scholarly journals Deep Learning Approach for Building Detection Using LiDAR–Orthophoto Fusion

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Faten Hamed Nahhas ◽  
Helmi Z. M. Shafri ◽  
Maher Ibrahim Sameen ◽  
Biswajeet Pradhan ◽  
Shattri Mansor
Keyword(s):  
Deep Learning ◽  
Dimensionality Reduction ◽  
Batch Size ◽  
Support Vector ◽  
Detection Accuracy ◽  
Building Detection ◽  
Testing Area ◽  
Building Recognition ◽  
Proposed Model ◽  
High Level

This paper reports on a building detection approach based on deep learning (DL) using the fusion of Light Detection and Ranging (LiDAR) data and orthophotos. The proposed method utilized object-based analysis to create objects, a feature-level fusion, an autoencoder-based dimensionality reduction to transform low-level features into compressed features, and a convolutional neural network (CNN) to transform compressed features into high-level features, which were used to classify objects into buildings and background. The proposed architecture was optimized for the grid search method, and its sensitivity to hyperparameters was analyzed and discussed. The proposed model was evaluated on two datasets selected from an urban area with different building types. Results show that the dimensionality reduction by the autoencoder approach from 21 features to 10 features can improve detection accuracy from 86.06% to 86.19% in the working area and from 77.92% to 78.26% in the testing area. The sensitivity analysis also shows that the selection of the hyperparameter values of the model significantly affects detection accuracy. The best hyperparameters of the model are 128 filters in the CNN model, the Adamax optimizer, 10 units in the fully connected layer of the CNN model, a batch size of 8, and a dropout of 0.2. These hyperparameters are critical to improving the generalization capacity of the model. Furthermore, comparison experiments with the support vector machine (SVM) show that the proposed model with or without dimensionality reduction outperforms the SVM models in the working area. However, the SVM model achieves better accuracy in the testing area than the proposed model without dimensionality reduction. This study generally shows that the use of an autoencoder in DL models can improve the accuracy of building recognition in fused LiDAR–orthophoto data.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals Deep Learning Assisted Covid-19 Detection using full CT-scans

2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Detection Accuracy ◽  
Residual Network ◽  
Excellent Performance ◽  
Examination Procedure ◽  
Medical Institutions ◽  
Proposed Model ◽  
Learning Technique ◽  
Intensive Approach

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>

Classification of plant diseases using machine and deep learning

2021 ◽  
Vol 11 (1) ◽  
pp. 491-508
Author(s):  
Monika Lamba ◽  
Yogita Gigras ◽  
Anuradha Dhull
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Early Recognition ◽  
Activation Function ◽  
Plant Diseases ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Proposed Model

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

scholarly journals Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5018 ◽  
Author(s):  
Kyu-Won Jang ◽  
Jong-Hyeok Choi ◽  
Ji-Hoon Jeon ◽  
Hyun-Seok Kim
Keyword(s):  
Support Vector Machine ◽  
Gas Sensors ◽  
Learning Algorithm ◽  
Support Vector ◽  
Detection Accuracy ◽  
Minimum Concentration ◽  
Industrial Sites ◽  
Combustible Gas ◽  
Proposed Model ◽  
Combustible Gases

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

scholarly journals Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5000 ◽  
Author(s):  
Zhuangzhuang Zhou ◽  
Qinghua Lu ◽  
Zhifeng Wang ◽  
Haojie Huang
Keyword(s):  
Specular Reflection ◽  
Receptive Fields ◽  
Ground Truth ◽  
Detection Performance ◽  
Support Vector ◽  
Detection Accuracy ◽  
Irregular Surfaces ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Improved Model

The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.

scholarly journals Deep Learning Based Biomedical Literature Classification Using Criteria of Scientific Rigor

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1253
Author(s):  
Muhammad Afzal ◽  
Beom Joo Park ◽  
Maqbool Hussain ◽  
Sungyoung Lee
Keyword(s):  
Deep Learning ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biomedical Literature ◽  
Training Dataset ◽  
Support Vector ◽  
Scientific Rigor ◽  
Cochrane Reviews ◽  
Cumulative Score ◽  
Proposed Model

A major blockade to support the evidence-based clinical decision-making is accurately and efficiently recognizing appropriate and scientifically rigorous studies in the biomedical literature. We trained a multi-layer perceptron (MLP) model on a dataset with two textual features, title and abstract. The dataset consisting of 7958 PubMed citations classified in two classes: scientific rigor and non-rigor, is used to train the proposed model. We compare our model with other promising machine learning models such as Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosted Tree (GBT) approaches. Based on the higher cumulative score, deep learning was chosen and was tested on test datasets obtained by running a set of domain-specific queries. On the training dataset, the proposed deep learning model obtained significantly higher accuracy and AUC of 97.3% and 0.993, respectively, than the competitors, but was slightly lower in the recall of 95.1% as compared to GBT. The trained model sustained the performance of testing datasets. Unlike previous approaches, the proposed model does not require a human expert to create fresh annotated data; instead, we used studies cited in Cochrane reviews as a surrogate for quality studies in a clinical topic. We learn that deep learning methods are beneficial to use for biomedical literature classification. Not only do such methods minimize the workload in feature engineering, but they also show better performance on large and noisy data.

scholarly journals A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps

2020 ◽  
Vol 25 (3) ◽  
pp. 373-382
Author(s):  
He Yu ◽  
Zaike Tian ◽  
Hongru Li ◽  
Baohua Xu ◽  
Guoqing An
Keyword(s):  
Deep Learning ◽  
Graph Model ◽  
Parameter Tuning ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Deep Feature ◽  
Proposed Model ◽  
Vector Machines ◽  
Deep Feature Extraction

Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.

scholarly journals Visual Detection for Android Malware using Deep Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 152-156
Keyword(s):  
Deep Learning ◽  
Main Idea ◽  
Mobile Internet ◽  
Android Application ◽  
Analysis Model ◽  
Android Malware ◽  
Proposed Model ◽  
Learning Technique ◽  
Android Applications ◽  
High Level

The most serious threats to the current mobile internet are Android Malware. In this paper, we proposed a static analysis model that does not need to understand the source code of the android applications. The main idea is as most of the malware variants are created using automatic tools. Also, there are special fingerprint features for each malware family. According to decompiling the android APK, we mapped the Opcodes, sensitive API packages, and high-level risky API functions into three channels of an RGB image respectively. Then we used the deep learning technique convolutional neural network to identify Android application as benign or as malware. Finally, the proposed model succeeds to detect the entire 200 android applications (100 benign applications and 100 malware applications) with an accuracy of over 99% as shown in experimental results.

scholarly journals Deep Learning Assisted Covid-19 Detection using full CT-scans

2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Detection Accuracy ◽  
Residual Network ◽  
Excellent Performance ◽  
Examination Procedure ◽  
Medical Institutions ◽  
Proposed Model ◽  
Learning Technique ◽  
Intensive Approach

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>

scholarly journals A Genetic Algorithm based Hybrid Deep Learning Approach for Heart Disease Prediction

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Khushbu Verma ◽  
Ankit Singh Bartwal ◽  
Mathura Prasad Thapliyal
Keyword(s):  
Genetic Algorithm ◽  
Heart Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Support Vector ◽  
Disease Prediction ◽  
The Past ◽  
Lifestyle Choices ◽  
Proposed Model ◽  
Tree Algorithms

People nowadays suffer from a variety of heart ailments as a result of the environment and their lifestyle choices. As a result, analyzing sickness at an early stage becomes a critical responsibility. Data mining uses disease data to uncover important knowledge. In this research paper, we employ the hybrid combination of a Genetic Algorithm based Feature selection and Ensemble Deep Neural Network Model for Heart Disease prediction. In this algorithm, we used a 0.04 learning rate and Adam optimizer was used for enhancement of the proposed model. The proposed algorithm has come to 98% accuracy of heart disease prediction, which is higher than the past approaches. Other exist models such as random forest, logistic regression, support vector machine, Decision tree algorithms have taken a higher time and give less accuracy compare to the proposed hybrid deep learning-based approach.

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