Supervised Classification of Remote Sensed Data using Support Vector Machine

Support vector machines have been used as a classification method in various domains including and not restricted to species distribution and land cover detection Support vector machines offer many key advantages like its capacity to handle huge feature spaces and its flexibility in selecting a similarity function In this paper the support vector machine classification method is applied to remote sensed data Two different formats of remote sensed data is considered for the same The first format is a comma separated value format wherein a classification model is developed to predict whether a specific bird species belongs to Darjeeling area or any other region The second format used is raster format which contains image of Andhra Pradesh state in India Support vector machine classification method is used herein to classify the raster image into categories One category represents land and the other water wherein green color is used to represent land and light blue color is used to represent water Later the classifier is evaluated using kappa statistics and accuracy parameter

Impact of Cloud Computing Announcements on Firm Valuation

With increasing demand for Cloud Computing services, a growing number of firms are citing business agility and costsavings as motivators for adopting Cloud Computing services. Extant literature does not provide any empirical evidence ofvalue of announcements made regarding the Cloud Computing environment. This paper examines impact of CloudComputing announcements on firm valuation, using event study methodology. This study explores the market impact ofadoption of Cloud Computing on the cloud vendors/providers and customers/adopters. The impact on firm value of thecompetitors, of the companies adopting Cloud Computing services, is also analyzed. The study shows that there is asignificant impact of those announcements on the firm value of the companies. However, it shows a contrasting impact on thecustomers, vendors and their respective competitors, when analyzed separately

Computer-aided financial fraud detection: Promise and applicability in monitoring financial transaction fraud

Anti-money Laundering (AML) and Financial Fraud Detection (FFD) have been receiving increasing attention in the past few years, especially in light of the global financial crisis. Closer systems integration and a number of latest steep technological developments in areas like Big Data; High Frequency Trading; e-payments; and mobile payment systems, to name a few, are now promising enhanced risk management through superior decision support for the global financial industry. At the same time, however, resident regulatory frameworks, national and international, appear to lack the connectivity and flexibility required to support integrated AML and FFD approaches. This is strongly testified by the disparate technological approaches to FFD across different Financial Institutions and their reluctance to share practice within the industry. Focusing on Financial Transaction Fraud, this paper draws on the authors’ past research work which presented a prototype system that uses a workflow approach to identify abnormal financial transactions and applies Artificial Intelligence for classification. That work has shown successful applicability at short scale experiments, limited by the wide concern that information sharing should be achieved within the broader sector in order to achieve improved results. Drawing from there, this paper proposes that extending that approach across transaction infrastructure will deliver higher quality intelligent monitoring against Financial Transaction Fraud. Following from that, we argue that the necessary technological maturity does exist to support full-scale operable FFD systems working on large disparate datasets. We then discuss the evidence in favour of the view that such systems can only be realised in the presence of wider regulatory consensus. There is, therefore, the need for a framework within which the technical infrastructure, business architecture and regulatory rules will harness that technological capability to deliver superior fraud prevention. The paper first reviews computer-aided techniques and approaches for FFD available to the financial sector and discusses the business value of their application. It then addresses the main impediments for their full-scale applicability and uses an analytical framework for assessing their significance, in technological, business-specific and regulatory terms. A brief account of the authors’ workflow-based approach is then provided and its capabilities are outlined

Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes

Detecting fraud in financial control processes poses significant challenges due to the complex nature of financial transactions and the evolving tactics employed by fraudsters. This paper investigates the integration of data mining techniques, specifically the combination of Benford's Law and machine learning algorithms, to create an enhanced framework for fraud detection. The paper highlights the importance of combating fraudulent activities and the potential of data mining techniques to bolster detection efforts. The literature review explores existing methodologies and their limitations, emphasizing the suitability of Benford's Law for fraud detection. However, shortcomings in practical implementation necessitate improvements for its effective utilization in financial control. Consequently, the article proposes a methodology that combines informative statistical features revealed by Benford’s law tests and subsequent clustering to overcome its limitations. The results present findings from a financial audit conducted on a road-construction company, showcasing representations of primary, advanced, and associated Benford’s law tests. Additionally, by applying clustering techniques, a distinct class of suspicious transactions is successfully identified, highlighting the efficacy of the integrated approach. This class represents only a small proportion of the entire sample, thereby significantly reducing the labor costs of specialists for manual audit of transactions. In conclusion, this paper underscores the comprehensive understanding that can be achieved through the integration of Benford's Law and other data mining techniques in fraud detection, emphasizing their potential to automate and scale fraud detection efforts in financial control processes

An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection

Today illegal activities regarding online financial transactions have become increasingly complex and borderless, resulting in huge financial losses for both sides, customers and organizations. Many techniques have been proposed to fraud prevention and detection in the online environment. However, all of these techniques besides having the same goal of identifying and combating fraudulent online transactions, they come with their own characteristics, advantages and disadvantages. In this context, this paper reviews the existing research done in fraud detection with the aim of identifying algorithms used and analyze each of these algorithms based on certain criteria. To analyze the research studies in the field of fraud detection, the systematic quantitative literature review methodology was applied. Based on the most called machine-learning algorithms in scientific articles and their characteristics, a hierarchical typology is made. Therefore, our paper highlights, in a new way, the most suitable techniques for detecting fraud by combining three selection criteria: accuracy, coverage and costs

A Novel RFID Authentication Protocol based on Elliptic Curve Cryptosystem

Recently, many researchers have proposed RFID authentication protocols. These protocols are mainly consists of two types: symmetric key based and asymmetric key based. The symmetric key based systems usually have some weaknesses such as suffering brute force, de-synchronization, impersonation, and tracing attacks. In addition, the asymmetric key based systems usually suffer from impersonation, man-in-the-middle, physical, and tracing attacks. To get rid of those weaknesses and reduce the system workload, we adopt elliptic curve cryptosystem (ECC) to construct an asymmetric key based RFID authentication system. Our scheme needs only two passes and can resist various kinds of attacks. It not only outperforms the other RFID schemes having the same security level but also is the most efficient

Applying Process-Oriented Data Science to Dentistry

Background: Healthcare services now often follow evidence-based principles, so technologies such as process and data mining will help inform their drive towards optimal service delivery. Process mining (PM) can help the monitoring and reporting of this service delivery, measure compliance with guidelines, and assess effectiveness. In this research, PM extracts information about clinical activity recorded in dental electronic health records (EHRs) converts this into process-models providing stakeholders with unique insights to the dental treatment process. This thesis addresses a gap in prior research by demonstrating how process analytics can enhance our understanding of these processes and the effects of changes in strategy and policy over time. It also emphasises the importance of a rigorous and documented methodological approach often missing from the published literature. Aim: Apply the emerging technology of PM to an oral health dataset, illustrating the value of the data in the dental repository, and demonstrating how it can be presented in a useful and actionable manner to address public health questions. A subsidiary aim is to present the methodology used in this research in a way that provides useful guidance to future applications of dental PM. Objectives: Review dental and healthcare PM literature establishing state-of-the-art. Evaluate existing PM methods and their applicability to this research’s dataset. Extend existing PM methods achieving the aims of this research. Apply PM methods to the research dataset addressing public health questions. Document and present this research’s methodology. Apply data-mining, PM, and data-visualisation to provide insights into the variable pathways leading to different outcomes. Identify the data needed for PM of a dental EHR. Identify challenges to PM of dental EHR data. Methods: Extend existing PM methods to facilitate PM research in public health by detailing how data extracts from a dental EHR can be effectively managed, prepared, and used for PM. Use existing dental EHR and PM standards to generate a data reference model for effective PM. Develop a data-quality management framework. Results: Comparing the outputs of PM to established care-pathways showed that the dataset facilitated generation of high-level pathways but was less suitable for detailed guidelines. Used PM to identify the care pathway preceding a dental extraction under general anaesthetic and provided unique insights into this and the effects of policy decisions around school dental screenings. Conclusions: Research showed that PM and data-mining techniques can be applied to dental EHR data leading to fresh insights about dental treatment processes. This emerging technology along with established data mining techniques, should provide valuable insights to policy makers such as principal and chief dental officers to inform care pathways and policy decisions