A Mobile Application for Plant Recognition through Deep Learning

2017 ◽  
Author(s):  
Min Gao ◽  
Lang Lin ◽  
Richard O. Sinnott
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Plant Recognition

Deep Learning-Based Mobile Application Isomorphic GUI Identification for Automated Robotic Testing

IEEE Software ◽  
2020 ◽  
Vol 37 (4) ◽  
pp. 67-74
Author(s):  
Tao Zhang ◽  
Ying Liu ◽  
Jerry Gao ◽  
Li Peng Gao ◽  
Jing Cheng
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Robotic Testing

scholarly journals Mosquito Larvae Detection using Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 804-809
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Web Application ◽  
Individual Performance ◽  
Mosquito Larvae ◽  
Accuracy Score ◽  
Performance Category ◽  
Training Time ◽  
The Cost ◽  
And Training

Dengue cases has become endemic in Malaysia. The cost of operation to exterminate mosquito habitats are also high. To do effective operation, information from community are crucial. But, without knowing the characteristic of Aedes larvae it is hard to recognize the larvae without guide from the expert. The use of deep learning in image classification and recognition is crucial to tackle this problem. The purpose of this project is to conduct a study of characteristics of Aedes larvae and determine the best convolutional neural network model in classifying the mosquito larvae. 3 performance evaluation vector which is accuracy, log-loss and AUC-ROC will be used to measure the model’s individual performance. Then performance category which consist of Accuracy Score, Loss Score, File Size Score and Training Time Score will be used to evaluate which model is the best to be implemented into web application or mobile application. From the score collected for each model, ResNet50 has proved to be the best model in classifying the mosquito larvae species.

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.

A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning

2021 ◽  
pp. 626-644
Author(s):  
Manokaran Newlin Rajkumar ◽  
Varadhan Venkatesa Kumar ◽  
Ramachandhiran Vijayabhasker
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Control Flow ◽  
Smart Phones ◽  
The People ◽  
Flow Information ◽  
Android Applications ◽  
Modern Era

This modern era of technological advancements facilitates the people to possess high-end smart phones with incredible features. With the increase in the number of mobile applications, we are witnessing the humongous increase in the malicious applications. Since most of the Android applications are available open source and used frequently in the smart phones, they are more vulnerable. Statistical and dynamical-based malware detection approaches are available to verify whether the mobile application is a genuine one, but only to a certain extent, as the level of mobile application scanning done by the said approaches are in general routine or a common, pre-specified pattern using the structure of control flow, information flow, API call, etc. A hybrid method based on deep learning methodology is proposed to identify the malicious applications in Android-based smart phones in this chapter, which embeds the possible merits of both the statistical-based malware detection approaches and dynamical-based malware detection approaches and minimizes the demerits of them.

Impact of online customer reviews and deep learning on product innovation empirical study on mobile applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huiliang Zhao ◽  
Qin Yang ◽  
Zhenghong Liu
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Product Innovation ◽  
Research Study ◽  
Market Competition ◽  
Online Reviews ◽  
Business Environment ◽  
Content Type ◽  
App Store ◽  
Networking Technology

PurposeThe customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product reviews effect as market competition. This research study explains deep learning, online reviews and product innovation empirical evidence used by mobile apps.Design/methodology/approachOnline reviews and product innovation are very important for every organization and firms to achieve a competitive advantage in a large business environment. When the authors see past traditional history, customers are not involved in product creating and innovating processes. Due to new technology changes, online systems and web 2.0 increase this ability.FindingsFor this research purpose, the authors use different analytical software to measure the impact among variables. This study is established on primary data; this study collected data from online customers and its users. For data collection, the authors use some questionnaires, and these questions are filled from 200 respondents.Research limitations/implicationsThis research study used data from the Google app store – Google product selling application – and gathered customers' online reviews. Research found that customers' online reviews and deep learning positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.Originality/valueThis research study used data from the Google app store Google product selling application and gathered customers' online reviews. Research founded that customers' online reviews and deep learning are positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

A Study on Classification of Mobile Application Reviews Using Deep Learning

2021 ◽  
Vol 10 (2) ◽  
pp. 76-83
Author(s):  
Jaeik Son ◽  
Mijin Noh ◽  
Tazizur Rahman ◽  
Gyujin Pyo ◽  
Mumoungcho Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mobile Application
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