Malware Detection of Hangul Word Processor Files Using Spatial Pyramid Average Pooling

Jeong, Young-Seob; Woo, Jiyoung; Lee, Sang‐Min; Kang, Ah Reum

doi:10.3390/s20185265

Cited by 6 publications

(3 citation statements)

References 21 publications

(37 reference statements)

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“…Table 7 shows per-label F1 scores, and the ANN is turned out to be the best amongst the models. This result is reasonable as the ANN is known to be effective in finding underlying patterns and gives significant performance improvement in many other classification tasks (e.g., malware detection [59], chatbot intent prediction [60]). We believe that the performance will be further improved if we collect more qualified data.…”

Section: Model Comparisonsupporting

confidence: 56%

Cocrystal Prediction Using Machine Learning Models and Descriptors

et al. 2021

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Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.

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Section: Model Comparisonsupporting

confidence: 56%

Cocrystal Prediction Using Machine Learning Models and Descriptors

et al. 2021

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“…Table 7 shows the test set F1 scores for each label, and the ANN, RFE, and XGB were shown to be the best of the implemented models. This is a realistic outcome because the best models (e.g., ANN) is known to be successful at detecting underlying patterns and significantly improves classification performance in a variety of classification tasks (e.g., malware detection [ 53 ], chatbot intent prediction [ 54 ]). We believe that collecting more qualified data will boost performance even further.…”

Section: Resultsmentioning

confidence: 99%

Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum

et al. 2021

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Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.e., molecular descriptors values) obtained from PaDEL software from SMILES of compounds and compare the machine learning models by experiments with our collected data of 4794 instances. As a consequence, we found that three models amongst the five, namely artificial neural network (ANN), extreme gradient boost (XGB), and random forest (RF), outperform the others in terms of accuracy while observing that, using roughly a quarter of the promising descriptors picked by the feature selection algorithm, the five models achieved equivalent and comparable performance. Nevertheless, the contribution of all molecular descriptors in the models was investigated through the comparison of their rank values by the feature selection algorithm and found that the most potent and relevant descriptors which come from the ‘Autocorrelation’ module contributed more while the ‘Atom type electrotopological state’ contributed the least to the model.

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“…Because traditional machine learning-based methods do not efficiently capture local patterns between amino-acids, we chose to use CNN as our network structure, which is known to be relatively lighter than other neural networks (e.g., recurrent neural networks), and it is effective in capturing local patterns [26,27]. In this paper, to capture the patterns of features in more detail, we develop a deep neural network that is comprised of ten identical deep neural structures, with each of them being derived from the original VGG16 model [28] and a MLP network, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16

Xu¹,

Zhao²,

Zhang³

2021

Algorithms

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The accurate of i identificationntrinsically disordered proteins or protein regions is of great importance, as they are involved in critical biological process and related to various human diseases. In this paper, we develop a deep neural network that is based on the well-known VGG16. Our deep neural network is then trained through using 1450 proteins from the dataset DIS1616 and the trained neural network is tested on the remaining 166 proteins. Our trained neural network is also tested on the blind test set R80 and MXD494 to further demonstrate the performance of our model. The MCC value of our trained deep neural network is 0.5132 on the test set DIS166, 0.5270 on the blind test set R80 and 0.4577 on the blind test set MXD494. All of these MCC values of our trained deep neural network exceed the corresponding values of existing prediction methods.

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Malware Detection of Hangul Word Processor Files Using Spatial Pyramid Average Pooling

Cited by 6 publications

References 21 publications

Cocrystal Prediction Using Machine Learning Models and Descriptors

Cocrystal Prediction Using Machine Learning Models and Descriptors

Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum

Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16

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