Recognition and Counting of Motorcycles by Fusing Support Vector Machine and Deep Learning

2019 ◽  
pp. 157-162
Author(s):  
Tzung-Pei Hong ◽  
Yu-Chiao Yang ◽  
Ja-Hwung Su ◽  
Shyue-Liang Wang
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Support Vector

Deep learning model for protein disease classification

2021 ◽  
Vol 16 ◽  
Author(s):  
Farida Alaaeldin Mostafa ◽  
Yasmine Mohamed Afify ◽  
Rasha Mohamed Ismail ◽  
Nagwa Lotfy Badr
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Protein Analysis ◽  
Kernel Functions ◽  
Support Vector ◽  
Feature Descriptors

Background: Protein sequence analysis helps in the prediction of protein functions. As the number of proteins increases, it gives the bioinformaticians a challenge to analyze and study the similarity between them. Most of the existing protein analysis methods use Support Vector Machine. Deep learning did not receive much attention regarding protein analysis as it is noted that little work focused on studying the protein diseases classification. Objective: The contribution of this paper is to present a deep learning approach that classifies protein diseases based on protein descriptors. Methods: Different protein descriptors are used and decomposed into modified feature descriptors. Uniquely, we introduce using Convolutional Neural Network model to learn and classify protein diseases. The modified feature descriptors are fed to the Convolutional Neural Network model on a dataset of 1563 protein sequences classified into 3 different disease classes: Aids, Tumor suppressor, and Proto oncogene. Results: The usage of the modified feature descriptors shows a significant increase in the performance of the Convolutional Neural Network model over Support Vector Machine using different kernel functions. One modified feature descriptor improved by 19.8%, 27.9%, 17.6%, 21.5%, 17.3%, and 22% for evaluation metrics: Area Under the Curve, Matthews Correlation Coefficient, Accuracy, F1-score, Recall, and Precision, respectively. Conclusion: Results show that the prediction of the proposed modified feature descriptors significantly surpasses that of Support Vector Machine model.

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

scholarly journals Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 61085-61095 ◽  
Author(s):  
Ahlam Al-Dhamari ◽  
Rubita Sudirman ◽  
Nasrul Humaimi Mahmood
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Abnormal Behavior ◽  
Support Vector ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

scholarly journals Analisis Trend Topik Penelitian pada Web Of Science dan SINTA untuk Penentuan Tema Tugas Akhir Mahasiswa AMIK Indonesia Banda Aceh

2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Bahruni Bahruni ◽  
Fathurrahmad Fathurrahmad
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Decision Tree ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Core Collection ◽  
Web Of Science ◽  
Support Vector ◽  
Large Margin

Penelitian ini mencoba melakukan penambangan dengan menggunakan teknologi web untuk mengumpulkan data informasi yang berasal dari Web of Science dan SINTA yang dikumpulkan. Metodologi Cross Industry Standard Process for Data Mining (CRISP–DM) digunakan sebagai standard proses data mining sekaligus sebagai metode penelitian. Peneliti mengumpulkan data melalui daftar jurnal Web of Science dan SINTA. Untuk melacak trend topik penelitian, peneliti memilih rentang waktu dari tahun 2018 sampai dengan 2019 dan mengekspor data dari Web of Science Core Collection pada April 2019. Ada 38.162 publikasi yang berhasil diambil di Web-Science-defined kategori Ilmu Komputer dan Sistem Informasi dan 230 diambil dari website SINTA. Tetapi, penulis hanya mengambil 20 Jurnal dengan H-Index Tertinggi di Web of Science Core Collection. Sedangkan pada SINTA, penulis juga mengambil 20 Jurnal dengan rangking SINTA 1 dan 2. penelitian ini menyimpulkan topik penelitian dalam jurnal Web of Science dan dikaitkan dengan dengan tren topik penelitian dan yang muncul terbanyak adalah learning, network, analysis, system, control, data, image, optimization, systems, dan neural. Adapun untuk klasifikasi menggunakan model Naive Bayes, Generalized Linear Model, Logistic Regression, Fast Large Margin, Deep Learning, Decision Tree, Random Forest, Gradient Boosted Trees, dan Support Vector Machine. Berdasarkan hasil akurasi, model Generalized Linear Model dan Decision Tree memiliki akurasi sebesar 94.3%, sedangkan Gradient Boosted Trees memiliki  persentase akurasi sebesar 93.8%. Naive Bayes menunjukkan tingkat akurasi sebesar 91.4%, diikuti dengan model Fast Large Margin, Deep Learning, Random Forest, dan Support Vector Machine memiliki akurasi sebesar 91.4%. Nilai dengan akurasi terendah menggunakan model Logistic Regression sebesar 65.2%. Hal ini menunjukan bahwa tingkat akurasi tertinggi yaitu dengan menggunakan model Generalized Linear Model dan Decision Tree sehingga hasilnya dapat memprediksi cukup akurat.

scholarly journals Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit

2019 ◽  
Vol 13 (1) ◽  
pp. 43
Author(s):  
Junanda Patihullah ◽  
Edi Winarko
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Hate Speech ◽  
Support Vector ◽  
Speech Detection ◽  
The People ◽  
Gated Recurrent Unit ◽  
Rule Out

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.

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 UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications

2019 ◽  
Author(s):  
Douglas Teodoro ◽  
Julien Knafou ◽  
Nona Naderi ◽  
Emilie Pasche ◽  
Julien Gobeill ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Deep Learning ◽  
Specific Protein ◽  
Support Vector ◽  
Protein Annotation ◽  
Feature Sets ◽  
Percentage Points ◽  
Main Challenge

AbstractIn the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliography in UniProt, we investigate a Convolution Neural Network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge to categorize publications at the accession annotation level is that the same publication can be annotated with multiple proteins, and thus be associated to different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achieved a F1-score of 0.72, outperforming baseline models based on logistic regression and support vector machine by up to 22 and 18 percentage points, respectively. We believe that such approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators to decide whether a publication is relevant for further curation for a protein accession.

scholarly journals Optimasi Klasifikasi Jenis Hutan Menggunakan Deep Learning Berbasis Optimize Selection

2019 ◽  
Vol 6 (1) ◽  
pp. 100-106
Author(s):  
Rizki Tri Prasetio ◽  
Endang Ripandi
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Forest Type ◽  
Support Vector ◽  
Type Mapping

AbstrakHutan adalah paru-paru dunia, maka menjaga kelestarian hutan merupakan suatu keharusan. Menurut WWF (2015) bahwa lebih dari 170 hektar hutan di seluruh dunia akan menghilang secara pesat hingga tahun 2030 mendatang. Brian Johnson, Ryutaro Tateishi, dan Zhixiao Xie (2010) melakukan penelitian untuk mengklasifikasikan jenis populasi tumbuhan hutan pada hutan Ibraki, Jepang. Algoritma SVM (Support Vector Machine) dan MLP (Multy Layer Perceptron) diterapkan untuk mengklasifikasikan jenis populasi tumbuhan hutan di hutan Ibraki dengan hasil akurasi 85.9%. Hasil Penelitian Brian Johnson dkk, diberi nama Forest Type Mapping Datasets.  Hasil akurasi yang diperoleh dari penerapan algoritma SVM dan MLP masih belum mencapai hasil akurasi yang optimal dan masih mungkin untuk ditingkatkan. Untuk dapat meningkatkan hasil akurasi yang optimal terhadap klasifikasi jenis populasi tumbuhan hutan pada Forest Type Mapping Datasets, maka pada penelitian ini diusulkan untuk menerapkan algoritma optimasi fitur Optimize Selection pada algoritma Deep Learning. Hasil penelitian menunjukan bahwa metode yang diusulkan membuat peningkatan akurasi yang signifikan. Nilai akurasi klasifikasi pada jenis populasi tumbuhan hutan yang dihasilkan algoritma Deep Learning dengan optimasi fitur Optimize Selection berhasil meningkat menjadi  96.46%.

Sign in / Sign up

Export Citation Format

Share Document