Fast Detection and Traceback-based Mitigation of Interest Flooding Attack

Natural products are the most important and commonly used in Traditional Chinese Medicine (TCM) for healthcare and disease prevention in East-Asia. Although the Meridian system of TCM was established several thousand years ago, the rationale of Meridian classification based on the ingredient compounds remains poorly understood. A core challenge for the traditional machine learning approaches for chemical activity prediction is to encode molecules into fixed length vectors but ignore the structural information of the chemical compound. Therefore, we apply a cost-sensitive graph convolutional neural network model to learn local and global topological features of chemical compounds, and discover the associations between TCM and their Meridians. In the experiments, we find that the performance of our approach with the area under the receiver operating characteristic curve (ROC-AUC) of 0.82 which is better than the traditional machine learning algorithm and also obtains 8%–13% improvement comparing with the state-of-the-art methods. We investigate the powerful ability of deep learning approach to learn the proper molecular descriptors for Meridian prediction and to provide novel insights into the complementary and alternative medicine of TCM.

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Revisiting the cosmic distance duality relation with machine learning reconstruction methods: the combination of HII galaxies and ultra-compact radio quasars

The European Physical Journal C ◽

10.1140/epjc/s10052-021-09713-5 ◽

2021 ◽

Vol 81 (10) ◽

Author(s):

Tonghua Liu ◽

Shuo Cao ◽

Sixuan Zhang ◽

Xiaolong Gong ◽

Wuzheng Guo ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Observational Data ◽

Duality Relation ◽

Learning Approaches ◽

Parametric Methods ◽

Compact Structure ◽

Reconstruction Methods ◽

Standard Cosmological Model ◽

Artificial Neural Network Ann

AbstractIn this paper, we carry out an assessment of cosmic distance duality relation (CDDR) based on the latest observations of HII galaxies acting as standard candles and ultra-compact structure in radio quasars acting as standard rulers. Particularly, two machine learning reconstruction methods [Gaussian Process (GP) and Artificial Neural Network (ANN)] are applied to reconstruct the Hubble diagrams from observational data. We show that both approaches are capable of reconstructing the current constraints on possible deviations from the CDDR in the redshift range $$z\sim 2.3$$ z ∼ 2.3 . Considering four different parametric methods of CDDR, which quantify deviations from the CDDR and the standard cosmological model, we compare the results of the two different machine learning approaches. It is observed that the validity of CDDR is in well agreement with the current observational data within $$1\sigma $$ 1 σ based on the reconstructed distances through GP in the overlapping redshift domain. Moreover, we find that ultra-compact radio quasars could provide $$10^{-3}$$ 10 - 3 -level constraints on the violation parameter at high redshifts, when combined with the observations of HII galaxies. In the framework of ANN, one could derive robust constraints on the violation parameter at a precision of $$10^{-2}$$ 10 - 2 , with the validity of such distance duality relation within $$2\sigma $$ 2 σ confidence level.

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Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Electronics ◽

10.3390/electronics10141694 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1694

Author(s):

Mathew Ashik ◽

A. Jyothish ◽

S. Anandaram ◽

P. Vinod ◽

Francesco Mercaldo ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Support Vector ◽

Malware Analysis ◽

Learning Approaches ◽

Dynamic Features ◽

System Calls ◽

Prevention Methods ◽

Structural Aspects

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

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Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽

10.3390/s21093068 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3068

Author(s):

Soumaya Dghim ◽

Carlos M. Travieso-González ◽

Radim Burget

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Processing ◽

Deep Learning ◽

The Other ◽

Support Vector ◽

Learning Approaches ◽

Microscopic Images ◽

Trained Neural Network ◽

Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

Journal of Sensors ◽

10.1155/2021/9998187 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Sergio V. Farias ◽

Osamu Saotome ◽

Haroldo F. Campos Velho ◽

Elcio H. Shiguemori

Keyword(s):

Neural Network ◽

Damage Detection ◽

Lamb Wave ◽

Learning Strategy ◽

Damage Identification ◽

Particle Collision ◽

Learning Approaches ◽

Propagation Methods ◽

Artificial Neural Network Ann ◽

Multiple Particle

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

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Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Water ◽

10.3390/w13192664 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2664

Author(s):

Sunil Saha ◽

Jagabandhu Roy ◽

Tusar Kanti Hembram ◽

Biswajeet Pradhan ◽

Abhirup Dikshit ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Landslide Susceptibility ◽

Learning Model ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Learning Approaches ◽

Statistical Measures ◽

Deep Learning Model

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

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Optical character recognition (OCR) system for Roman script & English language using Artificial Neural Network (ANN) classifier

2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS) ◽

10.1109/rains.2016.7764379 ◽

2016 ◽

Cited By ~ 4

Author(s):

Honey Mehta ◽

Sanjay Singla ◽

Aarti Mahajan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Character Recognition ◽

Optical Character Recognition ◽

English Language ◽

Optical Character ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Roman Script ◽

Ann Classifier

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Tagging terms in text

Terminology ◽

10.1075/term.21010.rig ◽

2022 ◽

Author(s):

Ayla Rigouts Terryn ◽

Véronique Hoste ◽

Els Lefever

Keyword(s):

Neural Network ◽

Machine Learning ◽

Language Processing ◽

Conditional Random Fields ◽

Traditional Approach ◽

Learning Approaches ◽

Term Extraction ◽

The Neural Network ◽

Predicted Probability ◽

Automatic Term Extraction

Abstract As with many tasks in natural language processing, automatic term extraction (ATE) is increasingly approached as a machine learning problem. So far, most machine learning approaches to ATE broadly follow the traditional hybrid methodology, by first extracting a list of unique candidate terms, and classifying these candidates based on the predicted probability that they are valid terms. However, with the rise of neural networks and word embeddings, the next development in ATE might be towards sequential approaches, i.e., classifying each occurrence of each token within its original context. To test the validity of such approaches for ATE, two sequential methodologies were developed, evaluated, and compared: one feature-based conditional random fields classifier and one embedding-based recurrent neural network. An additional comparison was added with a machine learning interpretation of the traditional approach. All systems were trained and evaluated on identical data in multiple languages and domains to identify their respective strengths and weaknesses. The sequential methodologies were proven to be valid approaches to ATE, and the neural network even outperformed the more traditional approach. Interestingly, a combination of multiple approaches can outperform all of them separately, showing new ways to push the state-of-the-art in ATE.

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Fundamental Categories of Artificial Neural Networks

Applications of Artificial Neural Networks for Nonlinear Data - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-4042-8.ch003 ◽

2021 ◽

pp. 30-64

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Artificial Neural Network Ann ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

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Fundamental Categories of Artificial Neural Networks

10.4018/978-1-6684-2408-7.ch001 ◽

2022 ◽

pp. 1-30

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Artificial Neural Network Ann ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

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