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
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
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
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
Synthesis of Newer Benzotriazol Derivatives for Antibacterial and Antioxidant Potential
In response to pandemics and microbial resistance, novel heterocyclic compound spotent biological activity are needed. A series of (E)-2-(2-((5-(1H-benzo[d][1,2,3]triazol-1-yl)-3- methyl-1-phenyl-1 H-pyrazol-4-methylene(hydrazinyl)-4-(aryl) thiazole derivatives were synthesized via a three- component reaction involving pyrazole-4-carbaldehyde, thio semicarbazide, and substituted phenacyl bromides for antibacterial and antifungal study. Recent health research focuses on multifunctional compounds that interact with multiple biological targets, streamlining multidrug therapies and enhancing patient adherence. This study aimed to develop novel multifunctional chemical entities incorporating a benzothiazole nucleus, a structure widely recognized for its diverse biological activities. Benzothiazole has gained attention due to its role as a scaffold in various multifunctional drugs, making it a promising candidate for innovative therapeutic applications that improve treatment efficacy and simplify pharmaceutical regimens. To combat the growing threat of multi-resistant bacteria, scientists synthesized four benzotriazole and three benzimidazole derivatives using two distinct methods, recognizing the vital role of heterocyclic compounds in medicinal chemistry. These newly developed compounds were then docked with two protein targets, DNA gyrase (PDB ID: 2XCT and 3ILW), to evaluate their binding potential. The effectiveness of these derivatives was compared with standard antibacterial drugs, sparfloxacin and ciprofloxacin. This study aims to identify promising candidates for overcoming bacterial resistance, providing valuable insights into new drug development strategies targeting resistant bacterial strains through advanced molecular docking techniques. Antimicrobial resistance (AMR) is a global health challenge, leading to higher mortality, morbidity, and treatment costs. The World Health Organization (WHO) reported in 2019 that only six out of 32 antibiotics in clinical trials featured innovative novel moieties, while the rest were based on existing compounds. This highlights the urgent need for new antibiotic development to combat resistance. Among promising candidates, benzothiazole derivatives stand out due to their broad spectrum of biological activities and significant medicinal applications. Their potential in drug discovery has gained attention for addressing resistance issues, reinforcing the necessity of developing novel compounds. Advancing research in benzothiazole derivatives may pave the way for effective antimicrobial agents to tackle evolving resistance problems and improve global healthcare outcomes.
Published by: Sheevendra Singh Sibbu
Author: Sheevendra Singh Sibbu
Paper ID: V11I3-1182
Paper Status: published
Published: May 16, 2025
Next Step: Find the Next Step in your Career
Choosing the right academic specialisation is a pivotal decision in a student's educational journey and has a profound impact on their future career. However, many students struggle with this choice due to a lack of clarity about their interests, strengths, and the job market relevance of different specialisations. The "Next Step" project aims to bridge this gap by offering a data-driven, survey-based guidance system that helps students identify the most suitable specialization based on their interests and aptitudes. The system utilizes a structured questionnaire designed to assess key personal and cognitive traits, such as analytical thinking, creativity, and technical enthusiasm. Based on the responses, the system suggests the most relevant specialisation, such as Artificial Intelligence, Data Science, or Cybersecurity, and subsequently provides a curated list of corresponding job roles. The solution is implemented as a web application, offering students a seamless and interactive experience while also allowing administrators to manage job role data dynamically. This approach not only improves self-awareness among students but also aligns their academic direction with industry demand, thus reducing the skills gap. The "Next Step" platform exemplifies how interest-based guidance can be transformed into an effective educational tool through the integration of survey methodologies, web technologies, and dynamic data mapping. It lays a scalable foundation for future career guidance systems that are personalized, adaptive, and aligned with real-world opportunities.
Published by: Harshal Patil, Divyansh Dubey, Harsh Singh Parihar, Aditya Upadhye, Shahin Makubhai
Author: Harshal Patil
Paper ID: V11I3-1163
Paper Status: published
Published: May 15, 2025
