scholarly journals Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4776
Author(s):  
Hyoungsik Nam ◽  
Ki-Hyuk Seol ◽  
Junhee Lee ◽  
Hyeonseong Cho ◽  
Sang Won Jung
Keyword(s):  
Machine Learning ◽  
Consumer Electronics ◽  
Optical Methods ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Accuracy Improvement ◽  
Tablet Pcs ◽  
Long Time ◽  
Intuitive Interfaces ◽  
User Friendly

Touchscreens have been studied and developed for a long time to provide user-friendly and intuitive interfaces on displays. This paper describes the touchscreen technologies in four categories of resistive, capacitive, acoustic wave, and optical methods. Then, it addresses the main studies of SNR improvement and stylus support on the capacitive touchscreens that have been widely adopted in most consumer electronics such as smartphones, tablet PCs, and notebook PCs. In addition, the machine learning approaches for capacitive touchscreens are explained in four applications of user identification/authentication, gesture detection, accuracy improvement, and input discrimination.

scholarly journals Measurements for non-intrusive load monitoring through machine learning approaches

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 90
Author(s):  
Giovanni Bucci ◽  
Fabrizio Ciancetta ◽  
Edoardo Fiorucci ◽  
Simone Mari ◽  
Andrea Fioravanti
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Management Service ◽  
Learning Approaches ◽  
Load Monitoring ◽  
Long Time ◽  
Processing Techniques ◽  
Energy Companies

The topic of non-intrusive load monitoring (NILM) has seen a significant increase in research interest over the past decade, which has led to a significant increase in the performance of these systems. Nowadays, NILM systems are used in numerous applications, in particular by energy companies that provide users with an advanced management service of different consumption. These systems are mainly based on artificial intelligence algorithms that allow the disaggregation of energy by processing the absorbed power signal over more or less long time intervals (generally from fractions of an hour up to 24 h). Less attention was paid to the search for solutions that allow non-intrusive monitoring of the load in (almost) real time, that is, systems that make it possible to determine the variations in loads in extremely short times (seconds or fractions of a second). This paper proposes possible approaches for non-intrusive load monitoring systems operating in real time, analysing them from the point of view of measurement. The measurement and post-processing techniques used are illustrated and the results discussed. In addition, the work discusses the use of the results obtained to train machine learning algorithms that allow you to convert the measurement results into useful information for the user.

scholarly journals Understanding the Early Evolution of COVID-19 Disease Spread Using Mathematical Model and Machine Learning Approaches

2020 ◽  
pp. 19-36
Author(s):  
Samuel Oladimeji Sowol ◽  
Abdullahi Adinoyi Ibrahim ◽  
Daouda Sangare ◽  
Ismaila Omeiza Ibrahim ◽  
Francis I. Johnson
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Time Evolution ◽  
Regression Models ◽  
Early Time ◽  
Epidemiological Model ◽  
Disease Spread ◽  
Learning Approaches ◽  
Long Time ◽  
Data Driven Approach

In response to the global COVID-19 pandemic, this work aims to understand the early time evolution and the spread of the disease outbreak with a data driven approach. To this effect, we applied Susceptible- Infective-Recovered/Removed (SIR) epidemiological model on the disease. Additionally, we used the Machine Learning linear regression model on the historical COVID-19 data to predict the earlier stage of the disease. The evolution of the disease spread with the Mathematical SIR model and Machine Learning regression model for time series forecasting of the COVID-19 data without, and with lags and trends, was able to capture the early spread of the disease. Consequently, we suggest that if using a more advanced epidemiological model, and sophisticated machine learning regression models on the COVID-19 data, we can understand, as well as predict the long time evolution of the disease spread.

scholarly journals Diabetic Retinopathy Detection System

2021 ◽  
Vol 9 (9) ◽  
pp. 1498-1502
Author(s):  
Manisha Gangesh
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Web Application ◽  
Detection System ◽  
Retinal Images ◽  
Learning Approaches ◽  
Keywords Diabetic Retinopathy ◽  
Machine Learning Model ◽  
User Friendly

Abstract: Diabetic Retinopathy is a diabetes problem that affects the eye. Injury to the blood vessels of the light sensitive tissue inside the rear of the eye (retina) is that the most reason for diabetic retinopathy. To begin with, Diabetic Retinopathy may have no symptoms or just cause minor vision problems. It has the potential to lead to blindness. Machine learning approaches can be used for the early detection of Diabetic Retinopathy. This paper proposes an automated Diabetic Retinopathy detection system that can detect the presence of Diabetic Retinopathy from retinal images. This work uses ResNet50 for the detection and classification of Diabetic Retinopathy. ResNet50 is a type of neural network used as a backbone for many computer-vision tasks. This paper proposes a machine learning model which is developed using ResNet50, then the model will be deployed as a user-friendly web application where the user can upload the retinal images as input to the system then system will detect the presence of Diabetic Retinopathy and classifies it into the stage or class which the particular image belongs to. Keywords: Diabetic Retinopathy, ResNet50, Proliferative diabetic retinopathy, non-proliferative diabetic retinopathy.

Distant Traffic Light Recognition Using Semantic Segmentation

2021 ◽  
pp. 036119812110164
Author(s):  
Shota Masaki ◽  
Tsubasa Hirakawa ◽  
Takayoshi Yamashita ◽  
Hironobu Fujiyoshi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Environmental Changes ◽  
Semantic Segmentation ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Accuracy Improvement ◽  
Traffic Lights ◽  
Automatic Driving

Traffic light recognition is an important task for automatic driving support systems. Conventional traffic light recognition techniques are categorized into model-based methods, which frequently suffer from environmental changes such as sunlight, and machine-learning-based methods, which have difficulty detecting distant and occluded traffic lights because they fail to represent features efficiently. In this work, we propose a method for recognizing distant traffic lights by utilizing a semantic segmentation for extracting traffic light regions from images and a convolutional neural network (CNN) for classifying the state of the extracted traffic lights. Since semantic segmentation classifies objects pixel by pixel in consideration of the surrounding information, it can successfully detect distant and occluded traffic lights. Experimental results show that the proposed semantic segmentation improves the detection accuracy for distant traffic lights and confirms the accuracy improvement of 12.8 % over the detection accuracy by object detection. In addition, our CNN-based classifier was able to identify the traffic light status more than 30 % more accurately than the color thresholding classification.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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