The TiHAN-V2X Dataset was collected in Hyderabad, India, across various Vehicle-to-Everything (V2X) communication types, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Vehicle (I2V), and Vehicle-to-Cloud (V2C). The dataset offers comprehensive data for evaluating communication performance under different environmental and road conditions, including urban, rural, and highway scenarios.
The dataset provides crop-type surveys for Canada's prairie provinces (Manitoba, Saskatchewan and Alberta) in 2020 and 2021. The data were collected via windshield survey(driving through the countryside with GPS-enabled data collection software and satellite imagery). Crop-type points and their geographic coordinates on the ground were gathered using data collection software. Field boundaries were identified on satellite imagery. A single observation point is dropped in a homogeneous area within the field.
Endemic fish species are key components in seafood culinary excursions. Despite the increasing interest in leveraging technology to enhance various seafood culinary activities, there is a shortage of comprehensive datasets containing images of seafood used in artificial intelligence research, mainly those showcasing endemic fish. This research endeavors to bridge this gap by increasing the accuracy of fish recognition and introducing a new dataset comprising images of native fish for application in various machine-learning investigations.
This dataset consists of MRI images of brain tumors, specifically curated for tasks such as brain tumor classification and detection. The dataset includes a variety of tumor types, including gliomas, meningiomas, and glioblastomas, enabling multi-class classification. Each MRI scan is labeled with the corresponding tumor type, providing a comprehensive resource for developing and evaluating machine learning models for medical image analysis. The data can be used to train deep learning algorithms for brain tumor detection, aiding in early diagnosis and treatment planning.
To download this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13896353
Please cite the following paper when using this dataset:
you can download these datasets from OpenML: https://www.openml.org/search?type=data&status=active&tags.tag=2019_multioutput_paper.
EDM: The Electrical Discharge Machining dataset (Karalic and Bratko 1997) represents a two-target regression problem. The task is to shorten the machining time by reproducing the behaviour of a human operator that controls the values of two variables. Each of the target variables takes 3 distinct numeric values ( -1,0,1 ) and there are 16 continuous input variables.
you can download these datasets from OpenML: https://www.openml.org/search?type=data&status=active&tags.tag=2019_multioutput_paper.
EDM: The Electrical Discharge Machining dataset (Karalic and Bratko 1997) represents a two-target regression problem. The task is to shorten the machining time by reproducing the behaviour of a human operator that controls the values of two variables. Each of the target variables takes 3 distinct numeric values ( -1,0,1 ) and there are 16 continuous input variables.
Liver cancer treatment, especially for metastatic cases, poses significant challenges in accurately targeting tumours while sparing healthy tissue. Radioembolisation with yttrium-90 (Y-90) microspheres is a promising technique, but precise imaging of microsphere distribution is crucial. This study utilises T-PEPT, a novel Positron Emission Particle Tracking (PEPT) algorithm that combines topological data analysis with machine learning to identify Y-90 microsphere clusters in a digital twin of a patient's liver.
This dataset webpage contains datasets of exisiting and proposed models:
Presented in my 2nd (may be last) keynote Speaker Presentation in Conference - 2025*.
Novel Perspective of Contemplating Existing Principles of Scientific Truth : Novel B-Unified Theory, Postulates, Propositions And Models With Applications in Impedance, Transformers, Inverters, Generators, Pumps, Solar, Machinery, Turbines, SMPS and Short Circuit Analysis
This dataset webpage contains datasets of existing and proposed models
Presented in my 3rd Speaker Presentation in Conference - 2025*.
