A Machine Learning Driven Android Based Mobile Application for Flower Identification

2021 ◽  
pp. 163-175
Author(s):  
Towhidul Islam ◽  
Nurul Absar ◽  
Abzetdin Z. Adamov ◽  
Mayeen Uddin Khandaker
Keyword(s):  
Machine Learning ◽  
Mobile Application

scholarly journals Internet of Things-Based Intelligent Smart Home Control System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Automation System ◽  
Home Automation ◽  
Area Of Interest ◽  
Home Automation System

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

Deep Learning-Based Mobile Application for Plant Disease Diagnosis

2019 ◽  
pp. 242-271 ◽  
Author(s):  
Shradha Verma ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Shubham Sharma ◽  
Puranjay Rajvanshi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Plant Disease ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Learning Tools ◽  
Class Labels

With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.

scholarly journals North American Hardwoods Identification Using Machine-Learning

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 298 ◽  
Author(s):  
Dercilio Junior Verly Lopes ◽  
Greg W. Burgreen ◽  
Edward D. Entsminger
Keyword(s):  
Machine Learning ◽  
North American ◽  
Mobile Application ◽  
Cross Validation ◽  
Data Augmentation ◽  
Technical Note ◽  
Machine Learning Method ◽  
Training Set ◽  
Hardwood Species ◽  
Fold Cross Validation

This technical note determines the feasibility of using an InceptionV4_ResNetV2 convolutional neural network (CNN) to correctly identify hardwood species from macroscopic images. The method is composed of a commodity smartphone fitted with a 14× macro lens for photography. The end-grains of ten different North American hardwood species were photographed to create a dataset of 1869 images. The stratified 5-fold cross-validation machine-learning method was used, in which the number of testing samples varied from 341 to 342. Data augmentation was performed on-the-fly for each training set by rotating, zooming, and flipping images. It was found that the CNN could correctly identify hardwood species based on macroscopic images of its end-grain with an adjusted accuracy of 92.60%. With the current growing of machine-learning field, this model can then be readily deployed in a mobile application for field wood identification.

Machine Learning Based Malicious Android Application Detection

2020 ◽  
Vol 17 (8) ◽  
pp. 3468-3472
Author(s):  
S. L. Jany Shabu ◽  
Rohan Loganathan Reddy ◽  
V. Maria Anu ◽  
L. Mary Gladence ◽  
J. Refonaa
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Learning Algorithms ◽  
Identity Theft ◽  
Machine Learning Algorithms ◽  
Android Application ◽  
Sensitive Data ◽  
Android Applications ◽  
Malicious Apps

The ultimate aim of the project is to improve permission for detecting the malicious android mobile application using machine learning algorithms. In recent years, the usages of smartphones are increasing steadily and also growth of Android application users are increasing. Due to growth of Android application users, some intruders are creating malicious android applications as a tool to steal the sensitive data and identity theft/fraud mobile bank, mobile wallets. There are so many malicious applications detection tools and software are available. But an effectiveness of malicious applications detection tools is the need for the hour. They are needed to tackle and handle new complex malicious apps created by intruder or hackers.

scholarly journals Development of Mobile Application for Faculty Time out Information

2020 ◽  
Vol 9 (4) ◽  
pp. 168-174
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Software Development ◽  
Mobile Applications ◽  
Mobile Application ◽  
Low Level ◽  
Time Out ◽  
The World ◽  
Faculty Time ◽  
High Level

In the trend of mobile applications, the world is surfing through many applications for various personal and professional purposes. In every domain including the cutting-edge technology such as Machine learning, IoT (Internet of Things), representing the data to the user in a proper and understanding manner is very important. This is where mobile applications come to use. Mobile applications can be used to resolve many communication issues especially when communication is between low level to high level and vice versa. This application is made to serve as one of the best ways of communication between faculty and students especially when the faculty is not available in the cabin and the student is willing to meet the faculty at the same time. The mobile application uses Dart Language with Flutter UI Software Development

scholarly journals Mushroom Diagnosis Assistance System Based on Machine Learning by Using Mobile Devices

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Intisar Shadeed Al-Mejibli ◽  
Dhafar Hamed Abd
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Mobile Devices ◽  
Mobile Application ◽  
Assistance System ◽  
Wild Mushrooms ◽  
Safety Issues ◽  
Novel Approach ◽  
Determination Process

Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a public issue within the last years because many types of mushrooms are poisonous. Proper determination of mushrooms is one of the key safety issues in picking activities of it, which is widely spread, in countries. This contribution proposes a novel approach to support determination of the mushrooms through using a proposed system with mobile devices.  Part of the proposed system is a mobile application that easily used by a user - mushroom picker. Hence, the mushroom type determination process can be performed at any location based on specific attributes of it. The mushroom type determination application runs on Android devices that are widely spread and inexpensive enough to enable wide exploitation by users. This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the mushroom types.  The proposed approach selects the most effective of the already known mushroom attributes, and then specify the mushroom type. The use of specific features in mushroom determination process achieved very accurate results.

scholarly journals An NLP Based Contrivance to Simplify Customer Interaction

2020 ◽  
Vol 9 (3) ◽  
pp. 2892-2895
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Mobile Application ◽  
Driving Force ◽  
User Behavior ◽  
Data Entry ◽  
Automated System ◽  
Visual Medium ◽  
Customer Interaction ◽  
Chat Bot

An intelligent application is an instrumental driving force in retention and satisfaction of customers. Consertle would be one of the first banking applications in India that enables users to interact with an intelligent application through a chat bot that is specifically designed to understand, interpret and analyze user behavior so as to provide better and more efficient results. While chat bots itself are a new introduction to the Indian financial system, an intelligent chat bot enables customer to instant and more efficient query resolution.Currently, most banking applications are visual medium which requires customer to proceed through various levels of data entry and selection in order to get the desired response. It may be a query related to one’s account, transactions or information about the bank in general; but the process to get a satisfactory result is a relatively long and tedious process. It is notable that automation in the financial sector is largely primitive even after the outbreak of technologies such as AI, CV, ML, etc.Natural Language Processing and Machine Learning enables the creation of an automated system that takes in input in the form of voice and/or text, processes it and gives an intelligent response to the user which would be aimed at satisfying their current requirements along with the possible, predicted, immediate query that is likely to arise. The key component of application Consertle is the portable mobile application that upholds the chatbot, where NLP based speech to text conversion and interpretation takes place, thus production accurate results and also providing suggestions, by analyzing user behavior which is dependent upon many factors.

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