First Name
Apurba
Last Name
Nandi
Dataset Entries from this Author
This study explores the relationship between social media sentiment and stock market movements using a dataset of tweets related to various publicly traded companies. The dataset comprises time-stamped tweets containing company-specific information, stock ticker symbols, and company names. By leveraging natural language processing (NLP) techniques, we analyze the sentiment of tweets to determine their impact on stock price fluctuations. This research aims to develop predictive models that incorporate tweet sentiment and frequency as features to forecast stock price movements.
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This study explores the relationship between social media sentiment and stock market movements using a dataset of tweets related to various publicly traded companies. The dataset comprises time-stamped tweets containing company-specific information, stock ticker symbols, and company names. By leveraging natural language processing (NLP) techniques, we analyze the sentiment of tweets to determine their impact on stock price fluctuations. This research aims to develop predictive models that incorporate tweet sentiment and frequency as features to forecast stock price movements.
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This study explores the relationship between social media sentiment and stock market movements using a dataset of tweets related to various publicly traded companies. The dataset comprises time-stamped tweets containing company-specific information, stock ticker symbols, and company names. By leveraging natural language processing (NLP) techniques, we analyze the sentiment of tweets to determine their impact on stock price fluctuations. This research aims to develop predictive models that incorporate tweet sentiment and frequency as features to forecast stock price movements.
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The database compiled for this study is a comprehensive and meticulously curated repository designed to evaluate the efficacy of anti-VEGF therapy in patients with Diabetic Macular Edema (DME). It includes clinical and imaging data from 193 diabetic patients, aged 18-70 years, who participated in a single-center, randomized, parallelgroup, double-masked clinical trial. The database encompasses detailed demographic and clinical information, such as age, gender, medical history, duration of diabetes, and baseline measurements like blood pressure and intraocular pressure.
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The dataset we have used, provides hourly traffic counts from four distinct junctions, comprising a total of 48,120 observations. Each entry includes a timestamp (DateTime), a junction identifier (Junction), the observed vehicle count (Vehicles), and a unique identifier (ID). The data highlights real-world complexities, as the sensors at these junctions operated over varying durations. While some junctions offer consistently recorded data, others have sparse or irregular observations.
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Building meaningful connections between students and alumni is critical for enhancing students’ professional growth, career advice, and networking. Despite these benefits, traditional platforms often lack personalization and scalability, limiting their ability to meet diverse student needs. This paper presents an AI-driven approach to revolutionize student-alumni interactions wih career guidance by leveraging advanced recommendation systems.
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The dataset presents a treasure trove of historical stock market information, specifically concentrating on 29 of the 30 companies listed in the Dow Jones Industrial Average (DJIA). Regrettably, Visa (V) is missing due to incomplete data covering the past 12 years. This rich compilation includes well-known firms such as Apple (AAPL), Cisco (CSCO), IBM (IBM), Microsoft (MSFT), and Amazon (AMZN), making it an invaluable resource for anyone keen on stock market analysis.
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The process of data collection using functional Near-Infrared Spectroscopy (fNIRS) involves placing sensors with both a light source and detector on the scalp, typically spaced 3 to 5 centimeters apart. The light source emits infrared light that passes through the scalp and skull, while the detector captures the scattered light, providing insight into brain activity. In our study, participants wore a full-brain cap embedded with fNIRS sensors to measure oxygenated (HbO) and deoxygenated hemoglobin (HbR) levels, which reflect brain activity during cognitive tasks.
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