Revista Latinoamericana de la Papa

Author: editor@papaslatina.org

  • Machine Learning Applications in Autism Spectrum Disorder Therapy: Personalized Intervention Planning, Behavioral Monitoring, and Outcome Prediction

    Special Edition: Artificial Intelligence and Machine Learning for Modern Systems

    Original Research Article

    Authors: Er. Rishabh Aryan1*, Prof. Dr. Tryambak Hiwarkar2

    1M.Tech (Artificial Intelligence and Data Science), Department of Computer Science and Engineering,

    Indian Institute of Information Technology, Bhagalpur (Bihar), India

    2Director, ASM Group of Institutions, Pune, Maharashtra, India

    E-mail: rishabh.250201011@iiitbh.ac.in  |  tryambakhiwarkar@asmedu.org

    *Corresponding author: rishabh.250201011@iiitbh.ac.in

    Abstract

    Autism Spectrum Disorder (ASD) presents substantial heterogeneity in behavioral profiles, making one-size-fits-all intervention strategies suboptimal. This paper proposes a novel multi-modal machine learning framework — ASD-ML-Net — that integrates transformer-based behavioral sequence modeling, wearable biosensor data fusion, and gradient-boosted outcome prediction to deliver personalized Applied Behavior Analysis (ABA) intervention plans. Using a longitudinal dataset of 412 children aged 3–12 (ASD-BEHAV-412), collected across 24 weeks at three clinical centers, our system achieves 93.7% accuracy in real-time behavioral state classification and a Mean Absolute Error (MAE) of 2.31 on the Vineland Adaptive Behavior Scales Third Edition (VABS-3). The proposed framework significantly outperforms standard ABA scheduling baselines (p < 0.001) and demonstrates clinically meaningful gains in social communication, adaptive behavior, and reduction of repetitive behaviors. Our results confirm that data-driven, personalized intervention planning can substantially improve therapeutic outcomes for children with ASD.

    Keywords — autism spectrum disorder, machine learning, personalized intervention, behavioral monitoring, LSTM, transformer, outcome prediction, ABA therapy.

    Received: 31/04/2026

    Accepted: 28/05/2026

    Published: 09/06/2026

    DOI: 10.37068/ralap.2026.aimlfms.spec.01


    Citation: Aryan, R., & Hiwarkar, T. (2026). Machine learning applications in autism spectrum disorder therapy: Personalized intervention planning, behavioral monitoring, and outcome prediction. Revista Latinoamericana de la Papa, 1 (Special Edition: Artificial Intelligence and Machine Learning for Modern Systems), 1–8. Available online at https://ojs.papaslatina.org/machine-learning-applications-in-autism-spectrum-disorder-therapy-personalized-intervention-planning-behavioral-monitoring-and-outcome-prediction/

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    Machine Learning Applications in Autism Spectrum Disorder Therapy






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  • REVOLUTIONISING DENTAL CARE THROUGH ARTIFICIAL INTELLIGENCE: APPLICATIONS IN ORAL SURGERY AND DIAGNOSTICS

    Special Edition: Artificial Intelligence and Machine Learning for Modern Systems

    Original Research Article

    Authors: Er. Rishabh Aryan1*, Prof. Dr. Tryambak Hiwarkar2

    1M.Tech (Artificial Intelligence and Data Science), Department of Computer Science and Engineering,

    Indian Institute of Information Technology, Bhagalpur (Bihar), India

    2Director, ASM Group of Institutions, Pune, Maharashtra, India

    E-mail: rishabh.250201011@iiitbh.ac.in  |  tryambakhiwarkar@asmedu.org

    *Corresponding author: rishabh.250201011@iiitbh.ac.in

    Abstract

    Artificial Intelligence (AI) is catalysing a paradigm shift in dental medicine, particularly in the domains of oral surgery and clinical diagnostics. This original research study presents a prospective, multi-centre evaluation of AI-assisted diagnostic and surgical-planning systems applied to 1,840 dental patients across four tertiary care centres between January 2022 and December 2024. Employing convolutional neural networks (CNN), residual deep learning (ResNet-50), long short-term memory (LSTM) architectures, and support vector machines (SVM), the integrated AI platform demonstrated diagnostic accuracy of 94.2% for carious lesion detection, 91.8% for periapical pathology, 93.5% for early-stage oral mucosal cancer screening, and 89.4% for implant site assessment — all significantly exceeding mean clinician baselines (p < 0.001). AI-assisted surgical planning reduced pre-operative planning time by 73.6% for implant placement and 71.1% for orthognathic surgery procedures. Patient-reported outcomes (PROs) improved significantly, with post-operative pain VAS scores reduced by 28.4% and complication rates declining from 7.2% to 3.1% in the AI-guided cohort. Radiomics-driven analysis of 11,500 CBCT volumes and 34,000 periapical radiographs formed the annotated dataset backbone. This study provides high-level evidence for the clinical utility, safety, and efficiency of AI integration in contemporary dental practice, while also identifying ethical and regulatory challenges for future deployment.

    Keywords: Artificial Intelligence, Dental Diagnostics, Oral Surgery, Convolutional Neural Networks, CBCT, Deep Learning, Radiomics, Implant Planning, Oral Cancer Screening, Periodontal Assessment.

    Received: 17/05/2026

    Accepted: 04/06/2026

    Published: 07/06/2026

    DOI: 10.37067/ralap.2026.aimlfms.spec.01

    Citation: Aryan, R., & Hiwarkar, T. (2026). Revolutionising dental care through artificial intelligence: Applications in oral surgery and diagnostics. Revista Latinoamericana de la Papa, 1 [Special Edition: Artificial Intelligence and Machine Learning for Modern Systems], 1–10. Available online at https://ojs.papaslatina.org/revolutionising-dental-care-through-artificial-intelligence-applications-in-oral-surgery-and-diagnostics/

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  • Neuro-Symbolic Deep Learning for Interpreting Pre-Columbian Geoglyphs and Rock Art in the Brazilian Amazon: Implementation of a Multimodal Model, Explainability Analysis, and Governance Frameworks for Indigenous Cultural Sovereignty

    Special Edition: Advanced Computational Methods in Cultural Heritage Preservation

    This Article is archived at

    Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brasil

    Special Editor: 

    Dr. Ana Carolina Silva Santos, PhD

    Departamento de Antropologia e Arqueologia

    Universidade Federal de Minas Gerais (UFMG)

    Original Research Article

    Author:

    Er. Rishabh Aryan
    Department of Computer Science and Engineering
    Indian Institute of Information Technology, Bhagalpur, Bihar, India
    Email: rishabh.250201011@iiitbh.ac.in

    Abstract

    The Brazilian Amazon harbours several thousand documented geoglyphs, earthworks, and rock-art sites that represent some of the most complex yet least-understood pre-Columbian cultural productions in the Americas. Manual documentation is impeded by the scale of the territory, forest canopy occlusion, and the scarcity of trained archaeologists who also hold the linguistic and cultural competence required for contextualised interpretation. This paper presents the design, training, and evaluation of a Neuro-Symbolic Multimodal Learning (NSML) framework that integrates convolutional feature extraction, Vision Transformer (ViT-L/16) encoders, and an indigenous-knowledge ontology to classify and partially interpret Amazonian geoglyphs and rock art. The model is trained on a curated dataset of 14,372 georeferenced image instances drawn from satellite synthetic aperture radar (SAR) imagery, aerial LiDAR point clouds, and ground-level RGB photography, spanning 47 verified archaeological sites across the states of Amazonas, Pará, Acre, and Mato Grosso. On the held-out test partition, NSML achieves an F1-score of 88.2% and an AUC of 0.95, outperforming five baseline deep-learning architectures. Gradient-weighted Class Activation Mapping (Grad-CAM) and attention rollout are used to generate spatially localised explanations. A governance framework grounded in the principles of Free, Prior, and Informed Consent (FPIC) and the CARE Principles for Indigenous Data Governance is formalised, addressing data sovereignty, differential access tiers, and benefit-sharing mechanisms. Our findings demonstrate that neuro-symbolic approaches not only surpass purely connectionist models in classification accuracy but also produce explanations that are more readily validated by indigenous knowledge holders, thereby strengthening community trust and cultural sovereignty over their own heritage data.

    Keywords: Neuro-Symbolic AI, Geoglyphs, Amazonian Rock Art, Vision Transformer, Explainability, Indigenous Data Sovereignty, FPIC, CARE Principles, LiDAR, Remote Sensing

    DOI: 10.37066/ralap.2026.ufmg.spec.01

    Citation:

    Er. R. Aryan, “Neuro-Symbolic Deep Learning for Interpreting Pre-Columbian Geoglyphs and Rock Art in the Brazilian Amazon: Implementation of a Multimodal Model, Explainability Analysis, and Governance Frameworks for Indigenous Cultural Sovereignty,” Revista Latinoamericana de la Papa, vol. 1, no. Special Edition: Advanced Computational Methods in Cultural Heritage Preservation, pp. 1–9, 2026. DOI: 10.37066/ralap.2026.ufmg.spec.01

    Received: 17/05/2026

    Accepted: 30/05/2026

    Published: 01/06/2026

    Copyright of this article is retained by the author.








    Neuro-Symbolic Deep Learning for Interpreting Pre-Columbian Geoglyphs and Rock Art in the Brazilian Amazon






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  • ENHANCING DERMATOLOGICAL DIAGNOSIS THROUGH MULTIMODAL DEEP LEARNING AND VISUAL–TEXTUAL DATA FUSION 

    Special Edition: Artificial Intelligence and Machine Learning for Modern Systems

    This Article is archived at

    CERN (Switzerland)

    Original Research Article

    Authors: I. Manimozhi¹*, D. Lakshmi2, Er. Rishabh Aryan3

    1Research Scholar, Dept. of CSE, AMET Deemed to be University, Kanathur-603112, India

    2 Professor, Dept of EEE, AMET Deemed to be University, Kanathur- 6003112, India

    3M.Tech Artificial Intelligence and Data Science, Department of CSE, Indian Institute of Information Technology, Bhagalpur (Bihar) 

    *Corresponding author: manimozhirajkumar02@gmail.com 

    Abstract

    Accurate dermatological diagnosis requires more than visual inspection of skin lesions—it also depends on patient history, symptoms, and demographic details. While current deep learning systems achieve high performance in image classification, they often ignore this complementary clinical information, limiting their usefulness in real-world practice. In this work, we present a multimodal deep learning framework that unifies dermoscopic images and textual clinical records into a single diagnostic model. Visual features are extracted through convolutional networks, while textual features are encoded using transformer-based language models. A hybrid fusion strategy is then employed to align and integrate these heterogeneous data streams, enabling richer and more context-aware predictions. Experiments conducted on publicly available dermatology datasets demonstrate that the proposed model consistently surpasses image-only and text-only baselines, particularly in cases where visual ambiguity exists. Beyond improving accuracy, the framework also enhances interpretability by highlighting the contribution of both image patterns and textual cues to the final decision. These results underline the importance of multimodal learning in building clinically robust AI systems, with strong potential to assist dermatologists in early and precise disease detection.

    Keywords: Multimodal learning, dermatology AI, image-text integration, skin disease diagnosis, clinical decision support, convolutional networks, transformers, healthcare intelligence.

    DOI: https://doi.org/10.5281/zenodo.20474713

    Published: 31/05/2026

    Citation:

    Manimozhi, et al. ENHANCING DERMATOLOGICAL DIAGNOSIS THROUGH MULTIMODAL DEEP LEARNING AND VISUAL–TEXTUAL DATA FUSION. Zenodo, May 2026, https://doi.org/10.5281/zenodo.20474713.

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    Enhancing Dermatological Diagnosis Through Multimodal Deep Learning and Visual–Textual Data Fusion






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  • INVESTIGATION OF FARMER LIVELIHOODS AND THE ROLE OF DIGITAL TECHNOLOGIES IN EMPOWERING RURAL COMMUNITIES IN BHAGALPUR, BIHAR, INDIA

    Full Length Research Article (Monograph)

    Authors: 

    1. Rishabh Aryan, M.Des (UX), School of Design, DIT University, Dehradun (Uttarakhand, India).
    2. Abdul Kalam, Assistant Professor, School of Design, DIT University, Dehradun (Uttarakhand, India).

    Corresponding Author: Abdul Kalam, Assistant Professor, School of Design, DIT University, Dehradun (Uttarakhand, India).

    Email ID: abdul.kalam@dituniversity.edu.in  

    Abstract

    In the rural, agrarian landscapes of Bhagalpur, Bihar, India, the dynamics of local livelihoods are deeply intertwined with the evolving influence of digital technologies. This study investigates the current status of farmer livelihoods, exploring how digital tools empower rural communities while identifying the multi-dimensional barriers that hinder widespread adoption. Historically constrained by small and fragmented landholdings, unpredictable weather, exploitative middlemen, and infrastructure deficits, local farming households face ongoing threats to their economic security. 

    The introduction of mobile connectivity and digital tools has initiated a transformative shift, bridging information gaps by offering real-time weather forecasts, market pricing transparency, and personalized agronomic advisories. Furthermore, the study evaluates the level of awareness across four digitized information sources, finding robust recognition for m-Agriculture (mean awareness score of 3.68), moderate awareness for web portals and hybrid ICT projects, and notably lower familiarity with physical knowledge centers. Beyond direct productivity and economic yields, digital platforms promote socioeconomic resilience, enhance financial inclusion via mobile banking, and open up specialized pathways for marginalized groups like women and rural youth. However, a persistent digital divide along socioeconomic, gender, and age lines, combined with erratic electricity and limited broadband in remote areas, threatens equitable digital inclusion. The study concludes that overcoming these systemic limitations requires multi-stakeholder collaboration, continuous policy support, targeted infrastructure investment, and advanced digital literacy programs to cultivate a sustainable, digitally empowered rural economy. 

    Keywords: Digital Technology Adoption , Farmer Livelihoods , Rural Empowerment, m-Agriculture, Agricultural Productivity, Digital Divide, Bhagalpur, Bihar, Socioeconomic Resilience.

    Accepted: 06/02/2025

    Published: 23/03/2025

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    INVESTIGATION OF FARMER LIVELIHOODS AND THE ROLE OF DIGITAL TECHNOLOGIES IN EMPOWERING RURAL COMMUNITIES IN BHAGALPUR, BIHAR, INDIA






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  • CONNECTING THE DOTS: MAPPING & MASTERING THE TSDPL CUSTOMER EXPERIENCE

    Full Length Research Article (Monograph)

    Authors:

    1. Rishabh Aryan, M.Des (UX), School of Design, DIT University, Dehradun (Uttarakhand, India).
    2. Abdul Kalam, Assistant Professor, School of Design, DIT University, Dehradun (Uttarakhand, India).
    3. Mahima Yadav, Assistant Professor, School of Design, DIT University, Dehradun (Uttarakhand, India).
    4. Subhash Chandra Pandey, Manager (Human Resources), Tata Steel Downstream Products Limited (Jamshedpur, Jharkhand).

    Corresponding Author: Abdul Kalam, Assistant Professor, School of Design, DIT University, Dehradun (Uttarakhand, India)

    Email ID: abdul.kalam@dituniversity.edu.in

    Abstract:

    In today’s intensifying business landscape, merely providing high-quality products is no longer sufficient to sustain a competitive edge. As customer expectations shift toward personalized and seamless interactions across channels, managing Customer Experience (CX) has become a vital driver of brand loyalty, advocacy, and financial performance. 

    This project explores the development of an extensive Customer Journey Map (CJM) for Tata Steel Downstream Products Limited (TSDPL), a major steel processing and supply chain solutions provider. Utilizing a mixed-method research approach combining qualitative customer interviews, quantitative experience surveys, website analytics, and cross-functional departmental collaboration, the study systematically tracks customer touchpoints across five critical stages: Awareness, Consideration, Purchase, Post-Purchase, and Advocacy. 

    The primary findings reveal a generally positive customer sentiment fueled by highly helpful sales representatives and prompt order accuracy. However, significant friction points and operational bottlenecks were identified, particularly concerning complex website navigation, user interface (UI) limitations, a desire for broader value-added services (such as specialized fabrication), and localized gaps in customer service technical knowledge. To address these challenges, distinct customer personas were formulated to guide target experiences. The report outlines critical strategic recommendations to optimize TSDPL’s touchpoints, emphasizing the enhancement of website UI/UX design, simplification of the online checkout process, integration of omni-channel communication networks via CRM systems, implementation of continuous customer feedback loops, and robust employee empowerment and training frameworks. Ultimately, this research underscores that continuous evaluation and responsive, customer-centric refinement of the customer journey are foundational to reinforcing long-term corporate growth, differentiation, and stakeholder value in the downstream steel sector. 

    Keywords: Customer Experience (CX), Customer Journey Mapping (CJM), Tata Steel Downstream Products Limited (TSDPL), Steel Processing Industry, Touchpoint Optimisation, UI/UX Design, Customer Personas, Supply Chain Management, Value-Added Steel Solutions, Omni-channel Communication, Employee Empowerment & Training, Design Thinking.

    Accepted: 19/10/2024

    Published: 10/11/2024

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    CONNECTING THE DOTS: MAPPING & MASTERING THE TSDPL CUSTOMER EXPERIENCE







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  • Enhancing Government-Citizen Interaction: Bridging Service Gaps through Digital Design Solutions in Bihar, India

    Case Study

    Authors:

    1. Rishabh Aryan, M.Des (UX), School of Design, DIT University, Dehradun (Uttarakhand, India).
    2. Abdul Kalam, Assistant Professor, School of Design, DIT University, Dehradun (Uttarakhand, India).

    Corresponding Author: Abdul Kalam, Assistant Professor, School of Design, DIT University, Dehradun (Uttarakhand, India)

    Email ID: abdul.kalam@dituniversity.edu.in

    Abstract:

    In the rapidly evolving landscape of public administration, leveraging digital design solutions is imperative to foster transparent, accountable, and inclusive governance. This study, titled “Enhancing Government-Citizen Interaction: Bridging Service Gaps through Digital Design Solutions in Bihar, India,” investigates the potential of utilising digital tools and user-centered design principles to mitigate deep-seated public service delivery constraints. Bihar faces significant administrative hurdles, characterised by inadequate infrastructure, low digital literacy, bureaucratic inefficiencies, and a pronounced rural-urban digital divide that often excludes marginalised communities. 

    Adopting a descriptive and exploratory mixed-methods approach, the research combines qualitative and quantitative methodologies—including structured surveys, stakeholder interviews, focus group discussions, and document analysis. To illustrate the practical flow of public service networks on the ground, the study utilizes Social Network Analysis (SNA) within a case study focused on the state’s agricultural sector. By interviewing 111 key informants across six districts, the network mapping evaluates how administrative figures—such as District Agricultural Officers, Block Agricultural Officers, and village-level advisors (Krishi Salahakars)—control, transmit, and utilize critical information. 

    The findings reveal that while bureaucratic hierarchies can be time-consuming and prone to information lag, the penetration of mobile communication technologies successfully expedites service responsiveness by bypassing unnecessary administrative layers. Furthermore, network dynamics vary heavily by district, indicating that a one-size-fits-all digital strategy is ineffective. Based on these insights, the study proposes actionable policy frameworks rooted in Human-Centered Design (HCD). It recommends formalizing grassroots workers as central information nodes, modernizing dissemination channels using mobile and video tools, upgrading personnel skills, and creating localized digital repositories in vernacular languages. Ultimately, this research provides a strategic blueprint for policymakers to minimize service delivery gaps, optimize digital interaction, and enhance overall public trust in governance. 

    Keywords: Digital Governance / E-Governance, Public Service Delivery, Human-Centered Design (HCD), User-Centered Design, Social Network Analysis (SNA), Government-Citizen Interaction, Digital Divide, Bihar Public Administration, Social Knowledge Networks (SKNs), Information Dissemination.

    Accepted: 26/06/2024

    Published: 18/07/2024

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    Enhancing Government-Citizen Interaction: Bridging Service Gaps through Digital Design Solutions in Bihar, India






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  • A Study on Digital Marketing and Its Impact on Revenue Generation

    Full Length Research Article (Monograph)



    Authors:

    1. Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.
    2. Poonam Gupta, Assistant Professor, Department of Computer Science and Engineering, MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Corresponding Author: Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Email ID: rishabharyan07052004@gmail.com

    Abstract:

    In today’s fast-paced digital landscape, businesses across industries are experiencing a paradigm shift in their marketing strategies. The emergence of digital marketing has ushered in a new era of connecting with consumers, driving engagement, and, most importantly, impacting revenue generation. This comprehensive study explores the intricate relationship between digital marketing and revenue generation, shedding light on the multifaceted aspects that shape this dynamic intersection. 

    The primary aim of this research is to dissect the landscape of digital marketing and its transformative effect on revenue streams for businesses of all sizes. Through a meticulous blend of empirical analysis, case studies, and industry expertise, this study endeavors to unearth the strategies, tools, and techniques that have reshaped the marketing landscape.

    Critical areas under examination encompass the pivotal role of social media platforms, the efficacy of content marketing, the science of search engine optimization (SEO), and the analytical prowess of data-driven marketing campaigns. Furthermore, this study delves into the profound impact of shifting consumer behavior within the digital realm, highlighting its profound implications for crafting marketing strategies that resonate with modern audiences.

    The findings of this study serve as a clarion call to businesses to embrace digital marketing as an indispensable component of their revenue-generation machinery. As consumer preferences evolve in response to the digital age, it is imperative for organizations to adapt their marketing strategies accordingly. The research underscores that successful navigation of the digital marketing landscape can lead to substantial revenue growth, ensuring the long-term sustainability and competitiveness of businesses in the digital era. 

    This study acts as a beacon for businesses seeking to harness the full potential of digital marketing to drive revenue generation. By illuminating the symbiotic relationship between digital marketing endeavors and revenue outcomes, it empowers businesses to make data-informed decisions, optimize their digital marketing strategies, and ultimately embark on a trajectory of enhanced revenue generation and sustainable growth in the digital age.

    Keywords: Digital marketing, Revenue generation, Marketing strategies, Consumer behavior, SEO.

    Accepted: 09/03/2023

    Published: 31/03/2023

    Author(s) Retains the Copyrights of This Article







    A Study on Digital Marketing and Its Impact on Revenue Generation






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  • To Study the Insurance Claims Detection Using Machine Learning

    Technical Notes

    Authors:

    Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Amit Kumar Sahu, Assistant Professor, Department of Computer Science and Engineering, MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Corresponding Author: Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Email ID: rishabharyan07052004@gmail.com

    Abstract:

    The proliferation of fraudulent insurance claims poses a significant challenge to the insurance industry, leading to substantial financial losses and eroding trust among policyholders. This study delves into the realm of Fraudulent Insurance Claims Detection Using Machine Learning, a dynamic approach aimed at harnessing the power of artificial intelligence to mitigate this pervasive issue.


    With the advent of advanced technologies and the availability of vast datasets, machine learning algorithms have emerged as a potent tool for automating the detection and prevention of fraudulent insurance claims. This research undertakes a comprehensive exploration of the subject, focusing on the
    development and evaluation of machine learning models designed to distinguish genuine claims from
    fraudulent ones.


    The study encompasses various facets of the fraudulent claims detection process, including data preprocessing, feature engineering, model selection, and performance evaluation. Machine learning algorithms such as Random Forest, Support Vector Machine, Neural Networks, and Decision Trees are
    scrutinized for their efficacy in identifying suspicious patterns and anomalies within insurance claims data. Natural Language Processing (NLP) techniques are also applied to textual information to extract valuable insights.


    Results obtained from extensive experimentation with real-world insurance datasets reveal promising outcomes. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) statistics are employed to gauge the model performance, including metrics such as True Positives (TP),
    True Negatives (TN), False Positives (FP), and False Negatives (FN).


    Additionally, this research investigates the integration of deep learning and generative adversarial networks (GANs) to enhance the accuracy and robustness of fraudulent claims detection. Practical aspects such as model deployment through Application Programming Interfaces (APIs) and user-friendly Graphical User Interfaces (GUIs) are also considered for real-world implementation.


    The findings from this study hold immense potential for the insurance industry, leading to improved fraud prevention and more efficient claims processing. Moreover, the research underscores the importance of proactive strategies in combating insurance fraud, offering a substantial return on investment (ROI) for insurers. By leveraging machine learning techniques and adhering to data-driven methodologies, the industry can better protect its financial stability while preserving the trust of policyholders.

    Keywords -Insurance Fraud Detection, Machine Learning Algorithms, Deep Learning, Natural Language Processing (NLP), Anomaly Detection, ROC-AUC Evaluation.

    Accepted: 04/11/2022

    Published: 30/11/2022

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    To Study the Insurance Claims Detection Using Machine Learning






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  • DAKSHIN BIHAR GRAMIN BANK: REDEFINING THE DIGITAL BANKING EXPERIENCE WITH A UNIFIED UI

    Case Study

    Authors:

    1. Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.
    2. Poonam Gupta, Assistant Professor, Department of Computer Science and Engineering, MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Corresponding Author: Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Email ID: rishabharyan07052004@gmail.com

    Abstract:

    This case study details the comprehensive multichannel user interface (UI) design initiative undertaken for Dakshin Bihar Gramin Bank to elevate its digital banking ecosystem. As part of an ambitious digital transformation journey, the project focuses on two critical operational components to ensure a seamless, engaging, and consistent user experience across all digital touchpoints. First, the Online Bank Management System is enhanced to optimize core transactions, including account monitoring, fund transfers, bill payments, and financial management, by prioritizing intuitive navigation, accessibility, and robust security. Second, the Bank Locker Management System is revamped to simplify how customers access, manage, and secure locker services digitally. By integrating these systems into a unified, user-centric interface, the initiative aims to boost customer satisfaction, accelerate digital adoption, and establish new industry benchmarks for modern banking experiences.

    Keywords – Fintech, Dakshin Bihar Gramin Bank, Digital Banking Transformation, Multichannel UI Design, Online Bank Management System, Bank Locker Management System, User-Centric Interface, Financial Security Protocols.

    Accepted: 01/07/2022

    Published: 14/07/2022

    Author(s) Retains the Copyrights of This Article




    DAKSHIN BIHAR GRAMIN BANK: REDEFINING THE DIGITAL BANKING EXPERIENCE WITH A UNIFIED UI



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