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📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

October-December 2024

Volume 1, Issue 1 - $2024Current Issue

Volume 1 Issue 1 Cover

Issue Details:

Volume 1 Issue 1
Published:Invalid Date

Editorial: October-December 2024

Welcome to the 2024 issue of Journal of Artificial Intelligence and Big Data Disciplines (JAIBDD). This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

Dr. Aaluri Seenu
Editor-in-Chief
Journal of Artificial Intelligence and Big Data Disciplines (JAIBDD)

Articles in This Issue

Showing 16 of 16 articles
Research PaperID: jaibdd110019

Advancing Explainable AI for AI-Driven Security and Compliance in Financial Transactions

Phanish Lakkarasu

Explainable AI (XAI) has been delivering ground-breaking results in various domains. Emerging in parallel with the rise of powerful machine learning models, how to extend explainability to those black-box systems and promote its integrality have evolved into a blooming research field. Financial services are among the first to highlight the requirement for interpretable and fair algorithms, and the European Union has established the minimum regulatory and supervisory expectations for taking Transparency and Explainability of AI into national law. And XAI seems to be an inevitable future trend in anti-money laundering detection due to the booming applications of machine learning techniques. Thereat, a novel and all-round XAI-Prompted AI-Driven Security and Compliance Platform for Financial Transactions is proposed, providing AI decision uncertainty and traces, disclosing feature attributions, and automatically generating data analytic compliance documentation. A comprehensive comparison of manifold interpretation methods is also conducted to yield salient results, suggesting that a model-specific and post-hoc algorithm can prominently outperform others in this special financial domain. Moreover, adopting the innovative language model to automatically generate explanations of the prediction target is also explored successfully. Fargo is an interdisciplinary team, composed of researchers across computer science, machine learning, natural language processing, and financial regulation. They communicate and cooperate to make Fargo transparent and clearly documented its model, data, techniques, methodology and results. They receive a financial transaction that is not classified as suspicious or unusual.

explainable AIXAIfinancial servicesAI interpretabilityalgorithmic transparencyanti-money laundering+16 more
2,795 views
891 downloads
DOI:
10.70179/784ef287

Contributors:

 Phanish Lakkarasu
Research PaperID: jaibdd110004

Advancing Financial Decision-Making through Quantum Computing and Cloud-Based AI Models: A Comparative Analysis of Predictive Algorithms

Srinivas Naveen Dolu-Surabhi

Developing a quantum computing algorithm that outperforms its classical counterpart is widely viewed as a major milestone for the field. We achieve this milestone by offering an explicit, efficiently implementable algorithm that solves a fundamental problem in investing. One would want faster algorithms for better models at the same scale before worrying about learning the entire wealth distribution. We propose efficient quantum algorithms for both of these key subproblems.For example, quantum computers can efficiently reverse-engineer private shares to attain an accurate estimate of price-sensitive inside information. In recent years, the role of artificial intelligence has grown considerably in the operational decision-making process and, in particular, in the financial services industry. This paper takes advantage of the progress in quantum computing, addressing the problem of wealth distribution prediction in a big data set, a key problem in the deployment of trading strategies. Through a high-performance cloud computing architecture, we assess the impact of quantum computing technologies in comparison with the classical approach through several prediction models and analytical methodologies in both machine and deep learning.

quantum computingquantum algorithmsclassical algorithm comparisonfinancial serviceswealth distribution predictionbig data analytics+14 more
1,513 views
606 downloads
DOI:
10.70179/gbwxre90

Contributors:

 Srinivas Naveen Dolu-Surabhi
Research PaperID: jaibdd110038

Agentic AI in Retail Banking: Redefining Customer Service and Financial Decision-Making

Ramesh Inala, Bharath Somu

Artificial Intelligence (AI) has become one of the most dominant enablers of digital transformation across a significant number of industries in recent years. With the application of AI technology including natural language processing (NLP), creating human-like chat bots has become easier. Today, a greater portion of India banks have switched over to Chat Bot technology for their customer on-boarding, queries, complaints, fund transfers, etc., due their proficiency in handling numerous queries with high response time and round the clock service. This case study is based on the virtual assistant of State Bank of India (SBI) – State Bank Intelligent Assistant (SIA), which is engaged in providing personalized service to users based on their historical preferences, search frequency etc., via analyzing the data with the assistance of AI techniques. The recent developments and emergence of Virtual Banking in India and the current trends in the modern banking systems are explained along with the features of SBI-SIA virtual assistant. As banking is reshaped by technology, financial stability is a key priority for the Bank of England. The Bank is engaging with FinTech companies to gather information and understanding on the financial stability risks that might emerge from FinTech developments. Such risks are expected from the explosion of technology-enabled financial services. It has now become common for many banks to integrate FinTech, machine learning and AI into their services because customers want more choices, flexibility and control over their banking. AI is a branch of FinTech but not every FinTech is AI. AI is machine intelligence. Machine learning and AI are often treated as synonyms. AI in retail banking is at its nascent stage in the UK, though the potential is extraordinary. In the UK, some banks have launched banking applications using voice recognition. The Royal Bank of Scotland (RBS) has decided to roll out its “Luvo” AI customer service assistant, powered by its partner firm, RBS Group, wider in its branches. Bank of America, Capital One, Société Générale and Swedbank are some of the banks that have experimented with chatbots.

artificial intelligenceAI in bankingdigital transformationvirtual assistantschatbotsnatural language processing+17 more
3,669 views
1,014 downloads
DOI:
10.70179/97ntmv88

Contributors:

 Ramesh Inala
,
 Bharath Somu
Research PaperID: jaibdd110039

Co-Creation and Connectivity: The Role of Consumers in Digital Ecosystem Evolution

Srinivas Kalyan Yellanki

In this paper, we examine a pivotal phenomenon in contemporary consumer behavior: the evolution of digital ecosystems in which a multitude of consumers co-create value and innovatively interact with each other and firms. We analyze this phenomenon through the empirical investigation of national consumer co-creation networks in 11 European countries as part of a transition to a digital ecosystem in shareable citizenship. The paper contributes to the literature on platform ecosystems and their evolution by explicitly addressing how ecosystems evolve among consuming agents and highlighting the role of a novel form of connectivity (the notion of shareable citizenship) as a resource of consumer co-creation. Integrating insights from the literature on systems competition, ecosystem evolution, and consumer role theory, we develop a theoretical framework to interpret the empirical findings. The paper concludes by discussing practical implications and presenting ideas for future research. With the expansion of digital platforms throughout the economy, consumers are increasingly viewed as actively co-creating value and innovating within ecosystems of interacting agents. As highly networked actors, consumers not only enrich or co-create platforms with their contributions but also collectively co-create the rules and designs that shape the platform ecosystem. This growing role of consumers in the emergence and evolution of digital ecosystems has direct implications for the effective management of such ecosystems, not only by firms but also by consumers and institutions. Consumers interact with firms and other consumers and play a co-creation role, i.e. are involved in one or multiple aspects of value creation involving digital platforms. This research was motivated by the observation of how networks of European consumers emerged in recent years to collectively combat platformization and the excessive power of a few technology firms. Exploring the co-creation roles of consumers in the context of a nascent digital ecosystem led to the discovery of a new type of connectivity: network-level co-creation across a multitude of actors. Insights from this research may not only be of relevance to understand the co-creation of value among consumers but also more generally to understand how digital ecosystems emerge in the broader realm of interactions among government, business, and society.

digital ecosystemsconsumer co-creationplatform ecosystemsshareable citizenshipnetworked consumersvalue co-creation+14 more
3,629 views
1,132 downloads
DOI:
10.70179/pbe9g995

Contributors:

 Srinivas Kalyan Yellanki
Research PaperID: jaibdd110018

Deep Learning Architectures for Automated Fraud Detection in Payroll and Financial Management Services: Towards Safer Small Business Transactions

Jeevani Singireddy

Fraudulent payroll accounts are a significant issue in the financial and payroll services market. About 75 percent of employees have stolen from their employer once, 20 percent have stolen at least twice, and half of this figure has stolen at least three times or more. The increased use of direct deposit has made it easier to steal the identity of a small payroll customer. A fraudulent account is set up with apparently legitimate credentials, and the account’s data set is changed after the customer’s login authentication returns an all-is-ok status. The direct deposits are then quickly withdrawn. It’s an ongoing game of cat-and-mouse: after a bank improves or alters its fraud detection system, criminals quickly adapt to avoid it. Small-scale business contract fraud is accepted and paid as normal business expenses, and there is extremely little fraud detection technology in place to pick up the kickback schemes. This type of fraud can bring a company to its knees. (B) Automated fraud detection in payroll and financial management services harnesses sophisticated deep learning architectures. Computers do the heavy lifting for identifying patterns, obtaining insights, making decisions, and taking action. Unless large fraudulent datasets are already commercially available, the models are unable to understand or predict fraud when trained only on legitimate transaction data. Constructing artificially oversampled imbalanced data sets leads to flawed models. A self-training active learning ensemble stack of models using transaction authentication data is described. It directly leverages fraud patterns in a mostly-unlabeled data set and requires minimal retraining when a pattern changes. Model performance is measured with area under the ROC curve, and this method outperforms existing techniques.

fraudulent payroll accountspayroll fraudfinancial services fraudidentity theftdirect deposit fraudkickback schemes+16 more
2,780 views
884 downloads
DOI:
10.70179/kehvpn52

Contributors:

 Jeevani Singireddy
Research PaperID: jaibdd110023

Deep Learning Frameworks for Multi-Modal Data Fusion in Retail Supply Chains: Enhancing Forecast Accuracy and Agility

Srinivas Kalisetty, Phanish Lakkarasu

Traffic flow forecasting is a key problem of intelligent transport systems and represents a challenging task due to the spatial-temporal correlation features and long temporal interdependence of the considered data. Conventional methods deal with this either by spatial forecasting given observed counts at previous times or by temporal forecasting given observed traffic counts in neighbouring locations. In order to fully exploit the spatio-temporal properties observed in the data, a hybrid multimodal deep learning method for short-term traffic flow forecasting called HaMDeepT is proposed. Specifically, the HaMDeepT method can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data through an attention-based auxiliary multimodal deep learning architecture. The base module of this method consists of a 1D CNN and GRU with the attention mechanism. The forecasted spatio-temporal traffic demand (counts of traffic passing through different locations at regular time intervals) is dependent on far more critical spatial factors than other point sensors such as weather stations. This HaMDeepT method, in terms of 3D CNN-GRU, which uses a stack of 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Units (GRUs) combined with the Correlation and Relative Operation layers to model both the spatial context features and the temporal dependencies of traffic count data at all locations, has a better performance compared to other network architectures. It overcomes the drawback of a fixed and handcrafted graph Laplacian matrix representation of the spatial relationships of the locations used by the ST-Graph. It uses the Correlation layer to estimate the spatial correlation features for each traffic count data point with others, focusing on the stations with major impacts on the target location, and the Relative Operation layer to model the relative distances thereafter. Using these novel methods, the traffic flow forecasting results for the miniNYC dataset are more accurate and more intuitive visualisation of the spatial structure that affects the performance of the predictions.

traffic flow forecastingintelligent transportation systemsspatio-temporal modelingmultimodal deep learningHaMDeepTshort-term traffic prediction+17 more
3,249 views
969 downloads
DOI:
10.70179/vc6nrq25

Contributors:

 Srinivas Kalisetty
,
 Phanish Lakkarasu
Research PaperID: jaibdd110040

Designing Infrastructure for Agentic AI Systems in Retail IT and Data Operations

Shabrinath Motamary

The integration of AI systems in consumer markets, especially in retail settings, introduces new uncertainties, risks, and challenges. The assortment of digital tools available to retailers has surged in recent years, with advancements in Artificial Intelligence (AI) software becoming readily accessible and cost-efficient to deploy. As retailers integrate these digital capabilities into their settings, the systems they create are increasingly free to change their behavior in informative, interpretative, or generative ways. This ability to create social artifacts enables new competitive capabilities for companies—their AI systems can proactively shape the market context in which they operate and the role of their organizational customers within it, radically transforming the competitive environment that has persisted over recent decades. These technological and social developments go hand-in-hand, shaping the design of the AI systems that are to be deployed and their business context. Data capabilities enable retailers to launch and scale data-driven AI capabilities that respond to changes in the market context. However, such systems are difficult to design and necessitate improvements in organizational sense-making and designing collaborative working practices. New practices that ground technical systems in human expertise, enable end-users in the business units to shape the target of AI systems, and operationalize expectations and predictions are required. Practitioners need procedures for analyzing how AI capabilities influence the end-users under their organizational responsibilities and the attended channels in their business market. Data operations that address both the NTY and sustain capabilities of AI systems are matters of a new kind of collective impact. The design of practices and the allocation of roles and responsibilities across units are of concern to corporate boards and the highest levels of management in the organization. Adopting and growing data-driven AI capabilities require substantive position and/or infrastructure changes at the highest organizational levels. Long-term commodification of data precepts into business decisions and practices is necessary to leverage the investment and risk that companies take on in new black-boxed AI capabilities.

AI integration in retailconsumer marketsdigital toolsartificial intelligence softwaregenerative AImarket shaping+17 more
3,726 views
1,104 downloads
DOI:
10.70179/wmj3cg65

Contributors:

 Shabrinath Motamary
Research PaperID: jaibdd110022

Innovative Intelligence Solutions for Secure Financial Management: Optimizing Regulatory Compliance, Transaction Security, and Digital Payment Frameworks Through Advanced Computational Models

Srinivasarao Paleti, Vamsee Pamisetty, Kishore Challa, Jai Kiran Reddy Burugulla, Abhishek Dodda

A Managing Compliance in Financial Institution Security is required to ensure that sensitive financial transactions are carried out without incurring losses. Losses would be due to a number of factors whether internal or external, deliberate or accidental, and strongly dependent on correct and timely reactions in response to incidents. Events related to the assessment of online compliance can be classified in terms of the impact on the financial transactions e.g. fraud. Events exposing the transaction to fraud are used to generate rules to monitor cryptographic techniques applied to sensitive financial data, either as part of the transaction or for value recovery. Intelligent block-based fuzzy classification is used to determine different safety levels for different parts of the financial data thereby enabling secure trade with a lowest level of encryption and s igning overhead. This is facilitated by intelligent targeting of fraud events cutting through a range of signatures. Experiments with sets of fraud profiles derived from analysis of previous incidents employing branded-transaction card fraud are presented. In these experiments, monitoring rules are generated automatically using unsupervised neural gas clustering from detection blocks that are input to the intelligent classification engine. It is suggested that the versatility of G- Cluster in this area is demonstrated by the ability to adjust the fraud profile easily.

financial institution securitycompliance managementfraud detectionsensitive financial transactionsonline compliance assessmentcryptographic techniques+14 more
3,508 views
1,077 downloads
DOI:
10.70179/dbqg5s13

Contributors:

 Srinivasarao Paleti
,
 Vamsee Pamisetty
,
 Kishore Challa
,
 Jai Kiran Reddy Burugulla
,
 Abhishek Dodda
Research PaperID: jaibdd110041

Integrating AI and Big Data for Real-Time Payment Processing in Digital Banking

Jai Kiran Reddy Burugulla

Real-time traffic congestion is a challenging problem in smartcities. An Intelligent Transportation System (ITS) is a big data application integrating sensor hardware and network technologies, which can intelligently capture traffic information, efficiently transform data into knowledge, and organize and manage transportation resources. Urban Traffic Control (UTC) is a critical component of ITS, analyzing real-time traffic information and coordinating traffic signal timing plans to optimize traffic network performance and improve vehicle travel speeds. However, traditional UTC based on centralized architecture would be challenged in data transmission, architecture malfunctions, system bottlenecks, etc. As a solution, the multi-Agent based RTC (ATC) with smart intersections is proposed. Additionally, more comprehensive traffic data can be captured with advanced detection techniques, and Regional Traffic Control (RTC) systems can be designed with advanced optimal control algorithms. Digital Banking (DB) Infrastructures powered with AI can be trained on historical data to simulate human understanding of patterns and trends when leveraging custom models tuned to understand banking transactions. Integrating artificial intelligence (AI) and automation into the digital banking infrastructure can result in a stable pipeline implementation of sorting transactions data for anomalies per the bank thresholds. Moreover, with the integration of AI systems, captured data can be analysed to study the traffic characteristics of the bank and determine how efficiently it is working. AI can also be used to examine this data, identify problematic data, perform risk prediction, timely tracking, and further determine whether it fits the standards of bank transactions. AI can warn of difficulties in bank transactions. When an unauthorised transaction is detected, that transaction can be prohibited in real-time. Furthermore, it can help to significantly increase banks' risk management levels, improved efficiency and a near-zero error output requirement of regular activity.

real time traffic congestionsmart citiesintelligent transportation systemurban traffic controlmulti agent systemssmart intersections+16 more
3,901 views
1,227 downloads
DOI:
10.70179/c7rg5a81

Contributors:

 Jai Kiran Reddy Burugulla
Research PaperID: jaibdd110005

Integrating Quantum Computing and Big Data Analytics for Accelerated Drug Discovery: A New Paradigm in Healthcare Innovation

Tulasi Naga Subhash Polineni

The manipulation of atomic and molecular structures has been a topic of interest in recent years owing to the broad range of applications that its control entails. Researchers in areas such as macromolecular science are highly interested in protein folding problems, while direct drug discovery methods focus on strategies for designing ligands or modulators that target proteins of interest. In addition to targeting a specific protein, one of the principal objectives of work related to drug production is the modification of drugs in such a way that their performance profile is improved concerning that of other drugs. In this regard, the large amount of chemical data and their respective biological activities encoded in big data analytics will be a cornerstone in dealing with problems related to drug design. As a result of the many different data sources employed, strategies for the analysis of big data emerging from the world of research, especially the computer and health sciences are currently quite varied.

atomic and molecular manipulationprotein foldingmacromolecular sciencedrug discoveryligand designprotein targeting+14 more
1,556 views
609 downloads
DOI:
10.70179/9mrpr264

Contributors:

 Tulasi Naga Subhash Polineni
Research PaperID: jaibdd110003

Optimizing Retail Demand Forecasting: Big Data-Driven AI Models for Enhanced Customer Experience and Operational Efficiency

Vishwanadham Mandala

The retail and consumer sector is a major contributor to global economic output but faces challenges in terms of uncertain consumer behavior, fast-changing technology, and retail convenience. Accurate demand forecasting is of paramount importance in the overall retail decision-making process. It is a prerequisite of broader application areas, such as stock control, merchandising, and pricing. Inadequate demand forecasting can lead to lost margin opportunities in terms of out-of-stock scenarios, as well as heavy discounting of goods in response to overstocking. In online retail markets, convenience disappears if goods are not available when the consumer wants or a store closes, as goods sell out at a full price. In a world of volatile trading conditions, the forecast of future demand is more difficult and less reliable than it used to be, due to both internal business complexity and increased external market disruption. Demand signal data need to be incorporated into the forecasting process, and it is necessary to use the techniques available to improve the efficiency and speed of the demand forecasting process to enhance the level of operational control. To cater to all these challenges and opportunities, recent academic research has covered a wide range of themes in retail demand forecasting. Retailers themselves are investing heavily in information systems, data management, analytical, and forecasting capabilities. Software providers offer a broad array of demand forecasting tools, some of which are cloud-hosted. Options are attractive as they allow access to broader data and more sophisticated models. Some business problems require solutions that are not covered by off-the-shelf software and therefore may require more customization to be suitable for enterprise implementation. Such solutions could aim at automating the demand forecasting process, leveraging a broad range of external data sources, creating outside views of future demand for bricks-and-mortar retail space, as well as seamless integration with operations and other decision-making cycles. Such AI-driven demand forecasting solutions can achieve positive outcomes, such as enhanced customer experience and supply chain optimization

retail demand forecastingconsumer behaviorretail analyticsstock controlmerchandisingpricing optimization+17 more
1,594 views
460 downloads
DOI:
10.70179/0z418572

Contributors:

 Vishwanadham Mandala
Research PaperID: jaibdd110021

Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management

Sneha Singireddy, Balaji Adusupalli, Avinash Pamisetty, Someshwar Mashetty, Pallav Kumar Kaulwar

This research study develops for the first time concepts that bring to the forefront an order of magnitude leap in the way financial risk is managed and mitigated by corporations, and in their interactions with their financial services suppliers. At the core of the research work are the development of a specific type of enterprise robotics theories and solution methodologies; end-to-end systems for secure access, navigation, and protection for financial applications using federated identity and attribute management; and the introduction of predictive intelligence using a few dominant principles, modeling, and estimation methods, risk equations, and estimation feedback protocols. In addition to the foundation studies for each of the three major components, we integrate the three into an enterprise financial risk solution and demonstrate through a set of complex activity models and problem derivative-specific applications the nature and degree of financial engineering, financial and data communications, modeling and corporate customer service integration analytics, information and business intelligence, managerial zones, and innovation theories involved.

financial risk managementcorporate financeenterprise roboticssecure financial applicationsfederated identity managementattribute management+14 more
3,018 views
1,019 downloads
DOI:
10.70179/k86dcv60

Contributors:

 Sneha Singireddy
,
 Balaji Adusupalli
,
 Avinash Pamisetty
,
 Someshwar Mashetty
,
 Pallav Kumar Kaulwar
Research PaperID: jaibdd110010

Resilient Manufacturing in the Era of Industry 4.0: Leveraging AI and Edge Computing for Real-Time Quality Control and Predictive

Manogna Dolu Surabhi

Quality is the driving factor of the manufacturing industry. With the advent of Industry 4.0, the integration of advanced sensors, edge computing, artificial intelligence, digital twins, and 3D printing has great potential to achieve resilient manufacturing. The first part of this study addresses real-time quality control with edge computing. A digital imaging system is introduced, equipped with an edge device for real-time defect detection in additive manufacturing. AI promises great potential for achieving zero-defect manufacturing, which was barely possible in the past. Then, as the main structure provider for Industry 4.0, edge computing is described. The computer is used in quality control features of edge computing by real-time cloud monitoring and real-time elasticity for cloud services. In the second part, a predictive maintenance framework is proposed by integrating the digital twin, advanced data processing, and AI algorithms. AI finally determines the degradation status of products with high confidence to maintain the resilience of the product life cycle. Expensive fault events on products can be avoided with the help of AI-based advanced planning of maintenance at an appropriate time. The approach could mitigate potential risks of shortening lifespan, increasing maintenance costs, triggering catastrophic events, or even causing social impact if combined with real-time edge computing and advanced sensors. Such an approach advances the deployment of the manufacturing industry in the era of Industry 4.0.

Industry 4.0resilient manufacturingreal-time quality controledge computingartificial intelligencedigital twins+16 more
2,138 views
713 downloads
DOI:
10.70179/c1b44587

Contributors:

 Manogna Dolu Surabhi
Research PaperID: jaibdd110002

Revolutionizing Patient Outcomes: The Role of Generative AI and Machine Learning in Predictive Analytics for Healthcare

Valiki Dileep

For the healthcare industry, predictive analytics offer revolutionary benefits for improving patient outcomes, reducing hospital readmissions, and lowering treatment costs. The increasing adoption of electronic health records allows the modeling of laboratory results, medications, and socio-economic data, as well as mental health, among others. We emphasize the opportunities that generative models offer for predictive healthcare analytics and the necessity for healthcare analytics to contextualize data relationships. We analyze predictive models, understand our contextual data relationships, interpret our results, expose them, and understand why models are learning certain relationships. We make use of benchmark data and case studies to illustrate our points. Our discussion concludes by offering a framework and a departure point for future related research.

predictive analyticshealthcare analyticspatient outcomeshospital readmissionstreatment cost reductionelectronic health records+15 more
1,578 views
417 downloads
DOI:
10.70179/js9jft76

Contributors:

 Valiki Dileep
Research PaperID: jaibdd110020

Strategic Financial Growth: Strengthening Investment Management, Secure Transactions, and Risk Protection in the Digital Era

Srinivasa Rao Challa, Kishore Challa, Phanish Lakkarasu, Harish Kumar Sriram, Balaji Adusupalli

This paper will take a critical view of issues of investment management, secure transactions, and risk in the digital era. The Financial Services Industry is a product of the Information Technology (IT) revolution. The application of IT have had profound implications for how products are produced, how they’re exchanged, who develops the new products, and how the old products are replaced. The current wave of technological evolution is changing the way in which financial services are produced and delivered. Developments in the World Wide Web sector have accounted for an expansion and strengthening of the online transaction sector, with a growing number of fully automated banking platforms. This transition has been accompanied by considerable public expenditures on new security technologies to protect it. Of particular interest in this context is the provision of data privacy and the prevention of fraud through secure transactions in order to bolster consumer and institutional confidence in the transacting financial markets. This paper will consider investment management, secure transactions, and risk assessment for online activities in the financial sector. Technological advancements in the field of IT have allowed the financial industry to take advantage of networked computing, to automate trading decisions, and to trade very quickly and in large volumes. The financial industry can now use sophisticated high performance computer driven trading platforms. These developments have had a significant impact on the way banks, fund managers, and individuals trade currency and equity. To this day most financial orders are negotiated through people, often using voice linked dealing systems. This experience has decreased considerably due to the rapid growth of automated computer systems capable of almost instantaneous order placement with the corresponding financial institutions. With appropriate technology and clear development the banks and exchanges alike can now record at high frequency virtually all transactions that occur in the financial market. Furthermore, several data vendors are now selling high frequency data in relatively cheap and easy to work formats, essentially lowering the barriers to entry in this field. Some people consider these characteristics to be sufficient to classify financial data (specifically price data) as “Big Data”.

investment managementsecure transactionsfinancial riskdigital financeinformation technology in financeonline banking platforms+15 more
3,239 views
1,015 downloads
DOI:
10.70179/1kfdwj89

Contributors:

 Srinivasa Rao Challa
,
 Kishore Challa
,
 Phanish Lakkarasu
,
 Harish Kumar Sriram
,
 Balaji Adusupalli
Research PaperID: jaibdd110011

Towards Zero Downtime: Enhancing Data Center Reliability with AI-Driven Predictive Maintenance and Edge Computing Strategies

Venkata Kesava Kumar Majjari

This study aimed to investigate the role of artificial intelligence (AI) in predictive data center maintenance practices and strategies. Timely detection of different types of equipment faults and subsequent predictive maintenance can enhance data center availability dramatically and minimize costly outages. The rationale for the study came from the rapid growth of digital services and users, as well as the costly data centers supporting this growth. The costs of a minute (or more) of an unplanned service disruption range from $10,000 to $65,000. The three main Data Center Infrastructure Management (DCIM) components—the common sensors for continuous data acquisition, prevention to power off, and ensuing prediction and solution techniques—are investigated. Multiple other machine learning classification models are suggested to be tested on a similar dataset, in addition to classification, and to quantitatively test a real-life installation in future research. In addition, how these components interact in real-world environments is still not clear and would require advanced statistical analyses, which have not been done in this research. Yet, under the conditions of this study, the results demonstrate how AI elements can provide a reliable solution for enhanced data center DCIM and be applied in deliverable form.

artificial intelligencepredictive maintenancedata center managementDCIMequipment fault detectionmachine learning+14 more
2,251 views
665 downloads
DOI:
10.70179/rvbfak07

Contributors:

 Venkata Kesava Kumar Majjari

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