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Thesis

A Study to Assess the Effectiveness of Structured Teaching Program on Knowledge regarding Good Touch & Bad Touch among Children in Selected Schools of Distt. Mandi (H.P.).

The first sensory input in life comes from the sense of touch experienced by a baby while he is in the mother’s womb and feels protective touch experienced by all of us through childhood, adulthood and into older age. Parents, siblings, elders and friends play an important role in child development, and teachers also play an important role. Objectives: To assess the knowledge regarding good touch & bad touch among children. Material and Method: A quasi-experimental approach and pre-test and post-test control group design was adopted. Non-probability, a convenient sampling technique, was used to select 60 students. i.e. 30 in each experimental and control group. Data collection was done using a self-knowledge questionnaire. The collected data were analysed by calculating frequency, percentages, mean, standard deviation, chi-square, and t-test. Findings: The findings of the experimental group, a majority 93.3% (28children) of participants scored in the good category, and the remaining 6.7% (2children) were in the average range. None scored in the category below, indicating a strong positive impact of the intervention. In control group had only 0% in the good, category, while most 60% (18children) remained average, and 40% (12 children) still scored Below average, showing minimal improvement without intervention. Conclusion: It was concluded that the structured teaching programme on Good Touch and Bad Touch was an effective programme in improving the knowledge of the children, as depicted in the results, which showed a marked increase in post-test level of knowledge.

Published by: Neha Kumari, Pallavi Mehra, Isha Thakur

Author: Neha Kumari

Paper ID: V11I6-1218

Paper Status: published

Published: May 4, 2026

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Research Paper

CAMPULSE – A Platform Connecting Students, Events & Achievements

Efficient management of student memberships, events, attendance, certificates, feedback, and analytics is essential for the effective functioning of academic committees. CAMPULSE is a web-based ERP platform developed for the Computer Science and Engineering student community to integrate membership administration and event lifecycle management into a unified digital system. The platform supports academic-year-wise membership processing, online payment integration, unique membership ID generation, automated receipt creation, and validity tracking, thereby streamlining the overall membership workflow and reducing administrative burden. For event management, CAMPULSE enables administrators to create, schedule, and manage events, define seat limits, control registration windows, and monitor real-time seat occupancy. The system also incorporates QR-based participation mechanisms to simplify attendance verification and ensure accurate participation records. Additionally, modules for automated attendance capture, certificate generation, feedback collection, and report generation significantly reduce manual effort while minimizing the risk of human errors. The system provides role-based dashboards for both students and administrators, offering actionable analytics and real-time insights into memberships, registrations, participation trends, and event outcomes. By centralizing data and automating routine administrative operations, CAMPULSE enhances transparency, strengthens operational control, and saves valuable time for committee members. Overall, CAMPULSE offers a scalable, reliable, and efficient digital solution for academic committee management by replacing fragmented manual processes with an integrated ERP framework. The proposed system improves organization, accuracy, accessibility, and decision-making while enabling smoother and more effective event execution. It further supports better coordination among committee members by maintaining a centralized record of activities and outcomes. The platform also promotes consistency in communication, reduces duplication of work, and improves responsiveness during event planning and execution. This makes CAMPULSE a practical and future-ready solution for modern student committee operations overall.

Published by: Om D. Ninawe, Maithili S. Dhage, Nihal J. Bawankule, Vedant H. Borkute, Ashwini S. Galhat, Vipul V. Tayde, Chaitanya A.Betwar, Dr. A. A. Jaiswal, Prof. S. B. Verma

Author: Om D. Ninawe

Paper ID: V12I2-1274

Paper Status: published

Published: May 1, 2026

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Research Paper

A Proof-Carrying Medical Legal Right Advisor where Natural Language Generation is Intentionally Non-Intelligent

This paper presents PC-MLRA (Proof-Carrying Medical-Legal Rights Advisor), a deterministic system designed to provide structured awareness of patient rights and professional ethics in medical contexts. The system maps user queries to legally grounded responses through a fixed, rule-based pipeline without machine learning or generative language models. Responses are generated using predefined templates linked to a static legal knowledge base derived from the NHRC Charter of Patients’ Rights (2019) and the IMC Ethics Regulations (2002). Each output can include a structured proof trace that records the matched intents, activated clauses, and template selection, enabling auditability and reproducibility. The system is evaluated with respect to determinism, legal clause coverage, trace completeness, and safety under ambiguous queries.

Published by: Nambarm Athoiba Khuman, Naman Kumar Sharma, Soshya Joshi

Author: Nambarm Athoiba Khuman

Paper ID: V12I2-1266

Paper Status: published

Published: April 29, 2026

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Research Paper

Feminova – New Era of Women’s Health

Abstract: Feminova - A New Chapter in Women's Health. The new women's health app, Feminova, opens a window to women's health through a digitally designed environment for actual day-to-day use. Logging assures the perfect blend of easy and safe personal entry. Cycle information is organized correctly - in a tracker that over time understands the rhythm, shift, and pattern. Rather than relying on estimation, users are informed of the potential start days of the period and ovulation phases based on recorded dates. A clever assistant is embedded in the app, LLaMA2-powered and functioning offline to maintain the privacy of conversations. No internet search is required for body changes explanations. Misconceptions? They disappear more quickly when the facts are presented straightforwardly without the drama. Selected articles, not inundated ones, each one related to the health issues that women encounter, are accessible. Physical activity recommendations demonstrate that even minimal alterations in posture or everyday habits can lead to less pain. Support is readily available, and crisis numbers are present with one click if things get out of hand. In connection with the local community, the system stores users' data securely in a private data vault. Feminova, a product of smart learning mechanisms coupled with internet resources, is evidence of the amalgamation of technology with the personalized women's care of today's generation.

Published by: Meghna Pawar, Shravani Patil, Saloni Sawant, Arundhati Niwatkar, Rachana Dhanawat

Author: Meghna Pawar

Paper ID: V12I2-1259

Paper Status: published

Published: April 27, 2026

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Research Paper

News-Aware Stock Market Movement Prediction for India Retail Traders

Generally, retail investors have been experiencing various difficulties in handling financial markets due to the impact of ever-changing price movements in conjunction with ever-changing financial news. In normal circumstances of trading mechanisms, it is possible to observe historical price movements or sentiments. However, it is not possible to observe the contextual relationship between financial news and financial markets. Such cognitive complexities always affect decision-making in an unfavorable manner. In order to bridge the knowledge gap in this regard, this paper proposes the idea of developing a trading interpreter that considers financial news sentiments and financial market price data in an integrated manner. Natural language processing techniques have been used for developing a system that extracts sentiments from financial news articles. Sentiments are mapped with structured financial market price intervals. Feature engineering techniques have been used for developing financial news sentiments, price-based feature development, and interaction feature development that considers immediate reactions and lagged reactions of financial markets with respect to financial news

Published by: Arun Kumar K, Shan Shad M, Gokul Karthik G M, Anitha P

Author: Arun Kumar K

Paper ID: V12I2-1256

Paper Status: published

Published: April 27, 2026

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Research Paper

Detecting Misinformation in News Using BERT and Natural Language Processing

The widespread use of social media and online news platforms has made it easier for misinformation and fake news to spread rapidly. This creates serious challenges for individuals and organizations that rely on accurate information. To address this problem, this study proposes a fake news detection system that combines Natural Language Processing (NLP) techniques with both traditional machine learning and transformer-based models. The dataset used for the study is derived from the WELFake dataset, containing labeled news articles categorized as real or fake. Text preprocessing techniques such as tokenization, removal of noise, and normalization are applied to prepare the data. Traditional models like Support Vector Machine (SVM) and LightGBM use TF-IDF features to capture important word patterns, while DistilBERT is used to understand contextual meaning in text. The results show that transformer-based models achieve higher accuracy, while traditional models remain efficient and reliable. This hybrid approach improves the overall effectiveness of fake news detection systems.

Published by: Ankannagari Harshith Reddy, Tabitha Indupalli, Dinesh Ragipani, T.Dheeraj, Ch.Bhanu uday

Author: Ankannagari Harshith Reddy

Paper ID: V12I2-1258

Paper Status: published

Published: April 24, 2026

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