Machine Learning

The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

This is a voiceprint dataset with speaker's normal voice and special voice.

We recruited user test participants by snowballing. We directly invited 34 participants, all of them are family members, friends and classmates of the authors, ranging in age from 16 to 55, with similar technical backgrounds. Next, the involved participants further invited their relatives and friends to join, then 21 additional participants joined our user study. Finally, 56 people participated in our user study.

This is a voiceprint dataset with speaker's normal voice and special voice.

We recruited user test participants by snowballing. We directly invited 34 participants, all of them are family members, friends and classmates of the authors, ranging in age from 16 to 55, with similar technical backgrounds. Next, the involved participants further invited their relatives and friends to join, then 21 additional participants joined our user study. Finally, 56 people participated in our user study.

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.

In My study, we evaluate the performance of the proposed clustering method across a wide range of publicly available datasets that represent different data modalities. Specifically, Jaffe, ExtendYaleB, and ORL are employed as facial image datasets to assess the method's capability in handling variations in facial expressions and lighting conditions.

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: 

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:

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.

SNMDat2.0 is a comprehensive multimodal dataset, expanded from the unimodal TwiBot-20, designed for Twitter social bot detection. Specifically, we add 274587 profile images and profile background images, 86498 tweet images and 49549 tweet videos based on the original 229580 twitter users, 227979 follow relationships and 33488192 tweet text.