Wearable ECG Monitoring with Embedded Deep Learning for Real-Time Arrhythmia Detection
ardiovascular diseases remain a leading cause of death worldwide, and arrhythmia, an irregularity in heart rhythm, demands timely detection. Conventional ECG monitoring is bulky, hospital-bound and computationally heavy, which limits continuous monitoring in everyday settings. This paper presents an IoT-based arrhythmia detection system that combines a lowcost wearable ECG front end with a lightweight deep learning model running directly on the sensor node. Biopotentials are acquired with ECG electrodes, conditioned by an AD8232 analog front end, digitised and processed by an ESP32 microcontroller, and classified by a one-dimensional convolutional neural network (DL-LAC) into five heartbeat classes: Normal (N), Supraventricular (S), Ventricular (V), Fusion (F) and Paced (P). Because the network learns features directly from raw single-lead ECG, no manual feature engineering is required. The model, trained on the MIT-BIH Arrhythmia Database, reaches approximately 96.2% overall accuracy, with on-device inference latency of about 80 ms per window and a memory footprint below 500 KB. Duty cycling reduces power consumption by about 40%, extending battery life from four to over six hours. The results indicate that on-device deep learning is a practical route to continuous, low-latency and privacy-preserving cardiac monitoring.
Published by: Ms. Subha P S
Author: Ms. Subha P S
Paper ID: V12I5-1185
Paper Status: published
Published: October 9, 2026
A Simple Dynamic GPS Attendance System for College Classrooms Without Dedicated Hardware
Taking attendance in college classes usually wastes lecture time or requires costly hardware like fingerprint readers, RFID cards, or Bluetooth beacons. Simple web forms are cheaper, but students can easily share access links or class codes with absent friends who punch in from outside the room. This paper presents a practical, low-cost attendance system that needs no extra hardware or paid server hosting. The teacher’s mobile phone serves as the location anchor. When an attendance session opens, the app records the teacher’s GPS coordinates. Students then open the web portal on their own phones, select their name, type in a temporary 6-digit code shown on the board, and fetch their device location. The system calculates the distance between the student and the teacher using the standard Haversine formula. If the student is within a preset boundary (such as 35 to 50 meters), the punch is recorded as present. We tested the system with a class of 78 engineering students in a multi-story concrete college building in Kerala. The test verified 98.7% of in-class submissions accurately while blocking students outside the boundary. The app also prevents duplicate submissions from the same phone, giving colleges an easy, free way to run quick roll calls.
Published by: Prasobh John
Author: Prasobh John
Paper ID: V12I5-1177
Paper Status: published
Published: October 9, 2026
Powering Micro-Businesses: A Theoretical Analysis of Electricity Reliability, Productivity and Capital Investment in Maharashtra
This paper examines how electricity reliability may affect the operating hours and capital investment decisions of micro-businesses in Maharashtra, India. While access to electricity has expanded substantially, having a grid connection does not necessarily guarantee a reliable or predictable supply. For micro-businesses operating with limited financial resources, frequent power interruptions and poor power quality can reduce productive operating time, leave electricity-dependent equipment underutilised, increase operating costs and create uncertainty around future investment. The study adopts a theoretical secondary research approach, drawing on existing literature on electricity reliability, firm productivity, capacity utilisation, infrastructure and investment under uncertainty. Rather than estimating a direct causal relationship using original business-level data, the paper synthesises these strands of literature to develop a conceptual framework for understanding how electricity reliability can influence micro-business decisions. The analysis focuses on two main channels: the effect of electricity reliability on productive operating time and capacity utilisation, and its potential effect on the expected returns and risks associated with capital investment. The paper argues that the economic value of electricity cannot be fully captured by measuring access alone. For micro-businesses, the reliability and predictability of supply may influence how effectively existing productive resources are used and the conditions under which further investment becomes worthwhile. The study concludes by considering the implications for electricity and MSME policy in Maharashtra and identifying areas for future empirical research.
Published by: Kavya Mehta
Author: Kavya Mehta
Paper ID: V12I5-1176
Paper Status: published
Published: October 3, 2026
A Study to Assess the Effectiveness of Structured Teaching Program on Knowledge Regarding Osteoporosis Among Post-Menopausal Women of Selected Areas of Distt. Mandi (H.P)
Abstract: Osteoporosis is a silent, progressive disease characterized by decreased bone mass and structural deterioration, leading to an increased risk of fractures. Postmenopausal women are particularly vulnerable due to hormonal changes, especially the decline in estrogen levels. In India, the prevalence of osteoporosis is high among post-menopausal women, particularly in rural areas where awareness and access to preventive healthcare are limited. Educational interventions like structured teaching programs can help bridge the knowledge gap and promote preventive practices. Aim :To assess the effectiveness of a structured teaching program on knowledge regarding osteoporosis among post-menopausal women in selected areas of District Mandi, Himachal Pradesh. Materials and Methods: A pre-experimental, one-group pretest/post-test designwas adopted. A total of 100 post-menopausal women were selected through non- probability purposive sampling. A structured questionnaire was used to assess their knowledge regarding osteoporosis before and after the intervention. The structured teaching program included lectures, visual aids, and demonstrations related to osteoporosis prevention. Data were analysed using descriptive and inferential statistics; the effectiveness of the intervention was tested using the paired t-test, and associations with demographic variables were analysed using the chi-square test. Findings: Thepretest findings revealed that 5% had good knowledge ,29% ofwomen had poor knowledge, 66% had average knowledge. After the intervention, 62% demonstrated good knowledge, 38% had average knowledge, and none had poor knowledge. The mean knowledge score increased from 13.56 (SD = 4.76) in the pretest to 22.20 (SD = 3.43) in the post-test.The paired t-test showed a statistically significant improvement (t = 14.28, p = 0.00001*). A significant association was found between knowledge levels and variables such as occupation and type of family. Conclusion: The structured teaching program was effective in significantly enhancing the knowledge of post-menopausal women regarding osteoporosis. Educational interventions can play a critical role in promoting awareness and preventive practices, especially in rural settings where health literacy is low.
Published by: Mrs. Shivani, Mrs. Priyanka sharma, Mrs. Sunita devi
Author: Mrs. Shivani
Paper ID: V12I5-1172
Paper Status: published
Published: September 30, 2026
Stocks, Bonds, Gold, and Bitcoin: A Comparative Analysis of Risk-Adjusted Returns from 2022 to 2024
This paper examines the relationship between U.S equities, gold, Bitcoin and short term government debt over the three years from January 2022 to December 2024, a period defined by the fastest Federal Reserve rate hiking cycle in four decades, the beginnings of a cutting cycle, alongside the January 2024 approval of spot Bitcoin exchange-traded funds. Using a monthly return rate data and a risk-free rate derived from Treasury bill yields, this study calculated Sharpe and Sortino ratios, a six-month rolling beta for Bitcoin and gold against the S&P 500 as well as a pairwise correlation matrix across all three price based assets which is set against the Federal Funds Effective Rate as a contextual overlay. The findings show that gold recorded the highest risk-adjusted return among the three assets. This was the case for both the Sharpe and Sortino ratios. Bitcoin’s beta against the S&P 500 also changed significantly during the period studied. It moved from below zero to above four within a few months. This increase occurred during the period following the approval of the Bitcoin ETFs. However, Bitcoin’s beta later fell again as the Federal Reserve approached its first rate cut in late 2024. Bitcoin’s correlation with the S&P 500 (0.571) was substantially stronger than gold’s (0.243). In comparison, Bitcoin and gold themselves had only a weak correlation of 0.299. Overall, these results suggest that Bitcoin behaved more like a volatile and equity-adjacent asset rather than an independent store of value during this period. The study connects this pattern to prospect theory and loss-averse behaviour.
Published by: Hrehaan Anand
Author: Hrehaan Anand
Paper ID: V12I5-1170
Paper Status: published
Published: September 25, 2026
Can Derivative-Based Hedging Improve the Risk-Adjusted Returns of Portfolios Holding Alternative Investments?
The inclusion of alternative investments in modern portfolio allocation can significantly increase diversification because investors want returns that are not fully explained by traditional equity and fixed-income holdings. Alternative investments are here defined as financial assets outside stocks, bonds and cash (e.g., hedge funds, private equity, private credit, real estate, commodities and infrastructure). Although these assets improve diversification and have attractive return potential they also come with risks which are often harder to observe or manage than those of traditional securities such as market risk, liquidity risk, credit risk, leverage risk, currency risk and interest rate rise plus non-linear/asymmetric return distributions. Derivatives like futures, options, forwards and swaps allow investors to hedge specific components of this risk without liquidating the underlying positions. This review synthesizes existing literature on derivative-based hedging to test whether it improves risk-adjusted returns of portfolios containing alternative investments. The literature suggests that hedging effectiveness depends on the relationship between the hedging instrument and the exposure being hedged, hedge ratio achieved, investment horizon and market conditions. Hedging costs are incurred, volatility reduction does not translate into improved risk-adjusted return because hedging also requires upside participation so volatility reduction doesn’t lead to higher risk adjusted return. From hedge funds, commodities and equity-linked strategies evidence shows that hedging is generally more reliable where underlying risk is systematic and hedging instrument tracks exposure closely but less reliable where risk is idiosyncratic, illiquid or hard to observe which is common in private equity and private credit. In general the literature seems to indicate that derivative-based hedging can enhance risk-adjusted returns of alternative-investment portfolios only if it is applied to identifiable systematic and hedgeable risks, costs are taken into account and so on. Hedging should therefore be viewed as a tool for managing risk exposure and improving efficiency in portfolio management rather than as a guaranteed means of increasing dollar returns.
Published by: Himanshu Garg
Author: Himanshu Garg
Paper ID: V12I5-1151
Paper Status: published
Published: September 23, 2026
