scholarly journals Android APK Identification using Non Neural Network and Neural Network Classifier

2021 ◽  
Vol 5 (2) ◽  
pp. 149-157
Author(s):  
Djarot Hindarto ◽  
Handri Santoso
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Mobile Applications ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
K Nearest Neighbor ◽  
Bayes Methods ◽  
K Nearest Neighbor Algorithm ◽  
Artificial Neural Network Ann

Currently adoption of mobile phones and mobile applications based  on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while  the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of  91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of  99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.

scholarly journals KOMPARASI METODE KLASIFIKASI PADA ANALISIS SENTIMEN USAHA WARALABA BERDASARKAN DATA TWITTER

2019 ◽  
Vol 15 (2) ◽  
pp. 267-274
Author(s):  
Tati Mardiana ◽  
Hafiz Syahreva ◽  
Tuslaela Tuslaela
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif.  Penelitian ini menggunakan data realtime dari  tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil  pengujian  dengan  confusion  matrix  diperoleh  nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine  sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine  dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.  

Research on Performance of the Classifying Models Based on Chinese, Pakistani, and Other Genres

2021 ◽  
Vol 11 (4) ◽  
pp. 61-79
Author(s):  
Ejaz ud Din ◽  
Long Hua ◽  
Zhongyu Lu
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Support Vector ◽  
The Internet ◽  
Pop Music ◽  
Music Recommendation ◽  
Audio Classification ◽  
K Nearest Neighbor ◽  
Chinese Music ◽  
Artificial Neural Network Ann

In recent years, with the increase in the amount of audio on the internet, the demand for audio classification is increasing. This paper focuses on finding the performance of the classifiers, uses Python for the simulation part, compares the performance, and finds the best classifier. Two experiments are performed for this paper; for the first part of the experiment, Pakistan and Chinese music samples are considered, and classifiers are used to classify these music samples. It is found that the artificial neural network (ANN) has lowest accuracy of 81.4%; additionally, support vector machine (SVM), k-nearest neighbor (KNN), and convolutional (CNN) accuracies remain between 82% to 86% based on the dataset. Random forest model has the highest accuracy of 94.3%. It is considered to be the best classifier. For the second part of the experiment, other genres such as classical, country, and pop music were added to the previous dataset. After adding these genres, performance of the classifying models varies slightly; it fluctuates between 75% to 84%. These results can be used for music recommendation applications.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

Application of Machine Learniing For Reservoir Facies Classification in Port Field, Offshore Niger Delta

2021 ◽  
Author(s):  
Jerome Asedegbega ◽  
Oladayo Ayinde ◽  
Alexander Nwakanma
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Niger Delta ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Data Preparation ◽  
K Nearest Neighbor ◽  
Blind Test ◽  
Facies Classification ◽  
Well Data

Abstract Several computer-aided techniques have been developed in recent past to improve interpretational accuracy of subsurface geology. This paradigm shift has provided tremendous success in variety of Machine Learning Application domains and help for better feasibility study in reservoir evaluation using multiple classification techniques. Facies classification is an essential subsurface exploration task as sedimentary facies reflect associated physical, chemical, and biological conditions that formation unit experienced during sedimentation activity. This study however, employed formation samples for facies classification using Machine Learning (ML) techniques and classified different facies from well logs in seven (7) wells of the PORT Field, Offshore Niger Delta. Six wells were concatenated during data preparation and trained using supervised ML algorithms before validating the models by blind testing on one well log to predict discrete facies groups. The analysis started with data preparation and examination where various features of the available well data were conditioned. For the model building and performance, support vector machine, random forest, decision tree, extra tree, neural network (multilayer preceptor), k-nearest neighbor and logistic regression model were built after dividing the data sets into training, test, and blind test well data. Results of metric score for the blind test well estimated for the various models using Jaccard index and F1-score indicated 0.73 and 0.82 for support vector machine, 0.38 and 0.54 for random forest, 0.78 and 0.83 for extra tree, 0.91 and 0.95 for k-nearest neighbor, 0.41 and 0.56 for decision tree, 0.63 and 0.74 for logistic regression, 0.55 and 0.68 for neural network, respectively. The efficiency of ML techniques for enhancing the prediction accuracy and decreasing the procedure time and their approach toward the data, makes it importantly desirable to recommend them in subsurface facies classification analysis.

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

2020 ◽  
Author(s):  
Nazrul Anuar Nayan ◽  
Hafifah Ab Hamid ◽  
Mohd Zubir Suboh ◽  
Noraidatulakma Abdullah ◽  
Rosmina Jaafar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Nearest Neighbor ◽  
Total Cholesterol Level ◽  
Population Based ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cvd Risk ◽  
Linear Discriminant ◽  
Artificial Neural Network Ann

Abstract Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs

2020 ◽  
Author(s):  
Hoda Heidari ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Iir Filters ◽  
Wide Range ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.

scholarly journals A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2814 ◽  
Author(s):  
Xiaoguang Liu ◽  
Huanliang Li ◽  
Cunguang Lou ◽  
Tie Liang ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Fall Detection ◽  
Classification Performance ◽  
Daily Activities ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Parallel Channels

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

scholarly journals The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Renzhou Gui ◽  
Tongjie Chen ◽  
Han Nie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Correlation Matrix ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Fmri Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Impact ◽  
Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

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