Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic & Statistical Insights on their Limitations

<p>In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning exploration. We reviewed the literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994-2019 period) and found two emerging trends: an increasing interest in this domain over time, and a complexification of ANN models towards deep learning. Despite the relatively positive results claimed in those studies, we verified that far simpler (and traditional) models seem to offer similar predictive powers, if not better ones. Those include an exponential law for magnitude prediction, and a power law (approximated by a logistic regression or one artificial neuron) for aftershock prediction in space. Due to the structured, tabulated nature of earthquake catalogues, and the limited number of features so far considered, simpler and more transparent machine learning models than ANNs seem preferable at the present stage of research. Those baseline models follow first physical principles and are consistent with the known empirical laws of Statistical Seismology (e.g. the Gutenberg-Richter law), which are already known to have minimal abilities to predict large earthquakes.</p>

Neural Network Applications in Earthquake Prediction (1994–2019): Meta-Analytic and Statistical Insights on Their Limitations

Seismological Research Letters ◽

10.1785/0220200021 ◽

2020 ◽

Vol 91 (4) ◽

pp. 2330-2342 ◽

Cited By ~ 6

Author(s):

Arnaud Mignan ◽

Marco Broccardo

Keyword(s):

Neural Network ◽

Deep Learning ◽

Earthquake Prediction ◽

Approximate Solutions ◽

Statistical Seismology ◽

Network Applications ◽

Emerging Trends ◽

Intractable Problems ◽

Neural Network Applications

Abstract In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning exploration. We reviewed the literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994–2019 period) and found two emerging trends: an increasing interest in this domain over time and a complexification of ANN models toward deep learning. Despite the relatively positive results claimed in those studies, we verified that far simpler (and traditional) models seem to offer similar predictive powers, if not better ones. Those include an exponential law for magnitude prediction and a power law (approximated by a logistic regression or one artificial neuron) for aftershock prediction in space. Because of the structured, tabulated nature of earthquake catalogs, and the limited number of features so far considered, simpler and more transparent machine-learning models than ANNs seem preferable at the present stage of research. Those baseline models follow first physical principles and are consistent with the known empirical laws of statistical seismology (e.g., the Gutenberg–Richter law), which are already known to have minimal abilities to predict large earthquakes.

Detection of Abnormal Activity to Alert the Nearby Persons via M-DNN Based Surveillance System

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2021.16.61 ◽

2021 ◽

Vol 16 ◽

pp. 668-685

Author(s):

Shankargoud Patil ◽

Kappargaon S. Prabhushetty

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Support Vector ◽

Learning Approach ◽

Normal Behavior ◽

Machine Learning Approach ◽

Static Conditions ◽

Advanced System

In today's environment, video surveillance is critical. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various wary activities from the live tracking of footages. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In a theoretical setting, a deep learning approach is utilized to detect suspicious or normal behavior and sends an alarm to the nearby people if suspicious activity is predicted. In this paper, data fusion technique is used for feature extraction which gives an accurate outcome. Moreover, the classes are classified by the well effective machine learning approach of modified deep neural network (M-DNN), that predicts the classes very well. The proposed method gains 95% accuracy, as well the advanced system is contrast with previous methods like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). This approach is well fitted for dynamic and static conditions.

Data Processing Using Artificial Neural Networks

Dynamic Data Assimilation - Beating the Uncertainties ◽

10.5772/intechopen.91935 ◽

2020 ◽

Author(s):

Wesam Salah Alaloul ◽

Abdul Hannan Qureshi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Topology ◽

Basic Concept ◽

Ann Model ◽

Learning Models ◽

Learning Rules ◽

Invariance Problem ◽

Artificial Neural ◽

The artificial neural network (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. Artificial intelligence (AI) pyramid illustrates the evolution of ML approach to ANN and leading to deep learning (DL). Nowadays, researchers are very much attracted to DL processes due to its ability to overcome the selectivity-invariance problem. In this chapter, ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules and activated function). The basic concept of node and neutron has been explained, with the help of diagrams, leading to the ANN model and its operation. All the topics have been discussed in such a scheme to give the reader the basic concept and clarity in a sequential way from ANN perceptron model to deep learning models and underlying types.

Handwritten Digit Recognition using Convolutional Neural Network in Python with Tensorflow and Observe the Variation of Accuracies for Various Hidden Layers

10.20944/preprints201903.0039.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Fathma Siddique ◽

Shadman Sakib ◽

Md. Abu Bakr Siddique

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Deep Learning ◽

Handwritten Digit Recognition ◽

Diverse Range ◽

Digit Recognition ◽

Handwritten Digit ◽

Artificial Neural ◽

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

Automated Detection and Classification of COVID-19 from Chest X-ray Images Using Deep Learning

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9439 ◽

2020 ◽

Vol 17 (12) ◽

pp. 5457-5463

Author(s):

K. Shankar ◽

Eswaran Perumal

Keyword(s):

Neural Network ◽

Deep Learning ◽

Simulation Analysis ◽

Input Image ◽

Classification Model ◽

Superior Performance ◽

X Rays ◽

Applied Model ◽

Effective Diagnosis

In recent times, COVID-19 has appeared as a major threat to healthcare professionals, governments, and research communities over the world from its diagnosis to medication. Several research works have been carried out for obtaining the possible solutions for controlling the epidemic proficiently. An effective diagnosis of COVID-19 has been carried out using computed tomography (CT) scans and X-rays to examine the lung image. But it necessitates diverse radiologists and time to examine every report, which is a tedious task. Therefore, this paper presents an automated deep learning (DL) based COVID-19 detection and classification model. The presented model performs preprocessing, feature extraction and classification. In the earlier stage, median filtering (MF) technique is applied to preprocess the input image. Next, convolutional neural network (CNN) based VGGNet-19 model is applied as a feature extractor. At last, artificial neural network (ANN) is employed as a classification model to identify and classify the existence of COVID-19. An extensive set of simulation analysis takes place to ensure the superior performance of the applied model. The outcome of the experiments showcased the betterment interms of different measures.

Advances in Logistics, Operations, and Management Science - Demand Forecasting and Order Planning in Supply Chains and Humanitarian Logistics ◽

Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods

10.4018/978-1-7998-3805-0.ch005 ◽

2021 ◽

pp. 140-170

Author(s):

Asma Husna ◽

Saman Hassanzadeh Amin ◽

Bharat Shah

Keyword(s):

Neural Network ◽

Supply Chain ◽

Deep Learning ◽

Supply Chain Management ◽

Short Term Memory ◽

Demand Forecasting ◽

Learning Approaches ◽

Chain Management ◽

Lstm Network ◽

Supply chain management (SCM) is a fast growing and largely studied field of research. Forecasting of the required materials and parts is an important task in companies and can have a significant impact on the total cost. To have a reliable forecast, some advanced methods such as deep learning techniques are helpful. The main goal of this chapter is to forecast the unit sales of thousands of items sold at different chain stores located in Ecuador with holistic techniques. Three deep learning approaches including artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) are adopted here for predictions from the Corporación Favorita grocery sales forecasting dataset collected from Kaggle website. Finally, the performances of the applied models are evaluated and compared. The results show that LSTM network tends to outperform the other two approaches in terms of performance. All experiments are conducted using Python's deep learning library and Keras and Tensorflow packages.

Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

10.20944/preprints201903.0039.v2 ◽

2019 ◽

Author(s):

Fathma Siddique ◽

Shadman Sakib ◽

Md. Abu Bakr Siddique

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Deep Learning ◽

Text Categorization ◽

Stochastic Gradient Descent ◽

Diverse Range ◽

Handwritten Digit ◽

Artificial Neural ◽

Classification of Surface Pavement Cracks as Top-down, Bottom-up, and Cement-Treated Reflective Cracking Based on Deep Learning Methods

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0808 ◽

2021 ◽

Author(s):

Nirmal Dhakal ◽

Mostafa A. Elseifi ◽

Zia U. Zihan ◽

Zhongjie Zhang ◽

Christophe N. Fillastre ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Asphalt Concrete ◽

Fatigue Cracking ◽

Ann Model ◽

Reflective Cracking ◽

Top Down ◽

Bottom Up ◽

Concrete Layer ◽

The treatment and repair strategies of reflective and fatigue cracking that initiate at the pavement surface (i.e. top-down cracking) and at the bottom of the asphalt concrete layer (i.e. bottom-up cracking) are noticeably different. However, pavement engineers are facing difficulties in identifying these cracks in the field as they usually appear in visually identical patterns. The objective of this study was to develop Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) applications to differentiate and classify top-down, bottom-up, and cement-treated reflective cracking in in-service pavements using deep-learning models. The developed CNN model achieved an accuracy of 93.8% in the testing and 91% in the validation phases and the ANN model showed an overall accuracy of 92%. The ANN classification tool was developed based on variables related to pavement and crack characteristics including age, Average Daily Traffic , thickness of Asphalt Concrete layer, type of base, crack orientation and location.

Online multiclass EEG feature extraction and recognition using modified convolutional neural network method

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i5.pp4016-4026 ◽

2021 ◽

Vol 11 (5) ◽

pp. 4016

Author(s):

Haider Abdulkarim ◽

Mohammed Z. Al-Faiz

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Majority Voting ◽

Linear Discriminant ◽

Emerging Trends ◽

Accuracy Increase

<p>Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accuracy</p>

An Overview of Convolutional Neural Network: Its Architecture and Applications

10.20944/preprints201811.0546.v1 ◽

2018 ◽

Author(s):

Shadman Sakib ◽

Nazib Ahmed ◽

Ahmed Jawad Kabir ◽

Hridon Ahmed

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Deep Learning ◽

Learning Strategies ◽

Learning Tasks ◽

Artificial Neural ◽

The Many

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.