Papers by Aparna Krishna Bhat
International Journal of Information Technology and Management Information Systems (IJITMIS), 2025
Disaster recovery (DR) has evolved from basic tape backups to advanced AI-driven solutions that e... more Disaster recovery (DR) has evolved from basic tape backups to advanced AI-driven solutions that ensure business continuity in the digital age. This paper examines the historical evolution of Disaster Recovery (DR), compares traditional and modern strategies, and highlights cutting-edge technologies such as Artificial Intelligence (AI), cloud computing, blockchain, and quantum computing in DR planning. By examining the financial services, healthcare, and technology sectors, the study highlights the growing importance of DR in reducing cyber threats and enhancing operational resilience. The paper also examines key data recovery (DR) methodologies, including multi-layered security frameworks to counter ransomware attacks and quantumenhanced data recovery.
International Journal of Advance Computational Engineering and Networking (IJACEN), 2014
Acoustic echo cancellation is an essential signal enhancement tool in hands-free communication. A... more Acoustic echo cancellation is an essential signal enhancement tool in hands-free communication. Acoustic echo is caused due to leakage from the loudspeaker to the microphone in settings like hands free telephony. Nowadays, adaptive filtering techniques are generally being implemented to suppress this echo. This paper discusses various variable step-size NLMS based algorithms which can be implemented in acoustic echo cancelling applications. Simulation results using real audio recordings in a room demonstrate that GSER algorithm have the best performance in real time environment. The performance of these algorithms in terms of ERLE and NSEC curves are obtained and comparison between them is done. Also a simple Double-Talk Detection scheme is proposed in this paper.
International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), 2015
Abstract: The Human heart ECG is the most vital sign for analyzing an individual personal health.... more Abstract: The Human heart ECG is the most vital sign for analyzing an individual personal health. The cardiac activity monitoring systems plays a very important role in understanding such a vital sign. Developing a wireless version of such a monitoring system is our main intension. Our paper describes the wireless monitoring of cardiac signals through Smartphone using the devices dedicated for the signal acquisition and synthesis such as AD620. And an instrumentation amplifier designed around it to enhance its function.The Paper also explains the use of a suitable microcontroller to process the incoming signal to perform the signal transformation of an analog time varying signal to a digital equivalent transferable data. The same microcontroller is playing an important role in establishing wireless communication with the appropriate Smartphone.The Smartphone is the end user application where the observation on the ECG will be made to facilitate the understanding of the activity of t...
International Journal of Science and Research (IJSR), 2024
Financial fraud has been resulting in substantial losses, leading researchers and academics to ex... more Financial fraud has been resulting in substantial losses, leading researchers and academics to explore developing a rigorous
method for detecting and preventing such fraud. They are broadly classified into four different categories namely securities and
commodities fraud, bank fraud, insurance fraud and other financial fraud. Insurance fraud, however, is a serious and growing problem,
has received a lot of attention since a variety of fraudulent methods result in significant losses for insurance firms and that traditional
approaches to tackling fraud are inadequate and has become increasingly complex as fraudsters adapt to new technologies and strategies.
Research on insurance fraud has traditionally concentrated on identifying attributes of fraudulent claims and claimants. This emphasis
is evident in the latest advancements in forensic and data analysis technologies for detecting fraudulent activities. An alternative method
involves optimizing and subsequently enhancing current procedures in the detection of fraudulent activities. Artificial Intelligence (AI)
is emerging as a powerful tool in mitigating fraud risks by identifying patterns and behaviors that may indicate fraudulent activity. This
paper explores the role of AI in early fraud detection during the intake phase of policy underwriting and the claims processing stage.
Additionally, it addresses a more insidious form of fraud involving agents who engage in internal policy manipulation to trick carriers
into paying for the same policies multiple times. The paper also highlights AI - driven strategies for combating these fraud risks and
suggests best practices for insurers seeking to deploy AI in their fraud detection efforts.
International Journal of Science and Research (IJSR), 2024
Fraud detection refers to measures put in place to prevent criminals from obtaining monetary bene... more Fraud detection refers to measures put in place to prevent criminals from obtaining monetary benefits through false claims. In the world of online commerce, scams, scams and malicious agents are harmful in many ways. Businesses should take steps to ensure that fraud is detected and stopped before it affects the business. Fraud prevention refers to the countermeasures in place to mitigate the impact that fraudsters can have on business operations, once detected. Fraud detection is the first step in identifying where the risk lies. Real-time fraud detection improves on-site management of fraudulent activities and channels that could otherwise lead to negative business outcomes. As fintech and e-commerce thrive, more and more bank payments and money transfers are facilitated through online channels, which are faster, more convenient and safer for health in the age of the coronavirus. Real-time fraud detection and prevention can be accomplished using fraud detection software, RiskOps tools, DataOps, and other risk management strategies that improve data usability. Fraud detection on elastic platforms like elastic search has the ability to detect information in real time through predefined standards and approaches that provide alerts when communication is imminent. However, the fraud detection approach works with a significant commitment to ensure consistent compliance with privacy rules, such as anonymization, which ensures that personally identifiable information is not used for malicious purposes. Therefore, the act of fraud detection requires instrumental data processing mechanisms and relies on the scalability and flexibility of elastic platforms to achieve a scalable operation.
International Journal of Scientific Research in Science, Engineering and Technology, 2024
AI in finance refers to the application of AI techniques in financial businesses. With the prolif... more AI in finance refers to the application of AI techniques in financial businesses. With the proliferation of AI-based tools and algorithms in financial decision-making, it is increasingly necessary to assess the impact of these technologies on the investment strategies and results of individual investors. The integration of artificial intelligence (AI) in financial decision-making heralds a technological revolution in the sector, which offers enormous potential benefits and significant challenges. This review aims to unravel the complexity surrounding AI in finance, focusing on identifying and addressing barriers to its effective implementation. Looking ahead, the article anticipates future trends and challenges in AIdriven finance, urging stakeholders to collaborate for sustainable innovation. Overall, AI offers tremendous potential for financial transformation, but careful consideration of ethical and regulatory issues is essential for long-term success.
2015 IEEE International Conference on Engineering and Technology (ICETECH’15), 2015
Renewable energy sources also called nonconventional energy are sources that are continuously rep... more Renewable energy sources also called nonconventional energy are sources that are continuously replenished by natural processes. For example, solar energy, wind energy, bio-energy -bio-fuels grown sustain ably), hydropower etc., are some of the examples of renewable energy sources. A renewable energy system converts the energy found in sunlight, wind, falling-water, sea-waves, geothermal heat, or biomass into a form, we can use such as heat or electricity. Most of the renewable energy is naturally available resource which is linked to sun directly or indirectly and wind and can never be exhausted, and therefore they are called renewable. This paper throws light on natural and renewable energy scenario of India. This paper puts forward existing status, acquired achievements and future aspects of renewable energy in India and the implementation of the renewable for the future is also been presented.
2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015
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Papers by Aparna Krishna Bhat
method for detecting and preventing such fraud. They are broadly classified into four different categories namely securities and
commodities fraud, bank fraud, insurance fraud and other financial fraud. Insurance fraud, however, is a serious and growing problem,
has received a lot of attention since a variety of fraudulent methods result in significant losses for insurance firms and that traditional
approaches to tackling fraud are inadequate and has become increasingly complex as fraudsters adapt to new technologies and strategies.
Research on insurance fraud has traditionally concentrated on identifying attributes of fraudulent claims and claimants. This emphasis
is evident in the latest advancements in forensic and data analysis technologies for detecting fraudulent activities. An alternative method
involves optimizing and subsequently enhancing current procedures in the detection of fraudulent activities. Artificial Intelligence (AI)
is emerging as a powerful tool in mitigating fraud risks by identifying patterns and behaviors that may indicate fraudulent activity. This
paper explores the role of AI in early fraud detection during the intake phase of policy underwriting and the claims processing stage.
Additionally, it addresses a more insidious form of fraud involving agents who engage in internal policy manipulation to trick carriers
into paying for the same policies multiple times. The paper also highlights AI - driven strategies for combating these fraud risks and
suggests best practices for insurers seeking to deploy AI in their fraud detection efforts.
method for detecting and preventing such fraud. They are broadly classified into four different categories namely securities and
commodities fraud, bank fraud, insurance fraud and other financial fraud. Insurance fraud, however, is a serious and growing problem,
has received a lot of attention since a variety of fraudulent methods result in significant losses for insurance firms and that traditional
approaches to tackling fraud are inadequate and has become increasingly complex as fraudsters adapt to new technologies and strategies.
Research on insurance fraud has traditionally concentrated on identifying attributes of fraudulent claims and claimants. This emphasis
is evident in the latest advancements in forensic and data analysis technologies for detecting fraudulent activities. An alternative method
involves optimizing and subsequently enhancing current procedures in the detection of fraudulent activities. Artificial Intelligence (AI)
is emerging as a powerful tool in mitigating fraud risks by identifying patterns and behaviors that may indicate fraudulent activity. This
paper explores the role of AI in early fraud detection during the intake phase of policy underwriting and the claims processing stage.
Additionally, it addresses a more insidious form of fraud involving agents who engage in internal policy manipulation to trick carriers
into paying for the same policies multiple times. The paper also highlights AI - driven strategies for combating these fraud risks and
suggests best practices for insurers seeking to deploy AI in their fraud detection efforts.