scholarly journals Prediksi Kelas Jamak dengan Deep Learning Berbasis Graphics Processing Units

2020 ◽  
Vol 20 (1) ◽  
pp. 67-76
Author(s):  
Rahmadya Trias Handayanto ◽  
Herlawati Herlawati
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Decision Tree ◽  
Graphics Processing Unit ◽  
Support Vector ◽  
Processing Unit ◽  
Class 1 ◽  
Graphics Processing

For the first time, machine learning did the classical classification process using two classes (bi-class) such as class -1 and class +1, 0 and 1, or the form of categories such as true and false. Famous methods used are Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The current development was a problem with more than two classes, known as multi-class classes. For SVM sometimes the plural classes are overcome by doing a gradual process like a decision tree (DT) method. Meanwhile, ANN has experienced rapid development and is currently being developed with a large number of layers with the new activation functions, i.e. the rectified linear units (ReLu), and the probabilistic-based activation, i.e. softmax, including its optimizer methods (adam, sgd, and others). Then the term changed to Deep Learning (DL). This study aimed to compare two well-known methods (DL and SVM) in classifying multiple classes. The number of DL layers was six with the neuron composition are 128, 64, 32, 8, 4, and 3, while SVM uses a radial kernel base function with gamma and c respectively 0.7 and 5. Besides, this study intends to compare the use of the Graphics Processing Unit (GPU) available on Google Interactive Notebook (Google Colab), an online Python language programming application. The results showed that DL accuracy outperformed SVM but required large computational resources, with the accuracy for DL and SVM are 99% and 98%, respectively. However, the use of the GPU can overcome these problems and is proven to increase the speed of the process as much as 47 times. Keywords: Artificial Neural Networks, Graphics Processing Unit, Google Interactive Notebook, Rectified Linear units, Support Vector Machine. Abstrak Di awal perkembangannya mesin pembelajaran melakukan proses klasikfikasi menggunakan dua kelas (bi-class) misalnya kelas -1 dan kelas +1, 0 dan 1, atau bentuk kategori seperti benar dan salah. Metode terkenal yang digunakan adalah Jaringan Syaraf Tiruan (JST) dan Support Vector Machine (SVM). Perkembangan selanjutnya adalah problem dengan kelas yang lebih dari dua kelas, dikenal dengan istilah kelas jamak (multi-class). Untuk SVM terkadang kelas jamak diatasi dengan melakukan proses berjenjang mirip pohon keputusan (decision tree). Sementara itu JST telah mengalami perkembangan yang pesat dan saat ini sudah dikembangkan dengan jumlah layer yang banyak disertai dengan fungsi-fungsi aktivasi terkini seperti rectified linear unit (ReLu), dan softmax yang berbasis probabilistik, termasuk juga metode-metode optimizernya (adam, sgd, dan lain-lain). Kemudian istilahnya berubah menjadi Deep Learning (DL). Penelitian ini mencoba membandingkan dua metode terkenal (DL dan SVM) dalam melakukan klasifikasi kelas jamak. Jumlah layer DL sebanyak enam dengan masing-masing neuron sebesar 128, 64, 32, 8, 4, dan 3, sementara SVM menggunakan kernel radial basis function dengan gamma dan c berturut-turut 0.7 dan 5. Selain itu penelitian ini bermaksud membandingkan penggunaan Graphics Processing Unit (GPU) yang tersedia di Google Interactive Notebook (Google Colab), sebuah aplikasi online pemrograman bahasa Python. Hasil penelitian menunjukan akurasi DL unggul tipis dibanding SVM namun memerlukan sumber daya komputasi yang besar masing-masing dengan akurasi 99% dan 98%. Namun penggunaan GPU mampu mengatasi permasalahan tersebut dan terbukti meningkatkan kecepatan proses sebanyak 47 kali. Kata kunci: Jaringan Syaraf Tiruan, Graphics Processing Unit, Google Interactive Notebook, Rectified Linear units, Support Vector Machine.

scholarly journals IDENTIFIKASI JENIS IKAN MENGGUNAKAN MODEL HYBRID DEEP LEARNING DAN ALGORITMA KLASIFIKASI

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Anifuddin Azis
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Deep Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Output

Indonesia merupakan negara dengan keanekaragaman hayati terbesar kedua di dunia setelah Brazil. Indonesia memiliki sekitar 25.000 spesies tumbuhan dan 400.000 jenis hewan dan ikan. Diperkirakan 8.500 spesies ikan hidup di perairan Indonesia atau merupakan 45% dari jumlah spesies yang ada di dunia, dengan sekitar 7.000an adalah spesies ikan laut. Untuk menentukan berapa jumlah spesies tersebut dibutuhkan suatu keahlian di bidang taksonomi. Dalam pelaksanaannya mengidentifikasi suatu jenis ikan bukanlah hal yang mudah karena memerlukan suatu metode dan peralatan tertentu, juga pustaka mengenai taksonomi. Pemrosesan video atau citra pada data ekosistem perairan yang dilakukan secara otomatis mulai dikembangkan. Dalam pengembangannya, proses deteksi dan identifikasi spesies ikan menjadi suatu tantangan dibandingkan dengan deteksi dan identifikasi pada objek yang lain. Metode deep learning yang berhasil dalam melakukan klasifikasi objek pada citra mampu untuk menganalisa data secara langsung tanpa adanya ekstraksi fitur pada data secara khusus. Sistem tersebut memiliki parameter atau bobot yang berfungsi sebagai ektraksi fitur maupun sebagai pengklasifikasi. Data yang diproses menghasilkan output yang diharapkan semirip mungkin dengan data output yang sesungguhnya.  CNN merupakan arsitektur deep learning yang mampu mereduksi dimensi pada data tanpa menghilangkan ciri atau fitur pada data tersebut. Pada penelitian ini akan dikembangkan model hybrid CNN (Convolutional Neural Networks) untuk mengekstraksi fitur dan beberapa algoritma klasifikasi untuk mengidentifikasi spesies ikan. Algoritma klasifikasi yang digunakan pada penelitian ini adalah : Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN),  Random Forest, Backpropagation.

scholarly journals A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

2020 ◽  
Author(s):  
Mohamed El Boujnouni
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The World ◽  
Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.

scholarly journals Detection of Inflatable Boats and People in Thermal Infrared with Deep Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5330
Author(s):  
Marcin Łukasz Kowalski ◽  
Norbert Pałka ◽  
Jarosław Młyńczak ◽  
Mateusz Karol ◽  
Elżbieta Czerwińska ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Processing Time ◽  
Graphics Processing Unit ◽  
Weather Conditions ◽  
Processing Unit ◽  
Long Time ◽  
Financial Interests ◽  
Graphics Processing

Smuggling of drugs and cigarettes in small inflatable boats across border rivers is a serious threat to the EU’s financial interests. Early detection of such threats is challenging due to difficult and changing environmental conditions. This study reports on the automatic detection of small inflatable boats and people in a rough wild terrain in the infrared thermal domain. Three acquisition campaigns were carried out during spring, summer, and fall under various weather conditions. Three deep learning algorithms, namely, YOLOv2, YOLOv3, and Faster R-CNN working with six different feature extraction neural networks were trained and evaluated in terms of performance and processing time. The best performance was achieved with Faster R-CNN with ResNet101, however, processing requires a long time and a powerful graphics processing unit.

scholarly journals A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

2020 ◽  
Author(s):  
Mohamed El Boujnouni
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Geographic Locations ◽  
The World ◽  
Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to achieve two main objectives: (1) to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world, and (2) to study the relation between these genomes and the regions of sampling. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has multiple origins with different degrees. Also, they were used to study the relation that exists between the geographic locations from where the genomes of COVID-19 were collected and the origins of this virus.

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 184 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Gal Amit ◽  
Hanan Datz
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Method ◽  
Algorithm Performance ◽  
Artificial Neural ◽  
First Time ◽  
Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

2019 ◽  
pp. 751-766
Author(s):  
Sajid Umair ◽  
Muhammad Majid Sharif
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Student Performance ◽  
Semantic Analysis ◽  
Vital Role ◽  
Support Vector ◽  
Data Set ◽  
Important Research Topic ◽  
Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

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