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Research Paper

The Impact of English language on different languages

This research paper examines the impact of English on different languages, German, Hindi and French. It looks at how English has evolved from being a regional language confined to a small area to a lingua franca through colonial expansion and globalisation. This research shows that the impact of English is neither uniform nor superficial. This paper highlights how the English language influences each of these three languages differently based on different historical, political, and socio-cultural contexts. This paper demonstrates how English simultaneously opens a path for global opportunities, but at the same time presents a challenge to linguistic integrity and heritage.

Published by: Aanya Chahal

Author: Aanya Chahal

Paper ID: V11I6-1189

Paper Status: published

Published: December 3, 2025

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Research Paper

Smart Home System Automation Based on Zigbee Protocol with Internal Cloud Server

The standard protocols used in educational, business, residential, and industrial environments in Indonesia still rely on standard protocols such as Wi-Fi and Bluetooth. This applied research is related to a standard protocol called Zigbee, which is IEEE 802.15.4 standard that has begun to be widely used in networks for IoT-based control purposes. In smart home technology, connectivity is very important. To build a smart home system, users must consider the connectivity or communication between one smart home device and another. The most familiar and widely used communication protocol network for smart home devices is currently Wi-Fi. However, alternative wireless connectivity protocols are now emerging, one of which is Zigbee. Zigbee is a global standard for low-power, short-range networks, offering a complete and operable Internet of Things (IoT) solution for homes and buildings. Zigbee has features that enable it to manage its own network and data exchange on the network, and it can support hundreds of devices and has reliable security features. In this applied research, monitoring uses the Zigbee protocol with a cluster topology consisting of a Zigbee coordinator and several Zigbee end devices that will be installed in locations with or without wall obstructions.

Published by: Muhammad Sulkhan Arif, Wawan Heryawan, bangbang Hermanto

Author: Muhammad Sulkhan Arif

Paper ID: V11I6-1226

Paper Status: published

Published: November 29, 2025

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Research Paper

Real-Time Detection of Obfuscated Abusive Multilingual Comments using Prompt-Tuned and LoRA Fine-Tuned LLMs

The rapid expansion of multilingual communication on social media has led to a surge in abusive, toxic, and offensive user-generated content. Traditional rule-based filters and monolingual detection systems fail to address modern linguistic challenges such as code-mixing (English–Hindi– Hinglish), transliteration variance, and obfuscation (e.g., “id!ot”, “b!tch”). To address these limitations, this work proposes a real-time multilingual abusive comment detection framework built using a LoRA-based parameter-efficient fine-tuning approach on Multi-lingual BERT (mBERT), augmented with a custom preprocessing pipeline and prompt-based linguistic normalization. The proposed system integrates leetspeak decoding, repeated-character normalization, slang expansion, context-driven abusive-pattern recognition, and Hindi transliteration handling, significantly improving classification robustness. The fine-tuning utilizes Low-Rank Adaptation (LoRA) to enable efficient domain adaptation without full-model training costs. A Flask-based REST API and Web Interface provide real-time detection capabilities with confidence scoring and content-restriction logic. Experiments show improved F1 scores on code-mixed and obfuscated datasets, demonstrating substantial gains over baseline mBERT and rule-based systems. Future work aims to extend the system to multimodal toxicity detection, emoji-semantic embeddings, and adversarial robustness.

Published by: Ayushi Gupta, Arjita Prajapati, Anushka Agrawal, Ayushi Uikey, Anamika Joshi

Author: Ayushi Gupta

Paper ID: V11I6-1224

Paper Status: published

Published: November 29, 2025

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Research Paper

Tackling Rural Healthcare Gaps: Intelligent Doctor Recommendation by Distil BERT with Coordinated Medicine Delivery

In rural healthcare, patients often struggle to navigate complex medical systems, leading to misdirected consultations and treatment delays. Traditional paper-based clinics further exacerbate inefficiencies with crowded queues and poor emergency handling. Current digital solutions typically offer only basic appointment booking or generic disease prediction, failing to provide personalized guidance or integrate the complete patient journey. Our AI-powered platform addresses these gaps using a fine-tuned Distil BERT model that analyses patient-described symptoms and recommends appropriate medical specialities with 83.8% accuracy. The system seamlessly integrates intelligent doctor matching with a token-based queue management system, real-time emergency SOS alerts, and digital prescription generation. This creates a comprehensive, patient-centric workflow from initial symptom assessment through treatment completion. Future enhancements will incorporate multi-lingual support, voice-input capabilities, pharmacy integration, and telemedicine modules to further expand accessibility and create a complete healthcare ecosystem for underserved communities.

Published by: Mansi Yadav, Mohammad Faizan, Nitin Singh Thakur, Neha Kumari

Author: Mansi Yadav

Paper ID: V11I6-1197

Paper Status: published

Published: November 26, 2025

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Research Paper

Visual Storytelling on Instagram: Redefining Digital Narratives in Indian Contexts

In contemporary India, Instagram has evolved into a powerful site of visual storytelling where images, reels, and aesthetic choices continually reshape how narratives are created, shared, and consumed. This paper explores how Indian creators—from rural artisans and independent journalists to lifestyle influencers and grassroots activists—use Instagram to express identity, culture, and socio-political consciousness. Moving beyond the platform’s commercialised image culture, the study highlights how Indian users adapt Instagram’s global features into localised storytelling traditions such as “darshana,” “kahani,” “dastangoi,” and “lok-drishti.” Through qualitative digital ethnography, content analysis of 200 Instagram accounts, and semi-structured interviews with 25 creators, this paper argues that Instagram in India is not merely a social media platform but a transformative narrative ecosystem. It enables micro-communities, democratizes content creation, and crafts new hybrid forms of cultural expression that blend imagery, captions, hashtags, and audio-visual cues. Findings reveal that Indian Instagram storytelling is shaped by regional languages, socio-economic aspirations, caste-class markers, digital literacy gaps, and platform algorithms that reward emotion-driven, relatable, and visually immersive content. The paper concludes with implications for digital culture, media literacy, and storytelling futures in India.

Published by: Vidhi Sikka, Mayank Arora

Author: Vidhi Sikka

Paper ID: V11I6-1190

Paper Status: published

Published: November 21, 2025

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Research Paper

Interpreting Opaque Scheduling Heuristics in Timetabling via Surrogate GNN

Opaque timetabling heuristics often deliver strong schedules yet remain difficult to interpret, limiting trust, diagnosis, and controlled adaptation in educational scenarios. This work presents a practical pipeline that learns a constraint-aware graph neural network (GNN) surrogate from input-output pairs of a black-box timetabling solver and then applies global explainability to characterize the surrogate’s decision-making logic across entities and relations in the context of timetabling. Using a synthetic dataset with hard constraints, the study evaluates both the assignment fidelity to the solver outputs and the feasibility under hard constraints, complementing these with global explanations and counterfactual sensitivity analysis. The results highlight which entities and relations most influence the predicted assignments. The pipeline is intended as a methods-oriented contribution that standardizes data generation, surrogate training, and explanation estimators for timetabling, enabling reproducible assessment of interpretability alongside fidelity and feasibility without claiming domain deployment readiness.

Published by: Kashish Ukey, Nakul Badwaik, Labdhi Soni, Ojas Kamde, Smita Nirkhi

Author: Kashish Ukey

Paper ID: V11I6-1159

Paper Status: published

Published: November 19, 2025

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