Android Application Analysis Using Machine Learning Techniques

2018 ◽  
pp. 181-205 ◽  
Author(s):  
Takeshi Takahashi ◽  
Tao Ban
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Android Application ◽  
Learning Techniques ◽  
Application Analysis

scholarly journals A Consumer-Friendly Machine Learning Based Mechanism to Recognize the Quality of Commercially Available Fruits

2020 ◽  
Vol 9 (3) ◽  
pp. 3833-3839
Keyword(s):  
Machine Learning ◽  
Nondestructive Method ◽  
Machine Learning Techniques ◽  
Android Application ◽  
Test Image ◽  
Ripening Process ◽  
Learning Techniques ◽  
Artificial Fruit ◽  
The Cost

Cultivators and sellers of many high-in-demand fruits traditionally preferred natural ripening after picking. Greed of hefty profits has motivated some of them to artificially hasten the ripening process at the cost of people’s health. Artificial ripening processes tend to degrade the entire quality of the fruit. The focus of this work is to describe a nondestructive method to detect artificial fruit ripening. To aid the detection, the proposed solution utilizes image processing and machine learning techniques to find the artificially ripened fruits. An input fruit image is selected as the test image. The next stage involves comparison of the features (histogram values) of the test image with the image of a naturally ripened one. A smartphone runs an android application to identify artificially ripened fruits. This work specifically concentrates on the commonly preferred Indian Mango and Indian Apple. The developed mechanism has an efficiency of 89-94% in correct detection.

scholarly journals Virtual Assistant for Prediction of Fake Job Profile Using Machine Learning

2021 ◽  
pp. 171-175
Author(s):  
Prof. R. S. Shishupal ◽  
Varsha . ◽  
Supriya Mane ◽  
Vinita Singh ◽  
Damini Wasekar
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Android Application ◽  
Detection Model ◽  
Learning Techniques ◽  
Job Offers ◽  
Online Conversation ◽  
Job Profile ◽  
Enormous Number

To avoid fraudulent post for job in the internet, an android application using machine learning based classification techniques is proposed in the paper. Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best fake job scam detection model. It helps in detecting fake job posts from an enormous number of posts. It is an android application that is used to conduct an online conversation via text and speech by using natural language processing (NLP) and predicts the fake job offers using machine learning techniques. The data we used contains real and fake job post. We cleaned and pre-processed our data. After we implemented the classifiers, we trained and evaluated them for prediction of fake job offers.

scholarly journals A Graph-Based Feature Generation Approach in Android Malware Detection with Machine Learning Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaojian Liu ◽  
Qian Lei ◽  
Kehong Liu
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Semantic Information ◽  
Control Flow ◽  
Machine Learning Techniques ◽  
Android Application ◽  
Feature Generation ◽  
Accuracy Rate ◽  
Android Malware ◽  
Learning Techniques

An explosive spread of Android malware causes a serious concern for Android application security. One of the solutions to detecting malicious payloads sneaking in an application is to treat the detection as a binary classification problem, which can be effectively tackled with traditional machine learning techniques. The key factors in detecting Android malware with machine learning techniques are feature selection and generation. Most of the existing approaches select and generate features without fully examining the structures of programs, and thus the important semantic information associated with these features is lost, consequently resulting in a low accuracy rate in detection. To address this issue, we propose a new feature generation approach for Android applications, which takes components and program structures into consideration and extracts features in a graph-based and semantics-rich style. This approach highlights two major distinguishing aspects: the context-based feature selection and graph-based feature generation. We abstract an Android application as a collection of reduced iCFGs (interprocedural control flow graphs) and extract original features from these graphs. Combining the original features and their contexts together, we generate new features which hold richer semantic information than the original ones. By embedding the features into a feature vector space, we can use machine learning techniques to train a malware detector. The experiment results show that this approach achieves an accuracy rate of 95.4% and a recall rate of 96.5%, which prove the effectiveness and advantages of our approach.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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