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Research Paper

FinTech Transformation and its Disruptive Impact on Traditional Financial Systems

The financial sector is undergoing a profound transformation driven by advancements in financial technology (FinTech). This study undertakes a comprehensive literature review to analyze the extent to which emerging technologies—such as artificial intelligence (AI), machine learning (ML), and big data analytics—are reshaping traditional banking and financial services. By examining key areas of disruption, including peer-to-peer lending, digital banking services, and mobile payment platforms, the research provides insights into the implications of FinTech innovations on legacy banking practices. Furthermore, the paper explores the strategic responses of traditional financial institutions, such as partnerships with FinTech firms and investments in digital transformation, alongside the critical regulatory challenges arising from these developments. The findings reveal a dual impact: FinTech has democratized access to financial services and enhanced operational efficiency, yet it has also introduced regulatory and cybersecurity complexities. The review concludes by emphasizing the importance of regulatory alignment, technological adaptation, and collaborative efforts for sustainable growth in the evolving financial ecosystem.

Published by: Aditya Prakash, Nikita Tanksali

Author: Aditya Prakash

Paper ID: V11I3-1356

Paper Status: published

Published: June 18, 2025

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Research Paper

Corporate Security and Safety System

In today's commercial world, corporate security and safety are essential elements. Ensuring the safety of personnel, property, and infrastructure becomes critical as businesses expand and function in more complicated and frequently dangerous contexts. Conventional security solutions, such having employees on the scene and manually monitoring video systems, have limits in terms of their scope and efficacy, particularly when it comes to spotting dangers that change quickly, like fire or firearms. Intelligent surveillance systems that make use of cutting-edge technology like computer vision and machine learning are becoming more and more necessary to overcome these constraints. Significant gains in object detection and real-time monitoring capabilities have been made possible by the latest developments in deep learning. One such innovation is the YOLO (You Only Look Once) algorithm, which is renowned for its high-accuracy real-time object detection capabilities. Because of its single-shot detection technique, YOLO can quickly and effectively identify objects in photos or video streams, which makes it a great option for applications that demand quick decision-making and high processing efficiency. The creation of an automated corporate safety system that uses the YOLO algorithm to detect fire and weapons is the idea put forth in this paper. Organizations can enhance security and fire safety measures by automating the identification of weapons, knives, and fire-related threats in corporate environments by incorporating YOLO.

Published by: Thayalaraj K, Vijaya Lakshmi S, Ponneela Vignesh R

Author: Thayalaraj K

Paper ID: V11I3-1347

Paper Status: published

Published: June 14, 2025

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Research Paper

Combining Machine Learning and Cryptography for Privacy-Focused Malicious URL Detection

Online safety is frequently and seriously at risk from malicious URLs and websites. Naturally, search engines are the cornerstone of information management. However, our users are now seriously at risk due to the widespread presence of bogus websites on search engines. The majority of methods used today to identify rogue websites focus on a specific attack. Online safety is frequently and seriously at risk from malicious URLs and websites. Naturally, search engines are the cornerstone of information management. However, our users are seriously at risk due to the rise of bogus websites on search engines. The majority of methods used today to identify rogue websites focus on a specific attack. However, a lot of websites remain unaffected by the widely accessible blacklist-based browser add-ons. Any data leaving the client side must be properly disguised, as the server cannot infer any meaningful information from the masked data. Here, the recommended initial Privacy-Preserving Safe Browsing (PPSB) service is given. Robust security assurances are given, which the existing SB services do not offer. The suggested method uses blacklist storage to identify malicious URL access. SVM classification was used to classify the user-provided input URL. SVM is a class of machine learning algorithms that reliably determines the safety or riskiness of a URL. Specifically, it retains the ability to identify malicious URLs while protecting the user's privacy, browsing history, and proprietary data of the blacklist provider (the list of dangerous URLs). This paper presented a technique that encrypts critical data to safeguard user privacy from outside analysts and service providers. Furthermore, completely supports the functions of chosen aggregates for analysing user behaviour online and guaranteeing differential privacy. The AES encryption method is used to protect user behaviour data online.

Published by: Sridevi S, Thayalaraj K

Author: Sridevi S

Paper ID: V11I3-1311

Paper Status: published

Published: June 14, 2025

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Research Paper

Creating Tailored Detection and Prevention Mechanisms for Targeted Threats

In today’s fast-paced world of cybersecurity, standard detection and prevention methods often fall short when it comes to dealing with specific, targeted threats. To truly protect against sophisticated cyberattacks, organizations need to create solutions that are tailored to their unique risks and threat profiles. This means understanding the types of potential attackers, analyzing threats specific to their industry, and implementing detection and prevention strategies that fit the organization’s systems, data, and daily operations. By focusing on detecting targeted threats, using advanced analytics, and continuously improving defenses based on real-time intelligence, organizations can stay ahead of emerging risks. This proactive approach helps minimize vulnerabilities and strengthen overall security, ensuring the organization is always one step ahead of potential attackers.

Published by: Poongodi R K, Thirumoorthi C, Mohamed ibrahim H, Jaysankar P, Aldan Jeri M

Author: Poongodi R K

Paper ID: V11I1-1494

Paper Status: published

Published: June 11, 2025

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Research Paper

The Effect of Product Display on Consumer Attention and Purchase Intention in Europe: A Comparative Analysis of Western and Eastern Europe

Product displays play a pivotal role in shaping consumer behavior in retail environments, influencing attention and purchase intentions. This study examines the differential effects of product display strategies on consumer attention and purchase intention in Western and Eastern Europe, exploring variations driven by cultural, economic, and technological factors. Using a mixed- methods approach, including eye-tracking experiments, surveys, and statistical analysis, we compare consumer responses to ordered versus disordered displays, in-store versus online settings, and the influence of visual merchandising elements. Results indicate significant regional differences, with Western European consumers showing greater sensitivity to ordered displays and Eastern European consumers responding more strongly to vivid, innovative displays. Implications for retailers and marketers are discussed, along with suggestions for future research.

Published by: Siddharth Jha

Author: Siddharth Jha

Paper ID: V11I3-1299

Paper Status: published

Published: June 11, 2025

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Survey Report

Survey Paper on Advancements in Dysarthric Speech Recognition Systems

Dysarthria, a motor speech disorder resulting from neurological injuries, severely impairs intelligibility, making automatic speech recognition (ASR) a vital tool for enhancing communication. Over the years, significant research has explored computational approaches to improve ASR performance for dysarthric speech, from early rule-based models to deep learning architectures. This survey presents a comprehensive review of the evolution of ASR techniques tailored to dysarthric speech, categorizing methods by architecture type (HMM, DNN, CNN, LSTM, Transformers), learning paradigm (supervised, self-supervised, meta-learning), and input modality (audio-only, multimodal). The study examines the role of acoustic features like MFCC, PLP, and raw waveform-based learning. It compares key models, including Wav2Vec2.0, TDNN, and UTran-DSR, across UA-Speech, TORGO, and CommonVoice datasets. A critical evaluation of strategies like speaker adaptation, transfer learning, end-to-end pipelines, and contrastive learning is provided, along with their impact on accuracy and generalization. The paper highlights emerging trends such as emotion-aware ASR, multimodal fusion, and personalized adaptation, while addressing persistent challenges including data scarcity, speaker variability, and real-time deployment. This survey aims to provide a clear roadmap of the progress and ongoing efforts in dysarthric ASR, guiding future research toward more inclusive and intelligent speech interfaces.

Published by: Sushmita Chaudhari, Mansi Chopkar, Harshvardhan Gaikwad, Anuj Raj

Author: Sushmita Chaudhari

Paper ID: V11I2-1282

Paper Status: published

Published: June 10, 2025

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