Artificial Intelligence

The Lemon Leaf Disease Dataset (LLDD) is a high-quality image dataset designed for training and evaluating machine learning models for lemon leaf disease classification. The dataset contains 9  classes of images of healthy and diseased lemon leaves, such as; Anthracnose. Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites, making it suitable for tasks such as plant disease instance segmentation, detection, image classification, and deep learning applications in agriculture.

These folders contain images showcasing various aspects of orange fruit and  leaf diseases, including black spot, greening, scap, canker diseases, melanose, and healthy leaves. The dataset serves as a valuable resource for research, machine learning model training, and analysis in the field of citrus diseases and nutrient imbalances.

The data covers the period from January 4, 2021, to August 16, 2023. It includes the carbon trading prices from the Hubei carbon market and other relevant feature data that may influence carbon prices. The feature data has undergone preliminary screening and consists of Brent crude oil prices, natural gas prices, Rotterdam coal prices, EU Emission Allowances, the China Securities 300 Index, and the Euro exchange rate.

This dataset was built as part of our study MentalAgora: A Gateway to Advanced Personalized Care in Mental Health through Multi-Agent Debating and Attribute Control. The dataset was sourced from mental health-related posts in Reddit Mental Health Dataset and tagged with responses from mental health professionals to selected posts. For more details on building the dataset, please see the paper.

Attached Image data set from combined OCT-SLO is used to train AI models and identify features to maximize quality of data set to adjust MZI reference arm, PMT Voltage of Liquid Lens and location of object. Why adjustment is needed is explained below: 

We are pleased to submit our manuscript entitled ​​"Hetero-modal Template Guide Search Region for RGBT Tracking"​​ for consideration for publication in IEEE Transactions on Consumer Electronics. This work presents a novel framework for robust RGB-Thermal (RGBT) object tracking, addressing critical challenges in consumer electronics applications such as smart security systems, autonomous navigation, and augmented reality devices.

We are pleased to submit our manuscript entitled ​​"Hetero-modal Template Guide Search Region for RGBT Tracking"​​ for consideration for publication in IEEE Transactions on Consumer Electronics. This work presents a novel framework for robust RGB-Thermal (RGBT) object tracking, addressing critical challenges in consumer electronics applications such as smart security systems, autonomous navigation, and augmented reality devices.

This dataset consists of meteorological and environmental data collected in Riyadh, Saudi Arabia, over multiple years. The variables include solar radiation, temperature (both maximum and minimum in Celsius and Fahrenheit), precipitation, vapor pressure, and snow water equivalent, among others. The data spans from 2010 to the present, providing insights into solar radiation patterns, daily temperature fluctuations, and weather-related factors that can impact solar power generation. Specifically, the dataset contains the following columns:

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.

The shift towards cloud-native applications has been accelerating in recent years. Modern applications are increasingly distributed, taking advantage of cloud-native features such as scalability, flexibility, and high availability. However, this evolution also introduces various security challenges. From a networking perspective, the large number of interconnected components and their intricate communication patterns make detecting and mitigating traffic anomalies a complex task.