of bird strike and introduce three distinct, but complementary, assessment techniques, i.e., Algebraic, Bayesian, and Clustering (ABC) for measuring the likelihood of bird strike in the face of constantly changing environmental conditions. The ABC techniques are evaluated using environment and wildlife observations routinely collected by the Brisbane Airport Corporation (BAC) wildlife hazard management team. Results indicate that each of the techniques meet the requirements of providing dynamic, realistic collision risks in the face of changing environmental conditions.

An Adaptive algorithm to predict bad debts during COVID-19 Pandemic

Abstract: Loan recovery during the COVID-19 pandemic is anxious. Automated decision-making would boost the identification of bad debts while issuing loans. The objective of the proposed work is thus to design and implement an adaptive algorithm, which will be used to predict bad debts. Machine learning is an artificial intelligence technology, which gives systems the ability to automatically learn and improve from experience without explicit programming. The adaptive algorithm proposed is deterministic, uses two parameters known as neighborhood distance and minimum support threshold value for the risk profile, and can be very useful in predicting bad debts. It produces overlapped as well as non-overlapped clusters. This algorithm can detect the outliers with the help of an adaptive threshold value for the object's risk profile attribute. Objects with a moderately high or high value of risk profile attribute may emerge as outliers, and these outliers can be known as bad debts. The clusters generated are labeled as paid fully, not paid fully, and not paid. It can also generate clusters of different sizes. The proposed adaptive deterministic algorithm clusters the dataset without knowing the number of clusters. Many clusters are generated using this algorithm, but the parameter risk profile minimum threshold value prunes the clusters being formed. This proposed adaptive algorithm is tested using real and artificial data sets and shows 83% accuracy in bad debt prediction.