HFDCM: A low-cost machine learning based class attendance monitoring system

2019 ◽  
Author(s):  
Azm Ehtesham Chowdhury ◽  
Omar Khaium Chowdhury ◽  
Md. Assaduzzaman Samrat ◽  
Md. Zillur Rahman ◽  
Tanvir Ahmed
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Low Cost ◽  
Class Attendance

scholarly journals Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 208 ◽  
Author(s):  
Jongryun Roh ◽  
Hyeong-jun Park ◽  
Kwang Lee ◽  
Joonho Hyeong ◽  
Sayup Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Low Cost ◽  
Load Cell ◽  
Sitting Posture ◽  
Posture Monitoring

scholarly journals A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers

2019 ◽  
Vol 252 ◽  
pp. 03009 ◽  
Author(s):  
Tomasz Cieplak ◽  
Tomasz Rymarczyk ◽  
Robert Tomaszewski
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Monitoring System ◽  
Low Cost ◽  
Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Air Quality Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The City

This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such ase.g.air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

scholarly journals COVIMOS: A Coastal Video Monitoring System

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
I Made Oka Widyantara ◽  
I Made Dwi Asana Putra ◽  
Ida Bagus Putu Adnyana
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Video Image ◽  
Video Monitoring ◽  
Reference Object ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Ip Camera ◽  
Class Labels

This paper intends to explain the development of Coastal Video Monitoring System (CoViMoS) with the main characteristics including low-cost and easy implementation. CoViMoS characteristics have been realized using the device IP camera for video image acquisition, and development of software applications with the main features including detection of shoreline and it changes are automatically. This capability was based on segmentation and classification techniques based on data mining. Detection of shoreline is done by segmenting a video image of the beach, to get a cluster of objects, namely land, sea and sky, using Self Organizing Map (SOM) algorithms. The mechanism of classification is done using K-Nearest Neighbor (K-NN) algorithms to provide the class labels to objects that have been generated on the segmentation process. Furthermore, the classification of land used as a reference object in the detection of costline. Implementation CoViMoS system for monitoring systems in Cucukan Beach, Gianyar regency, have shown that the developed system is able to detect the shoreline and its changes automatically.

A Simple, Low-cost Multi-sensor-based Smart Wearable Knee Monitoring System

2020 ◽  
pp. 1-1
Author(s):  
Abu Ilius Faisal ◽  
Sumit Majumder ◽  
Ryan Scott ◽  
Tapas Mondal ◽  
David Cowan ◽  
...  
Keyword(s):  
Monitoring System ◽  
Low Cost ◽  
Smart Wearable

scholarly journals Android Based Low Cost Sitting Posture Monitoring System

2020 ◽  
Author(s):  
Nusrat Binta Nizam ◽  
Tohfatul Jinan ◽  
Wahida Binte Naz Aurthy ◽  
Md. Rakib Hossen ◽  
Jahid Ferdous
Keyword(s):  
Monitoring System ◽  
Low Cost ◽  
Sitting Posture ◽  
Posture Monitoring
Sign in / Sign up

Export Citation Format

Share Document