GHio-Ca: An Android Application for Automatic Image Classification

2018 ◽  
pp. 248-257 ◽  
Author(s):  
Davide Polonio ◽  
Federico Tavella ◽  
Marco Zanella ◽  
Armir Bujari
Keyword(s):  
Image Classification ◽  
Android Application

scholarly journals Towards Image Classification with Machine Learning Methodologies for Smartphones

2019 ◽  
Vol 1 (4) ◽  
pp. 1039-1057 ◽  
Author(s):  
Lili Zhu ◽  
Petros Spachos
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Time Constraints ◽  
Learning Method ◽  
Android Application ◽  
Optimal Model ◽  
Recent Developments ◽  
Learning Architectures

Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery.

Android Application Based Real Time Home Automation

2011 ◽  
Vol 4 (7) ◽  
pp. 188-190 ◽  
Author(s):  
Kallakunta. Ravi Kumar ◽  
◽  
Shaik Akbar
Keyword(s):  
Real Time ◽  
Home Automation ◽  
Android Application

Myteam, an Android Application for Team Management

2012 ◽  
Vol 2 (3) ◽  
pp. 140-142
Author(s):  
Aabid A.S Mulani ◽  
◽  
Sagar A Patil ◽  
Yogesh R Khedkar
Keyword(s):  
Team Management ◽  
Android Application

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION

2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko
Keyword(s):  
Image Classification ◽  
Fuzzy Clustering ◽  
Structural Methods

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Remote Heart Beat Monitoring System

2019 ◽  
Vol 7 (1) ◽  
pp. 5-10
Author(s):  
Saman Shahid ◽  
Saima Zafar ◽  
Mansoor Imam ◽  
Muhammad Usman Chishtee ◽  
Haris Ehsan
Keyword(s):  
Heart Diseases ◽  
Cost Effective ◽  
Heart Beat ◽  
The Internet ◽  
Android Application ◽  
Development Environment ◽  
Future Assessment ◽  
Pulse Sensor ◽  
Hospital Visits ◽  
Monitoring Module

There is an increased prevalence of heart diseases in developing countries and continuous monitoring of heart beats is very much important to reduce hospital visits, health costs and complications. The Internet of Things (IoT) equipped with microcontrollers and sensors can give an easy and cost-effective remote health monitoring. We developed a Heart Beat monitoring module based on an android application. The software involved was the Android Application developed using Android Studio, which is the Integrated Development Environment (IDE). This app retrieved the data from the open IoT platform thingspeak.com. A highly sensitive Pulse Sensor was used to measure the heartbeat of the patient automatically. An Arduino Uno microcontroller interfaced with a Wi-Fi module ESP8266 used to transmit pulse reading over the internet using Wi-Fi. The heartbeat was displayed on the LCD of the patient in run-time. The heartbeat in beats per minute (BPM) was plotted against time (minutes). A mounted pulse sensor to the patient had monitored the heartbeat and transmitted it in the form of voltage signal to the microcontroller, which converted it back into a mathematical value. The Arduino transmitted the data onto the thingspeak.com portal, where it was plotted on a graph and the values were stored for future assessment. The user of the app was given a things peak API and the channel number as an access code, through which physician or nurse can accessed the patient’s data. IoT based heartbeat module as an android application can provide a convenient, cost effective and continuous remote measurements for heart patients to help physicians and nurses update. This app can reduce the burden of hospital visits or admissions for elderly patients.

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