Machine Learning

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper.


This open access webpage has all miscellaneous data and design related to ß-machines- Non-Medical, ß-Biomedical machines, processed ECG, sensors, heart and breathing patterns, sounds, finite element analysis, mathematics and BioEngineering. 

As all only want free download and I used to get emails to share data freely ; with IEEE DataPort I had began journey of Data, so I will do update data as long as IEEE DataPort allows it or as long as I can or . I might also upload same data externally to other site.


Falls are a major health problem with one in three people over the age of 65 falling each year, oftentimes causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data. Fall detection algorithms are typically trained on simulated fall data that contain a well-balanced number of examples of falls and activities of daily living. However, real-world falls occur infrequently, making them difficult to capture and causing severe data imbalance.


This dataset is from apache access log server. It contains: ip address, datetime, gmt, request, status, size, user agent, country, label. The dataset show malicious activity in IP address, request, and so on. You can analyze more as intrusion detection parameter.



This dataset is in support of my Research paper 'Design of 6-DoF Combat Quadcopter'.


The system is basic, on existing designs.It is very simple for any graduate,degree holder.


Related Claim : Novel ß Non-Linear Theory


This is an open-access page. All content can be freely downloaded after sign-up. This webpage contains datasets of 'Computational Biology' which are explored and based on known theory, datasets of self-claimed ß-Bio models for Woman/Females proposed by me as Self-Claimed advancements .


This is an open-access page. All content can be freely downloaded after sign-up. This webpage contains datasets and models, proposed by me which are Self-Claimed advancements. Complete Model and Project folder will be uploaded once Certain Medical People reject it for whatever reason, or accept it. Models shared only for humanitarian purposes, Can be used or modified by Clinical Doctors or Pharmacologists or Researchers under License CC-BY.  


The detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), Hewlett Packard Enterprise, SolarAid, and Data Science Experts, aims to promote research in automatic detection of human settlements deprived of access to electricity using multimodal and multitemporal remote sensing data.

Last Updated On: 
Thu, 01/06/2022 - 03:33
Citation Author(s): 
Colin Prieur, Hana Malha, Frederic Ciesielski, Paul Vandame, Giorgio Licciardi, Jocelyn Chanussot, Pedram Ghamisi, Ronny Hänsch, Naoto Yokoya

A medium-scale synthetic 4D Light Field video dataset for depth (disparity) estimation. From the open-source movie Sintel. The dataset consists of 24 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20–50 frames, and has ground-truth disparity values, so that can be used for training deep learning-based methods. Each scene was rendered with a clean pass after modifying the production file of Sintel with reference to the MPI Sintel dataset.