AI

 

The IEEE Hackathon on AI for Sustainable Agriculture is an integral part of the IEEE Africa Entrepreneurship Summit 2025, scheduled for 22nd-23rd May at the Crown Conference Hall in Kigali, Rwanda. This hackathon aims to foster innovation and collaboration among developers, researchers, and entrepreneurs to tackle pressing challenges in sustainable agriculture using artificial intelligence (AI). By leveraging AI, participants will create impactful solutions that address food security, resource efficiency, climate resilience, and farmer empowerment across Africa.

Last Updated On: 
Tue, 01/21/2025 - 11:40

This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJon

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Please cite the following paper when using this dataset:

Vanessa Su and Nirmalya Thakur, “COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations”, Proceedings of the IEEE 15th Annual Computing and Communication Workshop and Conference 2025, Las Vegas, USA, Jan 06-08, 2025 (Paper accepted for publication, Preprint: https://arxiv.org/abs/2412.17180).

Abstract:

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The rise in Generative Artificial Intelligence technology through applications like ChatGPT has increased awareness about the presence of biases within machine learning models themselves. The data that Large Language Models (LLMs) are trained upon contain inherent biases as they reflect societal biases and stereotypes. This can lead to the further propagation of biases. In this paper, I establish a baseline measurement of the gender and racial bias within the domains of crime and employment across major LLMs using “ground truth” data published by the U.S.

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The research team conducted logistic and Cox regression according to the behavioral data of gastroesophageal reflux disease patients who had long been drinking caffeinated coffee drinks, and determined the sensitivity and mathematical rationality of AI prediction model in behavioral science, which can support the research team to build a deep learning neural network and complete the prediction of gastrointestinal tract involvement.

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This paper conducts a systematic bibliometric analysis in the Artificial Intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 8,322 papers from the Web of Science (WoS) database, spanning 1990 to 2023.

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This paper conducts a systematic bibliometric analysis in the Artificial Intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 3,061 papers from the Web of Science (WoS) database, spanning 1994 to 2023.

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"LaneVisionIITR: A Comprehensive High-Resolution Dataset for Lane Detection recorded at IIT Roorkee ", which is a newly built high-resolution dataset for developing Lane detection dataset for advanced driver assistance systems. 

This folder consists of three files for each image: 

1. The image captured in .jpg format.

2. Annotations (.json) having left and center line coordinates represented as “L” and “C” respectively.

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