Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15 million deaths annually. Accurate diagnosis and routine monitoring of these diseases by auscultation are crucial for early intervention and treatment. However, auscultation using a conventional stethoscope is low in amplitude and subjective, leading to possible missed or delayed treatment. My research aimed to develop a stethoscope called SmartScope powered by machine-learning to aid physicians in rapid analysis, confirmation, and augmentation of cardiopulmonary auscultation. Additionally, SmartScope helps patients take personalized auscultation readings at home effectively as it performs an intelligent selection of auscultation points interactively and quickly using the reinforcement learning agent: Deep Q-Network. SmartScope consists of a Raspberry Pi-enabled device, machine-learning models, and an iOS app. Users initiate the auscultation process through the app. The app communicates with the device using MQTT messaging to record the auscultation, which is augmented by an active band-pass filter and an amplifier. Additionally, the auscultation readings are refined by a Gaussian-shaped frequency filter and segmented by a Long Short-Term Memory Network. The readings are then classified using two Convolutional Recurrent Neural Networks. The results are displayed within the app and LCD. After the machine-learning models were trained, 90% accuracy for cardiopulmonary diseases was achieved, and the number of auscultation points was reduced threefold. SmartScope is an affordable, comprehensive, and user-friendly device that patients and physicians can widely use to monitor and accurately diagnose diseases like COPD, COVID-19, Asthma, and Heart Murmur instantaneously, as time is a critical factor in saving lives.

Glaciers cover nearly 10 percent of the earth’s surface but are melting at an inexorable rate. According to the Pacific Standard magazine, the Arctic Sea ice has lost 80 percent of its volume since 1979. Antarctica’s ’Doomsday Glacier’ is melting faster and could raise global sea levels by two feet. As three-quarters of the earth’s fresh water is stored in glaciers, its melting depletes freshwater resources for millions of people. Glaciers also play a huge role in the climate crisis. Silica microspheres are promising materials to prevent glacier melting as it reflects most of the sun’s radiation. When spread in layers over the glacier, it can slow the rate of melt and aid in new ice formation. However, it is necessary to determine the ideal amount of silica to achieve the desired result with minimum environmental impact. This paper introduces a novel method SPF ICE to determine the optimal amount of silica based on glacier’s properties using reinforcement learning agents and a custom OpenAI Gym environment. The environment simulates a real-world model of a glacial setting using specific data, such as the glacier’s mass balance, temperature, and average accumulation and ablation. After testing the agents, the proposed solution reduced glacial melting by an average of 60.40% using the optimal amount of silica. The results indicate SPF ICE is a promising and cost-effective solution to curb glacier melting.

Glaciers cover nearly 10 percent of the earth’s surface but are melting at an inexorable rate. Antarctica’s Doomsday Glacier’ is melting faster and could raise global sea levels by two feet. As three-quarters of the earth’s fresh water is stored in glaciers, its melting depletes freshwater resources for millions of people. Glaciers also play a huge role in the climate crisis. Silica microspheres are promising materials to prevent glacier melting as it reflects most of the sun’s radiation. When spread in layers over the glacier, it can slow the rate of melt and aid in new ice formation. However, currently, no modeling is available to show the amount of silica needed and its effectiveness in advance. This paper introduces a novel method SPF ICE that models the silica amount based on glacier’s properties by testing reinforcement learning agents in a custom OpenAI Gym environment. The environment simulates a real-world model of a glacial setting using specific data, such as the glacier’s mass balance, average accumulation, and ablation. After testing RL agents like DQN and SARSA, the proposed solution modeled the silica amount that reduced glacial melting by an average of 60.40% extending its lifetime by many years. The results indicate SPF ICE is a promising and cost-effective solution to curb glacier melting.