The following dataset presents the students' performance in mathematics for level 6 students over three semesters. Each semester contains a number of assessment blocks

A monitoring data, which includes several OTDR traces incorporating various types of fiber events (e.g. reflective, non-reflective, merged events) induced along an optical fiber link, is provided. Different fiber faults such as fiber cut, and fiber bend are modeled using optical components such as connectors and variable optical attenuators (VOAs). The data can be used to train machine learning models for solving fiber fault diagnosis problems. 

Guava fruit production is one of the main sources of economic growth in Asian countries, the world production of guava in 2019 was 55 million tons. Guava disease is an important factor in economic loss as well as quantity and quality of guava. The original guava fruit disease dataset consist of 38 images of phytophthora, 30 images of root and 34 images of scab guava disease with 650x650x3 pixel.

The problem of effective disposal of the trash generated by people has rightfully attracted major interest from various sections of society in recent times. Recently, deep learning solutions have been proposed to design automated mechanisms to segregate waste. However, most datasets used for this purpose are not adequate. In this paper, we introduce a new dataset, TrashBox, containing 17,785 images across seven different classes, including medical and e-waste classes which are not included in any other existing dataset.

The Research Paper "Detection of Bicep Form Using Myoware and Machine Learning" based on the novel dataset has been recently accepted in September 2022 and is being published in SCOPUS Indexed SPRINGER Book Series “Lecture Notes in Networks and Systems”

The Paddy Doctor dataset contains 16,225 labeled paddy leaf images across 13 classes (12 different paddy diseases and healthy leaves). It is the largest expert-annotated visual image dataset to experiment with and benchmark computer vision algorithms. The paddy leaf images were collected from real paddy fields using a high-resolution (1,080 x 1,440 pixels) smartphone camera. The collected images were carefully cleaned and annotated with the help of an agronomist.

Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the design and deployment of deep learning-based surgical applications such as surgical instrument segmentation.

This is a data set of delay timer design for paper ''Alarm deadband design based on maximum amplitude deviations and Bayesian estimation''.

In the digital era of the Industrial Internet of Things (IIoT), the conventional Critical Infrastructures (CIs) are transformed into smart environments with multiple benefits, such as pervasive control, self-monitoring and self-healing. However, this evolution is characterised by several cyberthreats due to the necessary presence of insecure technologies. DNP3 is an industrial communication protocol which is widely adopted in the CIs of the US. In particular, DNP3 allows the remote communication between Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA).

This peach tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of peach trees, including stress analysis and prediction. An orchard of peach trees is considered in the area of Thessaly, where 889 peach trees were recorded in a full crop season starting from Jul. 2021 to Sep. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.

Using Python. we crawl a total of 18, 793 diabetes related Q&A between Jun. 1, 2016 and Sept. 1, 2020 on xywy.com, a famous Chinese Online Medical Community. Each data contains four parts of the question detail page: Title, Problem Description, User ID and Question Time, and three parts of the doctor’s answer page: Doctor ID, Answer Content and Answer Time. After preprocessing such as cleaning and deduplication, we finally obtain 18,521 valid data.

In this study, we present advances on the development of proactive control for online individual user adaptation in a welfare robot guidance scenario, with the integration of three main modules: navigation control, visual human detection, and temporal error correlation-based neural learning. The proposed control approach can drive a mobile robot to autonomously navigate in relevant indoor environments. At the same time, it can predict human walking speed based on visual information without prior knowledge of personality and preferences (i.e., walking speed).

In order to contribute to the development of automatic methods for the detection of bacilli, TBimages is an image dataset composed of two subsets: TbImages_SS1 contains 10 images per field, of different focal depths, and aims to support the definition of autofocus metrics and also the development of extended focus imaging methods to facilitate the detection of bacilli in smear microscopy imaging.   TbImages_SS2  aims to support the development of automatic bacilli detection.

This research proposes a method to detect alcohol consumption from Near-Infra-Red (NIR) periocular eye images. The study focuses on determining the effect of external factors such as alcohol on the Central Nervous System (CNS). The goal is to analyse how this impacts on iris and pupil movements and if it is possible to capture these changes with a standard iris NIR camera. This paper proposes a novel Fused Capsule Network (F-CapsNet) to classify iris NIR images taken under alcohol consumption subjects.

This is a research about a method that I studied to treat virus COVID-19 or any other viruses by modifying and tricking the virus .

The dataset was generated through the execution of a Python script designed to collect a comprehensive set of data samples from six different sensors for each specific gesture. Upon launching the script, users are prompted to initiate gesture 0, Once ready, users can commence recording, with the program automatically capturing 1000 samples for that particular gesture. Subsequently, the program prompts users to perform gesture 1, and this process repeats until data for all gestures is collected.

Blade damage inspection without stopping the normal operation of wind turbines has significant economic value. This study proposes an AI-based method AQUADA-Seg to segment the images of blades from complex backgrounds by fusing optical and thermal videos taken from normal operating wind turbines. The method follows an encoder-decoder architecture and uses both optical and thermal videos to overcome the challenges associated with field application.

Most plant diseases have observable symptoms, and the widely used approach to detect plant leaf disease is by visually examining the affected plant leaves. A model which might carry out the feature extraction without any errors will process the classification task successfully.  The technology currently faces certain limitations such as a large parameter count, slow detection speed, and inadequate performance in detecting small dense spots. These factors restrict the practical applications of the technology in the field of agriculture.

"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.

<p>Ten individuals in good health were enlisted to execute 16 distinct movements involving the wrist and fingers in real-time. Before commencing the experimental procedure, explicit consent was obtained from each participant. Participants were informed that they had the option to withdraw from the study at any point during the experimental session. The experimental protocol adhered to the principles outlined in the Declaration of Helsinki and received approval from the local ethics committee at the National University of Sciences and Technology, Islamabad, Pakistan.

Existing work on radar modulation recognition is

widely based on the assumption of a single in-pulse signal,

whereas overlapping two pulse signals is an actual situation. This

paper introduces a challenging problem: modulation recognition

of overlapping intra-pulse signals, where the difficulty lies in

the fact that the number of samples grows stepwise with the

permutation of the sub-signals. In this paper, for the first time,

a series of methods for target detection are used to solve this

Radio-Frequency (RF) based User identification enables many attractive applications such as smart homes, and security management. However, laborious data collection is required due to appearance changes, inconsistent walking paths, and environmental variations. Furthermore, multi-user identification persists as an imperative for real-world applications. To this end, we propose an RFID-based user identification system (RF-UI), a few-shot, cross-interference factor, and a continuous user identification system.

Seismic data, obtained from sensors placed on the Earth's surface or subsurface, provides valuable insights into the composition and structure of the Earth's subsurface layers. This data is typically collected in the form of digital recordings, which represent the vibrations produced by seismic waves generated by controlled sources or natural events such as earthquakes. Converting seismic data into speech signals allows researchers and professionals to gain aural insights into the subsurface characteristics.

The "Paddy Field Dataset Captured in Palakkad District, Kerala, India" is a comprehensive collection of geospatial and attribute data specifically focused on paddy cultivation within the Palakkad district of the Kerala state in India. This dataset encompasses a wide range of information related to paddy fields, including their spatial distribution, size, crop varieties cultivated, land management practices, and relevant contextual factors. Geographic Information System (GIS) technology has captured accurate geospatial coordinates, enabling precise mapping and analysis.