Evaluation Efficiency of Hybrid Deep Learning algorithms with Neural Network, Decision Tree and Boosting Methods for Predicting Groundwater Potential

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽

10.3390/s21154953 ◽

2021 ◽

Vol 21 (15) ◽

pp. 4953

Author(s):

Sara Al-Emadi ◽

Abdulla Al-Ali ◽

Abdulaziz Al-Ali

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Learning Algorithms ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Networks ◽

Detection And Identification ◽

Learning Techniques ◽

The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks

Drug Discovery Today ◽

10.1016/j.drudis.2018.06.016 ◽

2018 ◽

Vol 23 (10) ◽

pp. 1784-1790 ◽

Cited By ~ 51

Author(s):

Fahimeh Ghasemi ◽

Alireza Mehridehnavi ◽

Alfonso Pérez-Garrido ◽

Horacio Pérez-Sánchez

Keyword(s):

Neural Network ◽

Deep Learning ◽

Learning Algorithms ◽

Qsar Studies

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Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

Computational Intelligence in the Internet of Things - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-7955-7.ch007 ◽

2019 ◽

pp. 159-173

Author(s):

Jay Rodge ◽

Swati Jaiswal

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Deep Learning ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Learning Algorithms ◽

Activation Functions ◽

Comprehensive Overview ◽

Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

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Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation

Sensors ◽

10.3390/s20092574 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2574 ◽

Cited By ~ 1

Author(s):

Junhua Ye ◽

Xin Li ◽

Xiangdong Zhang ◽

Qin Zhang ◽

Wu Chen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Learning Algorithms ◽

Support Vector ◽

Classification Methods ◽

Pedestrian Navigation ◽

Mode Recognition ◽

Posture Recognition ◽

Motion Mode

Several pedestrian navigation solutions have been proposed to date, and most of them are based on smartphones. Real-time recognition of pedestrian mode and smartphone posture is a key issue in navigation. Traditional ML (Machine Learning) classification methods have drawbacks, such as insufficient recognition accuracy and poor timing. This paper presents a real-time recognition scheme for comprehensive human activities, and this scheme combines deep learning algorithms and MEMS (Micro-Electro-Mechanical System) sensors’ measurements. In this study, we performed four main experiments, namely pedestrian motion mode recognition, smartphone posture recognition, real-time comprehensive pedestrian activity recognition, and pedestrian navigation. In the procedure of recognition, we designed and trained deep learning models using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) networks based on Tensorflow framework. The accuracy of traditional ML classification methods was also used for comparison. Test results show that the accuracy of motion mode recognition was improved from 89.9 % , which was the highest accuracy and obtained by SVM (Support Vector Machine), to 90.74 % (LSTM) and 91.92 % (CNN); the accuracy of smartphone posture recognition was improved from 81.60 % , which is the highest accuracy and obtained by NN (Neural Network), to 93.69 % (LSTM) and 95.55 % (CNN). We give a model transformation procedure based on the trained CNN network model, and then obtain the converted . t f l i t e model, which can be run in Android devices for real-time recognition. Real-time recognition experiments were performed in multiple scenes, a recognition model trained by the CNN network was deployed in a Huawei Mate20 smartphone, and the five most used pedestrian activities were designed and verified. The overall accuracy was up to 89.39 % . Overall, the improvement of recognition capability based on deep learning algorithms was significant. Therefore, the solution was helpful to recognize comprehensive pedestrian activities during navigation. On the basis of the trained model, a navigation test was performed; mean bias was reduced by more than 1.1 m. Accordingly, the positioning accuracy was improved obviously, which is meaningful to apply DL in the area of pedestrian navigation to make improvements.

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Software Fault Prediction Using Deep Learning Algorithms

International Journal of Open Source Software and Processes ◽

10.4018/ijossp.2019100101 ◽

2019 ◽

Vol 10 (4) ◽

pp. 1-19

Author(s):

Osama Al Qasem ◽

Mohammed Akour

Keyword(s):

Neural Network ◽

Deep Learning ◽

Detection Rate ◽

Development Process ◽

Learning Algorithms ◽

Fault Prediction ◽

Software Faults ◽

Software Fault Prediction ◽

Development Lifecycle ◽

Software Fault

Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.

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An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

Energies ◽

10.3390/en14216928 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6928

Author(s):

Łukasz Wojtecki ◽

Sebastian Iwaszenko ◽

Derek B. Apel ◽

Tomasz Cichy

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Rock Mass ◽

Decision Tree ◽

Risk Prediction ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Hard Coal ◽

Underground Excavations

Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

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Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Sensors ◽

10.3390/s20061576 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1576 ◽

Cited By ~ 13

Author(s):

Li Zhu ◽

Lianghao Huang ◽

Linyu Fan ◽

Jinsong Huang ◽

Faming Huang ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Environmental Factors ◽

Recurrent Neural Network ◽

Landslide Susceptibility ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Predictive Rate

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

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Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms

Remote Sensing ◽

10.3390/rs12111838 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1838 ◽

Cited By ~ 8

Author(s):

Zhao Zhang ◽

Paulo Flores ◽

C. Igathinathane ◽

Dayakar L. Naik ◽

Ravi Kiran ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Standard Deviation ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Superior Performance ◽

Support Vector ◽

Unmanned Aerial Systems

The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging detection that can overcome or alleviate these shortcomings would be welcomed. This study proposed a systematic approach for wheat lodging detection in research plots (372 experimental plots), which consisted of using unmanned aerial systems (UAS) for aerial imagery acquisition, manual field evaluation, and machine learning algorithms to detect the occurrence or not of lodging. UAS imagery was collected on three different dates (23 and 30 July 2019, and 8 August 2019) after lodging occurred. Traditional machine learning and deep learning were evaluated and compared in this study in terms of classification accuracy and standard deviation. For traditional machine learning, five types of features (i.e. gray level co-occurrence matrix, local binary pattern, Gabor, intensity, and Hu-moment) were extracted and fed into three traditional machine learning algorithms (i.e., random forest (RF), neural network, and support vector machine) for detecting lodged plots. For the datasets on each imagery collection date, the accuracies of the three algorithms were not significantly different from each other. For any of the three algorithms, accuracies on the first and last date datasets had the lowest and highest values, respectively. Incorporating standard deviation as a measurement of performance robustness, RF was determined as the most satisfactory. Regarding deep learning, three different convolutional neural networks (simple convolutional neural network, VGG-16, and GoogLeNet) were tested. For any of the single date datasets, GoogLeNet consistently had superior performance over the other two methods. Further comparisons between RF and GoogLeNet demonstrated that the detection accuracies of the two methods were not significantly different from each other (p > 0.05); hence, the choice of any of the two would not affect the final detection accuracies. However, considering the fact that the average accuracy of GoogLeNet (93%) was larger than RF (91%), it was recommended to use GoogLeNet for wheat lodging detection. This research demonstrated that UAS RGB imagery, coupled with the GoogLeNet machine learning algorithm, can be a novel, reliable, objective, simple, low-cost, and effective (accuracy > 90%) tool for wheat lodging detection.

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Malaria Detection System Using Convolutional Neural Network Algorithm

Machine Learning and Deep Learning in Real-Time Applications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-3095-5.ch010 ◽

2020 ◽

pp. 219-230

Author(s):

Kanika Gautam ◽

Sunil Kumar Jangir ◽

Manish Kumar ◽

Jay Sharma

Keyword(s):

Neural Network ◽

Deep Learning ◽

Learning Algorithm ◽

Detection System ◽

Learning Algorithms ◽

Anopheles Mosquito ◽

Deep Learning Algorithm ◽

Blood Smears ◽

Neural Network Algorithm ◽

Modern Technologies

Malaria is a disease caused when a female Anopheles mosquito bites. There are over 200 million cases recorded per year with more than 400,000 deaths. Current methods of diagnosis are effective; however, they work on technologies that do not produce higher accuracy results. Henceforth, to improve the prediction rate of the disease, modern technologies need to be performed for obtain accurate results. Deep learning algorithms are developed to detect, learn, and determine the containing parasites from the red blood smears. This chapter shows the implementation of a deep learning algorithm to identify the malaria parasites with higher accuracy.

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