Revista Latinoamericana de la Papa

Category: Volume 1 (2026)

It contains the newest research, studies, or articles released by the publisher.

  • 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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