scholarly journals MACHINE LEARNING BASED AUTOMATIC PERMISSION GRANTING AND MALWARE IDENTIFICATION

2019 ◽  
Vol 01 (02) ◽  
pp. 96-107
Author(s):  
Duraipandian M. ◽  
Vinothkanna R.
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Mobile User ◽  
Machine Learning Techniques ◽  
Daily Routine ◽  
The Core ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Permission System ◽  
The Common

The mobile device have gained an imperative predominance in the daily routine of our lives, by keeping us connected to the real world seamlessly. Most of the mobile devices are built on android whose security mechanism is totally permission based controlling the applications from accessing the core details of the devices and the users. Even after understanding the permission system often the mobile user are ignorant about the common threat, due to the applications popularity and proceed with the installation process not aware of the targets of the application developer. The aim of the paper is to devise malware detection with the automatic permission granting employing the machine learning techniques. The different machine learning methods are engaged in the malware detection and analyzed. The results are observed to note down the approaches that aids better in enhancing the user awareness and reducing the malware threats, by detecting the malwares of the applications.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Combining Correlation-Based Feature and Machine Learning for Sensory Evaluation of Saigon Beer

2020 ◽  
Vol 11 (2) ◽  
pp. 71-85
Author(s):  
Nhat-Vinh Lu ◽  
Trong-Nhan Vuong ◽  
Duy-Tai Dinh
Keyword(s):  
Machine Learning ◽  
Sensory Evaluation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Methods ◽  
Feature Selection Technique ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Correlation Based Feature Selection ◽  
Positive Results

Sensory evaluation plays an important role in the food and consumer goods industry. In recent years, the application of machine learning techniques to support food sensory evaluation has become popular. Many different machine learning methods have been applied and produced positive results in this field. In this article, the authors propose a new method to support sensory evaluation on multiple criteria based on the use of a correlation-based feature selection technique, combined with machine learning methods such as linear regression, multilayer perceptron, support vector machine, and random forest. Experimental results are based on considering the correlation between physicochemical components and sensory factors on the Saigon beer dataset.

scholarly journals Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 511
Author(s):  
Duan Huang ◽  
Susu Liu ◽  
Ling Zhang
Keyword(s):  
Machine Learning ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Practical Security

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.

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