Dataset Search

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

The Smart Home Device Dataset consists of 5000 samples collected at an hourly interval starting from January 2022, representing consumer electronics and IoT-enabled devices in a home automation environment. Each entry is associated with a unique device ID, ensuring identification of distinct devices. The dataset captures real-time sensor readings, including temperature variations (18°C to 30°C), power consumption levels (10W to 500W), and user activity states (Active, Idle, or Sleep), which provide contextual insights into device operation.

This repository contains the code and documentation for a computational framework that leverages machine learning techniques to enable accurate classification of bacterial species, even closely related strains.

The framework integrates genomic analysis methods, such as motif screening and single nucleotide polymorphism (SNP) extraction, to derive informative features from bacterial genomes. These genomic insights are then fed into machine learning models, which are trained to reliably differentiate between bacterial species based on their distinctive patterns and characteristics.

• LIS is a low - light instance segmentation dataset. https://github.com/Linwei-Chen/LIS
• ExDark is a low - light object detection dataset. https://github.com/cs-chan/Exclusively-Dark-Image-Dataset
• ACDC is a semantic segmentation dataset for adverse weather conditions.

The Dash Cam Video Dataset is a comprehensive collection of real-world road footage captured across various Indian roads, focusing on lane conditions and traffic dynamics. Indian roads are often characterized by inconsistent lane markings, unstructured traffic flow, and frequent obstructions, making lane detection and traffic identification a challenging task for autonomous vehicle systems. Reliable lane detection is crucial for developing robust Advanced Driver Assistance Systems (ADAS) and autonomous driving models tailored for Indian conditions.

Flexible tactile sensors have attracted significant interest in robotics, medical monitoring, and wearable devices. This paper presents a capacitive flexible tactile sensor that employs a nickel carbonyl powder (NCP)-silicone rubber (SR) composite for pressure and bending sensing, fabricated using magnetic field curing. The performance of the sensor is evaluated independently for pressure and bending sensing, including sensitivity, response time, repeatability, and cyclic stability.

The Brain Tumor Detection Dataset is a dataset that's specifically designed for detecting brain tumours using advanced computer vision techniques. It's compatible with YOLOv8 an efficient and real-time object detection algorithm. The dataset was last updated about a year ago and is curated to help accurately detect and classify brain tumours into three distinct classes. The main goal of the project is to contribute to the early detection and diagnosis of brain tumours, which aims to provide valuable support to medical professionals in creating effective treatment plans.

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients.

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients.

Bananas are widely farmed and consumed, offering essential nutrients like manganese, vitamin B6, vitamin C, and magnesium. They come in various breeds with distinct visual traits, including size, shape, color, texture, and skin patterns. To classify these varieties, five deep learning models—VGG16, ResNet50, MobileNet, Inception-v3, and a customized CNN—were trained on banana images. These models enhance quality control and supply chain management by accurately identifying banana breeds.

The integration of wearable sensors with artificial intelligence forms the base for analyzing physical activities through digital signal processing, numerical methods, and machine learning. Computational intelligence and communication technologies enable personalized monitoring, training, and rehabilitation, with applications in sports, neurology, and biomedicine. This paper focuses on motion analysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate ski movement patterns.

Solar Insecticidal Lamps Internet of Things (SIL-IoTs) is an advanced agricultural IoT system integrating solar insecticidal lamps with wireless sensor networks. It attracts pests with light, then kills them with high-voltage metal grids. Equipped with wireless communication modules and environmental sensors, SIL-IoTs can collect and transmit field data, including pest counts (discharge pulse counts, insect-killing sound pulse counts), environmental data (air temperature, humidity, light intensity, equipment box temperature), and operational status.

We construct the Thyroid Nodule Ultrasound (TNUS) dataset with thyroid nodule positions and puncture annotations, lacking in existing datasets. It supports future research in automating detection and diagnosis, enhancing diagnostic accuracy and clinical applications. The TNUS dataset is a curated collection of thyroid nodule ultrasound (US) images designed to support research in puncture position detection and nodule segmentation. It contains 4,376 images with puncture position annotations and 2,626 additional images with thyroid/nodule masks.

ÛThis article examines Meta-AI's sociolinguistic challenges on WhatsApp through research-based analysis of its limitations in adapting lexicon and precise ethical practices in intercultural communication. The study demonstrates how Meta-AI system fails to read truncated vernacular speech patterns (“kenapa” → “enapa”) while missing customized slang (“puki”) used specifically in Maluku, North Maluku and East Nusa Tenggara regions to show fundamental limitations in error recognition capabilities and contextual understanding.

All three datasets are in CSV file format and contain renewable energy and load demand data from August 1, 2024 to August 30, 2024. The dataset is sourced from California ISO and is stored through an open-source repository https://github.com/gridstatus/gridstatus. Renewable energy sources include wind and solar energy, and the load demand is based on data from the Trinity Public Utility District (TIDC) and the Trook Irrigation District (TPWR).

The Drone Sensor Fusion Dataset features high-quality telemetry data from real and attack-modeled UAV flights, leveraging the PX4 flight log dataset. This includes normal flight data prepared for machine learning model training and simulated attack data generated using the 'Coordinated Sensor Manipulation Attack' (CSMA) model. CSMA simulates advanced threats by subtly altering GPS and IMU data to induce undetectable navigation drift.

The BNS (Bharatiya Nyay Sanhita) dataset is a comprehensive collection of legal texts which was web-scraped.. It consists of chapters and their respective sections, capturing detailed legal content relevant to the recently introduced BNS framework in India. This dataset was gathered using a Python-based web scraping script leveraging Selenium WebDriver, ensuring accuracy and completeness. Available in CSV formats, the dataset facilitates ease of access for legal research, natural language processing (NLP) tasks, and AI-based legal assistance applications.

This dataset provides a comprehensive analysis of global land use and biodiversity trends, offering insights into how ecosystems are changing over time. It includes country-wise data on arable land, forests, and permanent crops, helping to track the impact of agriculture and deforestation on natural landscapes. The biodiversity section highlights protected areas and key conservation indicators, allowing researchers to assess the effectiveness of environmental policies.

These are 3D contours from LiDAR point cloud of Las Vegas. The QL-1 datasets (≤10cm vertical/≤35cm horizontal accuracy, ≥8 points/m²) required preprocessing due to excessive data volume (142GB for Santa Clara alone). Our method reduces data while preserving structurally critical line features for satellite image-LiDAR point cloud registration, focusing on building contours rather than less prominent road edges. First, building footprints were extracted using Google's 2D shape vectors instead of raw segmentation or classification.

Online Quiz System is a web-based quiz system for accessing students. It is a system by which students can

sit in a quiz which need no pencil and paper. Nowadays, students take quiz manually .Lecturers need spend

more time on grading. Other than that, the quiz paper maybe will be missing. Students need to wait for

lecturers finish grading to get their result. Therefore, this system will help lecturers save their time because

of automated marking. Lecturers can set up a quiz which is it will auto-grade itself. Students can answer the