This research explores the integration of advanced AI technologies, specifically Google's Gemini-1.5-Flash-001 model and Vertex AI, into the development of EcoTrack-a mobile application that helps users monitor an...
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This research explores the integration of advanced AI technologies, specifically Google's Gemini-1.5-Flash-001 model and Vertex AI, into the development of EcoTrack-a mobile application that helps users monitor an...
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ISBN:
(数字)9798350355611
ISBN:
(纸本)9798350355628
This research explores the integration of advanced AI technologies, specifically Google's Gemini-1.5-Flash-001 model and Vertex AI, into the development of EcoTrack-a mobile application that helps users monitor and reduce their carbon emissions. At the core of this innovation is Ecobot, an AI-powered chatbot embedded within EcoTrack, designed to deliver personalized, actionable insights on sustainability. by leveraging Gemini's capabilities alongside real-time data through Vertex AI, Ecobot enhances user engagement by offering tailored advice on reducing their carbon footprint. The study illustrates how Ecobot processes various data points, including user input and environmental metrics, to generate precise recommendations for users looking to make more sustainable choices. The focus of this work is on the importance of scalable AI frameworks that can adapt to evolving sustainability goals while efficiently managing real-time data. Additionally, this paper addresses the ethical dimensions of using AI for environmental purposes, discussing its potential to drive meaningful action at both the individual and societal levels, contributing to the global effort to combat climate change.
In recent years, there has been a significant increase in interest in medical health applications using Wireless body Sensor Networks (WbSN). Monitoring people health conditions is crucial activity of today's heal...
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In recent years, there has been a significant increase in interest in medical health applications using Wireless body Sensor Networks (WbSN). Monitoring people health conditions is crucial activity of today's heal...
In recent years, there has been a significant increase in interest in medical health applications using Wireless body Sensor Networks (WbSN). Monitoring people health conditions is crucial activity of today's healthcare system. As the future state of health is a consequence of past state, we induce data mining approaches on the window of past signals to predict worst health condition coming in the future. We propose the combinatorial real-time clustering and classification module, in which the future health risk would be predicted in real time. Also to enable continuous monitoring of the patient in real time, we are introducing mobile healthcare system by which the patient analysis and future predictions are carried out and informed to the patient by physician. The proposed Online Distribution Resource Aware (ODRA) algorithm has demonstrated superior accuracy and a decreased rate of False Positives (FPR) in predicting the risk status of 64 patients with varying illnesses using their Heart Rate (HR), Oxygen saturation (SpO2), and blood Pressure signals. This performance is in comparison to the Resource Aware High-Quality Clustering (RAH) algorithm. This indicates that ODRA can be a promising algorithm for healthcare professionals to better assess the risk status of their patients and provide more accurate diagnoses and treatment plans.
The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 m...
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The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 million people have been screened, in which the infection has made more than 2.7 million people positive, in which 182,740 people have died due to infection. More than 80 countries have closed their borders from transitioning countries, ordered businesses to close, instructed their populations to self-quarantine, and closed schools to an estimated 1.5 billion children. The world’s top ten economies such as the United States, China, Japan, Germany, United Kingdom, France, India, Italy, brazil, and Canada stand on the verge of complete collapse. In addition, stock markets around the world have been pounded, and tax revenue sources have fallen off a cliff. The epidemic due to infection is having a noticeable impact on global economic development. It is estimated that by now the virus could exceed global economic growth by more than 2.0% per month if the current situation persists. Global trade may also fall from 13 to 32% depending on the depth and extent of the global economic slowdown. The full impact will not be known until the effects of the epidemic occurred. This research analyses the impact of COVID-19 on the economic growth and stock market as well. The aim of this research is to present how well COVID-19 correlated with economic growth through gross domestic products (GDP). In addition, the research considers the top five other tax revenue sources like S&P500 (GPSC), Crude oil (CL = F), Gold (GC = F), Silver (SI = F), Natural Gas (NG = F), iShares 20 + Year Treasury bond (TLT), and correlate with the COVID-19. To fulfill the statistical analysis purpose this research uses publically available data from yahoo finance, IMF, and John Hopkins COVID-19 map with regression models that revealed a moderated positive correlation between them. The model was
Semantic Index, Human Action Detection, and Event Detection are video surveillance packages that assist automate surveillance tasks. Video surveillance structures have entered the generation of virtual surveillance st...
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In the process of medication, it is a common practice to treat patients with saline for dehydration and other medical ailments to improve the health condition of the patients. When fed with saline continuous observati...
In the process of medication, it is a common practice to treat patients with saline for dehydration and other medical ailments to improve the health condition of the patients. When fed with saline continuous observation of nurses is mandatory in monitoring the level of the saline. There are many cases where patients are being harmed due to the staff inattentiveness, as their absence does not notice the completion of saline level in the container. This arise the problem of back flow of blood immediately after the completion of saline in container. Hence to protect the patient from getting harmed an IoT based saline level monitoring system has been developed. The proposed model incorporates a sensor which continuously detects the saline drops. Whenever the sensor does not detect the drops for a certain interval it alerts the staff of the hospital with the buzzer, helping to monitor the safety of the patients.
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