DeepOSN: Bringing deep learning as malicious detection scheme in online social network

Wanda, Putra; Hiswati, Marselina Endah; Huang, Jie

doi:10.11591/ijai.v9.i1.pp146-154

Cited by 20 publications

(15 citation statements)

References 19 publications

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“…Recently, Deep Learning has been in the spotlight in the development of Machine Learning. The reason is that deep learning has achieved excellent results in many areas [15] [16]. It is a branch of Machine Learning inspired by the human cortex by implementing an artificial neural network with many hidden layers.…”

Section: Related Workmentioning

confidence: 99%

Implementation of CNN for Plant Leaf Classification

Diqi

Mulyani²

2021

ijicom

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Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.

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Section: Related Workmentioning

confidence: 99%

Implementation of CNN for Plant Leaf Classification

Diqi

Mulyani²

2021

ijicom

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“…Many studies use the CNN algorithm to solve classification problems. We also calculate accuracy and test to get the best results [22]. the following is the formula we use to calculate the model we created:…”

Section: 𝑓(X) = X • W + 𝑏mentioning

confidence: 99%

Efficient Fruits Classification Using Convolutional Neural Network

Abidin¹,

Hamzah²,

Endah³

2021

ijicom

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Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

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“…Pengklasifikasian akun palsu menggunakan algoritma DL mendapatkan hasil yang akurat dan signifikan untuk menciptakan proteksi di OSN [15]. Untuk mendeteksi akun palsu, peneliti lain menggunakan SVM dan neural network.…”

Section: Landasan Teori Dan Tinjauan Pustakaunclassified

Re-Fake: Klasifikasi Akun Palsu di Sosial Media Online menggunakan Algoritma RNN

et al. 2021

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Online Social Network (OSN) adalah aplikasi social media yang memungkinkan komunikasi publik dan berbagi informasi. Namun, akun palsu di OSN dapat menyebarkan informasi palsu dengan sumber yang tidak diketahui. Ini adalah tugas yang menantang untuk mendeteksi akun berbahaya dalam sistem OSN yang besar. Keberadaan akun palsu atau akun yang tidak dikenal di OSN dapat menjadi masalah serius dalam menjaga privasi data. Berbagai komunitas telah mengusulkan banyak teknik untuk menangani akun palsu di OSN, termasuk teknik hitam-putih berbasis aturan hingga pendekatan pembelajaran. Oleh karena itu, dalam penelitian ini kami mengusulkan model klasifikasi menggunakan RNN untuk mendeteksi akun palsu secara akurat dan efektif. Kami melakukan penelitian ini dalam beberapa langkah, termasuk mengumpulkan dataset, pra-pemrosesan, ekstraksi, melatih model kami menggunakan RNN. Berdasarkan hasil eksperimen, model yang kami usulkan dapat menghasilkan akurasi yang lebih tinggi daripada model pembelajaran konvensional.

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DeepOSN: Bringing deep learning as malicious detection scheme in online social network

Cited by 20 publications

References 19 publications

Implementation of CNN for Plant Leaf Classification

Implementation of CNN for Plant Leaf Classification

Efficient Fruits Classification Using Convolutional Neural Network

Re-Fake: Klasifikasi Akun Palsu di Sosial Media Online menggunakan Algoritma RNN

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