An In-Depth Analysis of Dollar Liquidity in the Global Economy
As the US dollar is the basis of international finance and trade, dollar liquidity is vital to the health of the economy. Developing countries such as India feel the brunt of less dollar access through higher import costs, volatile currencies, and reduced corporate competitiveness. Cross-border banks, upon which the availability of dollar financing depends, are also at risk and may produce credit shortages. United States policy making can rock global markets, as was done with the 2008 Financial Crisis and the 2013 Taper Tantrum. The paper puts emphasis on stable dollar liquidity by emphasising the complexity of the global economy and how dislocation of dollar flow impacts banks, companies, and individuals everywhere.
Published by: Jaanya Rathi
Author: Jaanya Rathi
Paper ID: V11I3-1279
Paper Status: published
Published: June 1, 2025
Thyroid Gland Abnormality Detection Using Pre-Trained Neural Networks
Medical image analysis plays a crucial role in the early detection and diagnosis of thyroid nodules, which are indicative of various thyroid illnesses. Thyroid nodules are classified using machine learning methods like Random Forest and Support Vector Machine in the current framework. In this work, we propose a unique use of transfer learning algorithms to thyroid nodule categorization. Neural network models that have already been trained on large datasets are modified for specific tasks that require less data through the use of transfer learning. Our approach involves using a state-of-the-art convolutional neural network (CNN) that has been pre-trained on a range of medical pictures to extract significant information from thyroid ultrasound scans. To optimize its performance for accurate classification, the model is trained on a particular dataset of thyroid nodule images. We examine the effectiveness of many transfer learning architectures, such as VGG16 and Xception CNN, and assess their overall accuracy, sensitivity, and specificity. The proposed methodology aims to provide physicians with a reliable thyroid problem diagnosis tool by increasing the categorization efficiency of thyroid nodules. The results pave the way for more precise thyroid image analysis, diagnosis by demonstrating how transfer learning can be utilized to maximize model performance even in the presence of sparsely labelled medical data.
Published by: B. Madhu Varshini, S. Sridevi, G. Kokila
Author: B. Madhu Varshini
Paper ID: V11I3-1256
Paper Status: published
Published: May 30, 2025
Invisible Economies: The Gendered Burden and Cultural Dimensions of Unpaid Labour
This paper critically examines the pervasive issue of unpaid labour through a gendered lens, focusing on its systemic normalization and deeply entrenched roots in patriarchal traditions. Primarily undertaken by women, unpaid labour includes caregiving, household maintenance, and community service—tasks essential to the functioning of society yet systematically excluded from economic valuations and policy recognition. Drawing on feminist economic theory, particularly the work of Marilyn Waring, the paper explores how unpaid work perpetuates gender inequality by limiting women's access to education, employment, and leadership roles. Cultural contexts, especially in South Asia, further entrench these roles, framing domestic work as a woman's natural duty. The discussion incorporates cross-cultural comparisons, highlighting how traditions, economic transformations, and evolving gender norms affect perceptions of labour equity. Additionally, the mental health ramifications of this invisible burden are analysed, revealing a gendered gap rooted in structural inequities and societal expectations. By exposing the fiction of the "head of household," the paper advocates for an equitable redistribution of unpaid work, challenging outdated norms and emphasizing the shared responsibility of dismantling patriarchal labour divisions. Recognising and valuing unpaid labour is crucial not only for women's empowerment but for redefining partnerships and societal well-being at large.
Published by: Samara Khanduja
Author: Samara Khanduja
Paper ID: V11I3-1254
Paper Status: published
Published: May 30, 2025
Smart Wheel Bot: An IoT-Driven Obstacle Avoidance System for Wheelchairs
Like many other sectors, the medical field in India is not widely known for its automation. Even in contemporary society, people with physical disabilities often rely on a caregiver for movement assistance. However, caregivers may be busy attending to other responsibilities and obligations, which can leave patients feeling stuck and dependent. To solve this problem, we designed an autonomous wheelchair which further enhances safety and facilitates greater independence in mobility. The Smart Mobility Bot is an economical autonomous wheelchair with decently priced features. It is controlled by DC motors and employs ultrasonic sensors for detecting obstacles.
Published by: Surya J, Swetha S, Vinayaga Moorthi M A
Author: Surya J
Paper ID: V11I3-1246
Paper Status: published
Published: May 29, 2025
Real-Time Bicep Curl Tracking and Pose Detection Using OpenCV and Media-Pipe
Human pose estimation is crucial for enabling real-time monitoring of physical exercise via the analysis of movement and orientation of the body. However, existing pose estimation techniques are prone to major flaws such as mislocalization of joints, occlusion issues, and mis-recognition of repetition of exercises. Such flaws undermine the efficacy and reliability of fitness tracking systems. In an attempt to address these flaws, the present study proposes a real-time bicep curl tracking system based on OpenCV and MediaPipe. The proposed system is designed to accurately estimate human pose, calculate joint angles, and provide automatic user feedback. One of the system's basic features is that it uses a state-based repetition counter, which improves accuracy in repetition detection by eliminating false positives caused by minor landmark placement variation. The system only detects repetitions when form is proper and range of motion is full. In addition to providing real-time feedback on posture changes and detecting improper exercise form, the system effectively eliminates the risk of injury during the execution of strength training exercises. It provides real-time feedback on posture changes and incorrect exercise form. Through empirical analysis, the system proposed has a remarkable accuracy of 96% in quantifying repetitions, which outperforms the performance of the traditional pose tracking models. The high accuracy verifies the system's robustness as well as its usability in real-world fitness applications. Findings indicate that the integration of AI-driven pose estimation and feedback mechanisms can potentially make personalized fitness training much more effective. Together with real-time correction and individualized data, these technologies can improve efficiency in training while motivating safer training habits. This work contributes to the growing field of AI-driven health and fitness technology and opens the door to more advanced and responsive physical activity monitoring devices
Published by: Shiv Arora, Drishti Sharma, Shubh Mudgal, Sudhanshu Chaudhary
Author: Shiv Arora
Paper ID: V11I3-1257
Paper Status: published
Published: May 28, 2025
EV BMS with Charge Monitoring and Fire Protection
In the silent heartbeat of our electrified era, lithium-ion batteries hum with potential and peril, their compact chemistry balancing progress on the knife’s edge of combustion. As thermal runaway lurks—unseen, unbidden, catastrophic—the Battery Management System (BMS) emerges not as a passive overseer but as an intelligent, multi-layered oracle of prevention, prediction, and protection. Within this chaotic choreography of heat, gas, pressure, and current, sensors become storytellers, whispering the earliest murmurs of disaster; algorithms, trained on the echoes of past failures, thread together anomalies into foresight; and suppression technologies, ever-vigilant, stand ready to suffocate the spark before it breathes. This paper explores the hybrid symphony of emergent AI, sensor fusion, and real-time control systems, where layered architectures form not just circuits, but cybernetic guardians. No longer are BMS mere managers; they are sentinels, anatomies of foresight crafted in silicon and code, promising not just energy, but safe, self-aware power in a world increasingly defined by its electric pulse.
Published by: Navajeevan, Rakesh, Sandeep M, Vishal T, Tenson Jose
Author: Navajeevan
Paper ID: V11I3-1251
Paper Status: published
Published: May 28, 2025
