scholarly journals Malware Classification Using Machine Learning and Image Processing

2022 ◽  
Vol 10 (1) ◽  
pp. 531-541
Author(s):  
Zainab Mushtaq
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computer Security ◽  
Transfer Learning ◽  
Learning Technology ◽  
Test Dataset ◽  
Learning Network ◽  
Malware Classification ◽  
Learning Technique ◽  
Deep Learning Network

Abstract: Malware is routinely used for illegal reasons, and new malware variants are discovered every day. Computer vision in computer security is one of the most significant disciplines of research today, and it has witnessed tremendous growth in the preceding decade due to its efficacy. We employed research in machine-learning and deep-learning technology such as Logistic Regression, ANN, CNN, transfer learning on CNN, and LSTM to arrive at our conclusions. We have published analysis-based results from a range of categorization models in the literature. InceptionV3 was trained using a transfer learning technique, which yielded reasonable results when compared with other methods such as LSTM. On the test dataset, the transferring learning technique was about 98.76 percent accurate, while on the train dataset, it was around 99.6 percent accurate. Keywords: Malware, illegal activity, Deep learning, Network Security,

scholarly journals Efficient CNN Approach for Facial Expression Recognition

2021 ◽  
Vol 2129 (1) ◽  
pp. 012083
Author(s):  
Gheyath Mustafa Zebari ◽  
Dilovan Asaad Zebari ◽  
Diyar Qader Zeebaree ◽  
Habibollah Haron ◽  
Adnan Mohsin Abdulazeez ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Expression Recognition ◽  
Machine Learning Technique ◽  
Learning Technique

Abstract In the last decade, the Facial Expression Recognition field has been studied widely and become the base for many researchers, and still challenging in computer vision. Machine learning technique used in facial expression recognition facing many problems, since human emotions expressed differently from one to another. Nevertheless, Deep learning that represents a novel area of research within machine learning technology has the ability for classifying people’s faces into different emotion classes by using a Deep Neural Network (DNN). The Convolution Neural Network (CNN) method has been used widely and proved as very efficient in the facial expression recognition field. In this study, a CNN technique for facial expression recognition has been presented. The performance of this study has been evaluated using the fer2013 dataset, the total number of images has been used. The accuracy of each epoch has been tested which is trained on 29068 samples, validate on 3589 samples. The overall accuracy of 69.85% has been obtained for the proposed method.

Application of deep learning for seismic horizon interpretation

2019 ◽  
Vol 59 (1) ◽  
pp. 426
Author(s):  
James Lowell ◽  
Jacob Smith
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Test Data ◽  
Seismic Data ◽  
Training Data ◽  
Learning Networks ◽  
Learning Network ◽  
Deep Learning Network

The interpretation of key horizons on seismic data is an essential but time-consuming part of the subsurface workflow. This is compounded when surfaces need to be re-interpreted on variations of the same data, such as angle stacks, 4D data, or reprocessed data. Deep learning networks, which are a subset of machine learning, have the potential to automate this reinterpretation process, and significantly increase the efficiency of the subsurface workflow. This study investigates whether a deep learning network can learn from a single horizon interpretation in order to identify that event in a different version of the same data. The results were largely successful with the target horizon correctly identified in an alternative offset stack, and was correctly repositioned in areas where there was misalignment between the training data and the test data.

3D modelling of a frame assembly using deep learning and the Chu–Liu–Edmonds Algorithm

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hao Cao ◽  
Rong Mo ◽  
Neng Wan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heuristic Algorithms ◽  
Classification Rate ◽  
Content Type ◽  
Learning Network ◽  
Automated Method ◽  
Deep Learning Network ◽  
D Model ◽  
Better Than

Purpose The proposed method is to generate the 3 D model of frame assemblies based on their topological model automatedly. It was a very demanding task and there was no appropriate automated method to facilitate this work. Design/methodology/approach The proposed method includes two stages. The first stage is decisive. In this stage, a deep learning network and the Chu–Liu–Edmonds algorithm are used to recognize contact relations among parts. Based on this recognition, the authors perform a geometrical computation in the second stage to finalize the 3 D model. Findings The authors verify the feasibility of the proposed method using a case study and find that the classification rate of the deep learning network for part contact relations is higher than 75 per cent. Furthermore, more accurate results could be achieved with modification by the Chu–Liu–Edmonds algorithm. The proposed method has lower computational complexity compared with traditional heuristic methods, and its results are more consistent with existing designs. Research limitations/implications The paper introduces machine learning method into assembly modelling issue. The proposed method divides the assembly modelling into two steps and solves the assemble relation creatively. Practical implications Frame assemblies are fundamental to many areas. The proposed method could automate frame assembly modelling in a viable way. It could benefit design and manufacture process significantly. Originality/value The proposed method expands the application of machine learning into a new field. It would be more useful than simple machine learning in industry. The proposed method is better than general heuristic algorithms. It outputs identical results when the inputs are the same. Meanwhile, the algorithmic complexity in worst situation is better than general heuristic algorithms.

scholarly journals Application of Machine Learning in Collaborative Filtering Recommender Systems

2018 ◽  
Vol 7 (4.38) ◽  
pp. 213
Author(s):  
Rajesh Kumar Ojha ◽  
Dr. Bhagirathi Nayak
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Intelligent Systems ◽  
Nearest Neighbors ◽  
Learning Technology ◽  
K Nearest Neighbors ◽  
Learning Technique

Recommender systems are one of the important methodologies in machine learning technologies, which is using in current business scenario. This article proposes a book recommender system using deep learning technique and k-Nearest Neighbors (k-NN) classification. Deep learning technique is one of the most effective techniques in the field of recommender systems. Recommender systems are intelligent systems in Machine Learning that can make difference from other algorithms. This article considers application of Machine Learning Technology and we present an approach based a recommender system. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based book recommender system. We analyze the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the projected algorithm is more precise over the existing algorithm, it also consumes less time and reliable than the existing methods.   

scholarly journals A 3D High Resolution Generative Deep-learning Network for Fluorescence Microscopy Image

2019 ◽  
Author(s):  
Zhou Hang ◽  
Li Shiwei ◽  
Huang Qing ◽  
Liu Shijie ◽  
Quan Tingwei ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Learning Technology ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Learning Network ◽  
Light Sheet Microscopy ◽  
Image Pairs ◽  
Deep Learning Network

AbstractDeep learning technology enables us acquire high resolution image from low resolution image in biological imaging free from sophisticated optical hardware. However, current methods require a huge number of the precisely registered low-resolution (LR) and high-resolution (HR) volume image pairs. This requirement is challengeable for biological volume imaging. Here, we proposed 3D deep learning network based on dual generative adversarial network (dual-GAN) framework for recovering HR volume images from LR volume images. Our network avoids learning the direct mappings from the LR and HR volume image pairs, which need precisely image registration process. And the cycle consistent network makes the predicted HR volume image faithful to its corresponding LR volume image. The proposed method achieves the recovery of 20x/1.0 NA volume images from 5x/0.16 NA volume images collected by light-sheet microscopy. In essence our method is suitable for the other imaging modalities.

scholarly journals Research on Defect Recognition of Lithium Battery Pole Piece Based on Deep Learning

2021 ◽  
Vol 261 ◽  
pp. 01021
Author(s):  
Jiwei Li ◽  
Linsheng Li ◽  
Changlu Xu
Keyword(s):  
Deep Learning ◽  
Network Model ◽  
Lithium Battery ◽  
High Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Accuracy Rate ◽  
Learning Network ◽  
Defect Recognition ◽  
Deep Learning Network

In the field of defect recognition, deep learning technology has the advantages of strong generalization and high accuracy compared with mainstream machine learning technology. This paper proposes a deep learning network model, which first processes the self-made 3, 600 data sets, and then sends them to the built convolutional neural network model for training. The final result can effectively identify the three defects of lithium battery pole pieces. The accuracy rate is 92%. Compared with the structure of the AlexNet model, the model proposed in this paper has higher accuracy.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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