scholarly journals Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification

2019 ◽  
Vol 9 (3) ◽  
pp. 597
Author(s):  
Junhong Kim ◽  
Hyungseok Kim ◽  
Jaesun Park ◽  
Kyounghyun Mo ◽  
Pilsung Kang
Keyword(s):  
Neural Network ◽  
Semiconductor Manufacturing ◽  
Classification Model ◽  
Learning Models ◽  
Wafer Inspection ◽  
Map Coloring ◽  
Sorting Test ◽  
The Relationship ◽  
Bin Map ◽  
Machine Learning Models

A wafer bin map (WBM), which is the result of an electrical die-sorting test, provides information on which bins failed what tests, and plays an important role in finding defective wafer patterns in semiconductor manufacturing. Current wafer inspection based on WBM has two problems: good/bad WBM classification is performed by engineers and the bin code coloring scheme does not reflect the relationship between bin codes. To solve these problems, we propose a neural network-based bin coloring method called Bin2Vec to make similar bin codes are represented by similar colors. We also build a convolutional neural network-based WBM classification model to reduce the variations in the decisions made by engineers with different expertise by learning the company-wide historical WBM classification results. Based on a real dataset with a total of 27,701 WBMs, our WBM classification model significantly outperformed benchmarked machine learning models. In addition, the visualization results of the proposed Bin2Vec method makes it easier to discover meaningful WBM patterns compared with the random RGB coloring scheme. We expect the proposed framework to improve both efficiencies by automating the bad wafer classification process and effectiveness by assigning similar bin codes and their corresponding colors on the WBM.

First-Break Picking Classification Models Using Recurrent Neural Network

2021 ◽  
Author(s):  
Mohammed Ayub ◽  
SanLinn Kaka
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Contextual Information ◽  
Classification Model ◽  
Superior Performance ◽  
Learning Models ◽  
Neural Network Models ◽  
Minimum Number ◽  
Machine Learning Models

Abstract Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.

scholarly journals CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

2020 ◽  
Vol 34 (03) ◽  
pp. 2594-2601
Author(s):  
Arjun Akula ◽  
Shuai Wang ◽  
Song-Chun Zhu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Input Image ◽  
Classification Model ◽  
Learning Models ◽  
Fault Line ◽  
Semantic Level ◽  
Explainable Ai ◽  
Fault Lines ◽  
Classification Category

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

A Deep Learning Approach for Detection of Application Layer Attacks in Internet

2020 ◽  
pp. 175-188
Author(s):  
V. Punitha ◽  
C. Mala
Keyword(s):  
Deep Learning ◽  
Transport Layer ◽  
Classification Model ◽  
Learning Approach ◽  
Ddos Attacks ◽  
Application Layer ◽  
Learning Models ◽  
Technological Transformation ◽  
Application Deployment ◽  
Machine Learning Models

The recent technological transformation in application deployment, with the enriched availability of applications, induces the attackers to shift the target of the attack to the services provided by the application layer. Application layer DoS or DDoS attacks are launched only after establishing the connection to the server. They are stealthier than network or transport layer attacks. The existing defence mechanisms are unproductive in detecting application layer DoS or DDoS attacks. Hence, this chapter proposes a novel deep learning classification model using an autoencoder to detect application layer DDoS attacks by measuring the deviations in the incoming network traffic. The experimental results show that the proposed deep autoencoder model detects application layer attacks in HTTP traffic more proficiently than existing machine learning models.

Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake

2020 ◽  
Vol 36 (3) ◽  
pp. 1166-1187 ◽  
Author(s):  
Shohei Naito ◽  
Hiromitsu Tomozawa ◽  
Yuji Mori ◽  
Takeshi Nagata ◽  
Naokazu Monma ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Aerial Photographs ◽  
Learning Models ◽  
Visual Interpretation ◽  
Damage Classification ◽  
Kumamoto Earthquake ◽  
Machine Learning Models

This article presents a method for detecting damaged buildings in the event of an earthquake using machine learning models and aerial photographs. We initially created training data for machine learning models using aerial photographs captured around the town of Mashiki immediately after the main shock of the 2016 Kumamoto earthquake. All buildings are classified into one of the four damage levels by visual interpretation. Subsequently, two damage discrimination models are developed: a bag-of-visual-words model and a model based on a convolutional neural network. Results are compared and validated in terms of accuracy, revealing that the latter model is preferable. Moreover, for the convolutional neural network model, the target areas are expanded and the recalls of damage classification at the four levels range approximately from 66% to 81%.

scholarly journals Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

2018 ◽  
Vol 8 (12) ◽  
pp. 2663 ◽  
Author(s):  
Davy Preuveneers ◽  
Vera Rimmer ◽  
Ilias Tsingenopoulos ◽  
Jan Spooren ◽  
Wouter Joosen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Training Data ◽  
Learning Models ◽  
Traditional System ◽  
Blockchain Technology ◽  
Malicious Behavior ◽  
Machine Learning Models

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

scholarly journals CPT Data Interpretation Employing Different Machine Learning Techniques

Geosciences ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 265
Author(s):  
Stefan Rauter ◽  
Franz Tschuchnigg
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Random Forest ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
Cone Penetration ◽  
Tip Resistance ◽  
Machine Learning Models

The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. Since Machine Learning could play a key role in reducing the costs and time needed for a suitable site investigation program, the basic ability of Machine Learning models to classify soils from Cone Penetration Tests (CPT) is evaluated. To find an appropriate classification model, 24 different Machine Learning models, based on three different algorithms, are built and trained on a dataset consisting of 1339 CPT. The applied algorithms are a Support Vector Machine, an Artificial Neural Network and a Random Forest. As input features, different combinations of direct cone penetration test data (tip resistance qc, sleeve friction fs, friction ratio Rf, depth d), combined with “defined”, thus, not directly measured data (total vertical stresses σv, effective vertical stresses σ’v and hydrostatic pore pressure u0), are used. Standard soil classes based on grain size distributions and soil classes based on soil behavior types according to Robertson are applied as targets. The different models are compared with respect to their prediction performance and the required learning time. The best results for all targets were obtained with models using a Random Forest classifier. For the soil classes based on grain size distribution, an accuracy of about 75%, and for soil classes according to Robertson, an accuracy of about 97–99%, was reached.

scholarly journals Fraudulent Face Image Detection

2020 ◽  
Vol 32 ◽  
pp. 03005
Author(s):  
Rahul Awhad ◽  
Saurabh Jayswal ◽  
Adesh More ◽  
Jyoti Kundale
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Face Image ◽  
Support Vector ◽  
Learning Models ◽  
Image Detection ◽  
Software Applications ◽  
Support Vector Classifier ◽  
The Face ◽  
Machine Learning Models

Due to the growing advancements in technology, many software applications are being developed to modify and edit images. Such software can be used to alter images. Nowadays, an altered image is so realistic that it becomes too difficult for a person to identify whether the image is fake or real. Such software applications can be used to alter the image of a person’s face also. So, it becomes very difficult to identify whether the image of the face is real or not. Our proposed system is used to identify whether the image of a face is fake or real. The proposed system makes use of machine learning. The system makes use of a convolution neural network and support vector classifier. Both these machine learning models are trained using real as well as fake images. Both these trained models will take an image as an input and will determine whether the image is fake or real.

scholarly journals Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Tsehay Admassu Assegie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Liver Disease ◽  
Classification Model ◽  
Support Vector ◽  
Disease Prediction ◽  
Accuracy Score ◽  
Learning Models ◽  
Accuracy And Precision ◽  
Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.  

scholarly journals Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257069
Author(s):  
Jae-Geum Shim ◽  
Kyoung-Ho Ryu ◽  
Sung Hyun Lee ◽  
Eun-Ah Cho ◽  
Sungho Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Random Forest ◽  
Tracheal Tube ◽  
Pediatric Patients ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Objective To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning. Methods Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retrospective study. The optimal location of the tracheal tube was defined as the median of the distance between the upper margin of the first thoracic(T1) vertebral body and the lower margin of the third thoracic(T3) vertebral body. We applied four machine learning models: random forest, elastic net, support vector machine, and artificial neural network and compared their prediction accuracy to three formula-based methods, which were based on age, height, and tracheal tube internal diameter(ID). Results For each method, the percentage with optimal tracheal tube depth predictions in the test set was calculated as follows: 79.0 (95% confidence interval [CI], 73.5 to 83.6) for random forest, 77.4 (95% CI, 71.8 to 82.2; P = 0.719) for elastic net, 77.0 (95% CI, 71.4 to 81.8; P = 0.486) for support vector machine, 76.6 (95% CI, 71.0 to 81.5; P = 1.0) for artificial neural network, 66.9 (95% CI, 60.9 to 72.5; P < 0.001) for the age-based formula, 58.5 (95% CI, 52.3 to 64.4; P< 0.001) for the tube ID-based formula, and 44.4 (95% CI, 38.3 to 50.6; P < 0.001) for the height-based formula. Conclusions In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients.

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