Manuscripts

Recent Papers

Research Paper

Geometrical Approach to Kepler’s Laws of Planetary Motion

We know that the earth is a planet revolving round the sun is an elliptical orbit, the sun being at the focus. The time taken by the earth to complete one revolution is called an year which is equal to 365.25 days relative to the earth the sun describes an ellipse round the earth. The elementary pen and string method to draw ellipse has been devised to examine planetary orbits on the basis of the Kepler's Laws. Besides qualitative feature of the orbits. Quantitative depends of the orbital shape on the quantities appearing in the Kepler's Laws can also be analyzed with simple geometrical procedures. The method thus provides a relevant intermediate step to students prior to the study of the rigorous theory of central force problems. The students were asked questions relating to Kepler's three laws of motion, as well as what keeps planets in orbit around the sun. Less common ideas include a mix of circular and highly elliptical orbital shapes. Many students have conceptions consistent with the Kepler's second and third laws of motion and the case with which the models are adopted by students may suggests some ways to teach these concepts the types of ideas about orbital shapes and orbital behavior may originate in common depictions of orbits often seen in print and on the internet.

Published by: Ram Saroj Sah

Author: Ram Saroj Sah

Paper ID: V11I3-1186

Paper Status: published

Published: May 20, 2025

Full Details
Research Paper

Development and Optimization of Pumpkin Pomace Enhanced Savory Crackers

The development of functional food products using agro-industrial by-products such as pumpkin pomace is a sustainable and nutritious approach. This study focuses on the formulation of fiber-rich savory crackers enhanced with wet pumpkin pomace, blended with wheat flour and carom seeds. Pumpkin pomace, rich in dietary fiber and β-carotene, was incorporated to improve the nutritional profile without compromising sensory quality. The optimized formulation was evaluated for its physical, textural, and nutritional properties, including moisture, fat, ash, pH, protein, and fiber. Results indicated a significant enhancement in dietary fiber and protein, with acceptable sensory scores. This study supports the use of fruit and vegetable residues in mainstream food formulations to promote health, reduce food waste, and improve sustainability.

Published by: Y. Noor E Nazneen, Dr. A. Swaroopa Rani, G. Vikram Goud

Author: Y. Noor E Nazneen

Paper ID: V11I3-1192

Paper Status: published

Published: May 20, 2025

Full Details
Research Paper

A Smart Medicine Reminder System Using React Native and Timezone-Aware Notifications for Personalized mHealth Assistance

Medication non-adherence contributes to nearly 125,000 preventable deaths annually and accounts for approximately 10% of hospitalizations globally. While existing mobile health (mHealth) solutions provide basic reminders, they often overlook key factors such as time zone differences, dosage schedules, and UI accessibility. This paper presents a cross-platform medicine reminder system built using React Native, with time zone-aware scheduling and personalized notification logic. The system leverages a Node.js backend with MongoDB for dynamic user and medicine tracking, and cron-based scheduling for precision delivery. Early-stage testing indicates significant improvement in reminder accuracy across different time zones and positive user feedback on usability. This work contributes a scalable, open-source solution aimed at enhancing medication adherence for diverse populations.

Published by: Nandigama Prashanth Kumar

Author: Nandigama Prashanth Kumar

Paper ID: V11I2-1359

Paper Status: published

Published: May 17, 2025

Full Details
Research Paper

Gender Disparities in Employment

Gender inequality in employment describes barriers to accessing opportunities in, and the treatment offered in the workplace. These disparities can result in pay gaps, lower representation of women in leadership roles, and a stagnating economy. Gender inequality in employment restricts a country’s full economic potential and sustains or elevates social inequalities. This study assesses gender disparity in employment in India on a zone-wise basis, by reviewing the NSDP, and gender-based labour force participation and unemployment from 2011 to 2024. The research utilizes publicly available data from government-sourced employment datasets such as the PLFS and MOSPI. The findings indicate various disparities in work engagement rates by regions and gender. Regression models assess the influence of male and female participation on the economic output by state. The study supplements fixed effects with year, allowing the study to examine whether states including females in the labour pool favourably correlated with inclusive economic performance. Overall, the study found that female labour participation was positively correlated with economic output under fixed effects with year. Urban areas typically have a higher full employment unemployment (UE) rate for females, against a backdrop of increased educational access to women, and the North-East demonstrates enhanced gender participation even with lower NSDP. In aggregate, the study identifies that structural changes, social changes, and natural, smart, and inclusive gender-based policy changes are essential to encourage equitable growth and to benefitably use a society’s economic potential.

Published by: Yashi Garg, Priyonkon Chatterjee

Author: Yashi Garg

Paper ID: V11I3-1154

Paper Status: published

Published: May 17, 2025

Full Details
Research Paper

Glaucoma Detection through Deep Learning on Fundus Images

Glaucoma is a leading cause of irreversible blindness worldwide, often progressing without noticeable symptoms until significant vision loss occurs. Early detection is critical to prevent permanent damage, but conventional screening methods are time-consuming and require expert interpretation. In recent years, deep learning has emerged as a powerful tool in medical image analysis, offering promising solutions for automated and accurate glaucoma detection. This paper explores the application of deep learning techniques, particularly convolutional neural networks (CNNs), to detect glaucoma from retinal fundus images. A curated dataset of labeled fundus images is used to train and evaluate the model, achieving high accuracy in distinguishing glaucomatous eyes from normal ones. The study highlights the potential of deep learning to enhance the efficiency and accessibility of glaucoma screening, paving the way for real-time clinical decision support systems. Future directions include improving model generalizability across diverse populations and integrating multimodal data to further boost diagnostic performance.

Published by: Patnam Rakesh, Thalari Surya Ajay Kumar, Sheeba, Dr. Sundara Rajulu Navaneethakrishnan

Author: Patnam Rakesh

Paper ID: V11I3-1173

Paper Status: published

Published: May 16, 2025

Full Details
Research Paper

Largest Convex Quadrilateral in a Terrain

This paper discusses my understanding, implementation and analysis of various techniques for finding the largest convex quadrilateral inside a terrain. we present a simple new linear time alogrithm for finding the quadrilateral of largest area contained in a convex polygon. A near quadratic time algorithm to locate a largest area convex quadrilateral inside a terrain is presented in this paper.A terrain is a type of simple polygon that is bounded by:- A monotone polygonal chain (usually the upper or lower boundary), and A straight line segment (usually the base or bottom boundary).It is a subclass of simple monotone polygons and is widely studied in GIS(Geographic information system), data modeling, and computational geometry. In this paper we present a novel algorithm to find a largest area of convex quadrilateral inside a terrain with n vertices that run in O(n log2 n) times.

Published by: Himanshu Kumar Sah

Author: Himanshu Kumar Sah

Paper ID: V11I3-1181

Paper Status: retracted

Submitted: May 16, 2025

Full Details Track Status