scholarly journals Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3923 ◽  
Author(s):  
Sonain Jamil ◽  
Fawad ◽  
MuhibUr Rahman ◽  
Amin Ullah ◽  
Salman Badnava ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Remote Monitoring ◽  
Weather Conditions ◽  
Visual Features ◽  
Support Vector ◽  
Security Threats ◽  
Open Research ◽  
Deep Feature ◽  
Improved Performance ◽  
Safety Applications

Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.

scholarly journals Optimization of PV Systems Using Data Mining and Regression Learner MPPT Techniques

2018 ◽  
Vol 10 (3) ◽  
pp. 1080
Author(s):  
Adedayo M. Farayola ◽  
Ali N Hasan ◽  
Ahmed Ali
Keyword(s):  
Gaussian Process Regression ◽  
Weather Conditions ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Improved Performance ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Power Point

<span>Supervised machine learning techniques such as artificial neural network (ANN) and ANFIS are powerful tools used to track the maximum power point (MPPT) in photovoltaic systems. However, these offline MPPT techniques still require large and accurate training data sets for successful tracking. This paper presents an innovative use of rational quadratic gaussian process regression (RQGPR) technique to generate the large and very accurate training data required for MPPT task. To confirm the effectiveness of the RQGPR technique, the combination of ANN and RQGPR as ANN-RQGPR technique results were compared with the conventional ANN technique results, and that of combined ANN and linear support vector machine regression as ANN-LSVM technique results under different weather conditions. Results show that ANN-RQGPR technique produced the overall best result and with an improved performance. </span>

scholarly journals Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

2021 ◽  
Vol 11 (21) ◽  
pp. 10464
Author(s):  
Muhammad Asam ◽  
Shaik Javeed Hussain ◽  
Mohammed Mohatram ◽  
Saddam Hussain Khan ◽  
Tauseef Jamal ◽  
...  
Keyword(s):  
Feature Space ◽  
Support Vector ◽  
Cyber Attack ◽  
Discrimination Power ◽  
Class Differences ◽  
Malware Classification ◽  
Deep Feature ◽  
Feature Spaces ◽  
Unseen Data ◽  
Improved Performance

Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space-based Malware classification (DBFS-MC). In the proposed DFS-MC framework, deep features are generated from the customized CNN architectures and are fed to a support vector machine (SVM) algorithm for malware classification, while, in the DBFS-MC framework, the discrimination power is enhanced by first combining deep feature spaces of two customized CNN architectures to achieve boosted feature spaces. Further, the detection of exceptional malware is performed by providing the deep boosted feature space to SVM. The performance of the proposed malware classification frameworks is evaluated on the MalImg malware dataset using the hold-out cross-validation technique. Malware variants like Autorun.K, Swizzor.gen!I, Wintrim.BX and Yuner.A is hard to be correctly classified due to their minor inter-class differences in their features. The proposed DBFS-MC improved performance for these difficult to discriminate malware classes using the idea of feature boosting generated through customized CNNs. The proposed classification framework DBFS-MC showed good results in term of accuracy: 98.61%, F-score: 0.96, precision: 0.96, and recall: 0.96 on stringent test data, using 40% unseen data.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

Smart paddy field monitoring system using deep learning and IoT

2021 ◽  
pp. 1063293X2198894
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Nithiyakanthan Kannan ◽  
Sridevi Narayanan ◽  
Chanki Pandey
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Paddy Field ◽  
New Technology ◽  
Support Vector ◽  
Nitrogen Status ◽  
Deep Feature ◽  
Per Capita ◽  
Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

Bit Error Rate Performance Analysis of Hybrid Subcarrier Intensity Modulation-Based FSO with Spatial Diversity in Various Weather Conditions

2019 ◽  
Vol 40 (3) ◽  
pp. 307-314 ◽  
Author(s):  
Rajat Kumar Giri ◽  
Bijayananda Patnaik
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Weather Conditions ◽  
Spatial Diversity ◽  
Intensity Modulation ◽  
Free Space Optical ◽  
Diversity Techniques ◽  
Link Distance ◽  
Simulation Results ◽  
Improved Performance

Abstract In this paper, we study the performance improvement of free space optical (FSO) communication system with spatial diversity techniques employing hybrid pulse position modulation-binary phase shift keying-subcarrier intensity modulation (PPM-BPSK-SIM). The involvement of multiple photo-detectors in diversity based FSO systems offers an effective way to overcome scntillation. In this paper, we have simulated the bit error rate (BER) with respect to different parameters like average SNR, link distance at various weather conditions. The simulation results are verified in Matlab environment with the mathematical analysis. The simulation results show that higher order single input multiple output (SIMO) system achieves better BER performance and hybrid PPM-BPSK-SIM has significant improved performance than the common modulation schemes like PPM, BPSK-SIM.

scholarly journals Performance optimization of polymeric porous membrane-based liquid desiccant air dehumidifier used in air conditioning system

2019 ◽  
Vol 11 (1) ◽  
pp. 55-71
Author(s):  
Ali Mohammad Jafarpour ◽  
Farivar Fazelpour ◽  
Seyyed Abbas Mousavi
Keyword(s):  
Performance Optimization ◽  
Air Conditioning ◽  
Weather Conditions ◽  
Porous Membrane ◽  
Operating Conditions ◽  
Regeneration System ◽  
Liquid Desiccant ◽  
Air Conditioning System ◽  
Air Stream ◽  
Improved Performance

AbstractIn this study an experimental design was developed to optimize the performance and structure of a membrane-based parallel-plate liquid desiccant dehumidifier used in air conditioning regeneration system which operates under high humidity weather conditions. We conducted a series of polymeric porous membranes with different compositions fabricated that were prepared with various weight percentages of polysulfone (PSU), mixed with N-methyl-2-pyrrolidone (NMP) and dimethyl form amide (DMF) solvents. Furthermore, the designed experiments were performed under various operating conditions, indicating that the dehumidification efficiency declines with increasing flow rate, temperature, and humidity. Consequently, a membrane with optimized porosity and moisture permeability was selected which resulted in eliminating the carryover of solution droplets in the air, largely due to separating the flow condition of liquid desiccant (Li Cl) and air. This specific design is also greatly benefited by removing the water vapor from the air stream. The results of mathematical model simulations indicate that the DMF solvent had higher dehumidification capability compared with that of NMP under the optimized operating conditions. Additionally, it can clarify the porosity of the membrane which plays a significant role in the overall performance. Therefore, the fabricated membrane produces fresh cool air, and it can be applied as a guiding sample for designing the membrane-based dehumidifier with improved performance.

UAV Lab, Open Research Platform for Unmanned Aerial Vehicles

2011 ◽  
pp. 175-186 ◽  
Author(s):  
Péter Bauer ◽  
Paw Yew Chai ◽  
Luigi Iannelli ◽  
Rohit Pandita ◽  
Gergely Regula ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Open Research ◽  
Research Platform

scholarly journals Exploiting Visual Features in Financial Time Series Prediction

2020 ◽  
Vol 14 (2) ◽  
pp. 61-76
Author(s):  
Adil Gürsel Karaçor ◽  
Turan Erman Erkan
Keyword(s):  
Financial Time Series ◽  
Time Series Prediction ◽  
Visual Features ◽  
Gradient Boosting ◽  
Support Vector ◽  
Foreign Exchange Rates ◽  
Extreme Gradient Boosting ◽  
Vector Machines ◽  
Direction Of Movement ◽  
Visual Properties

The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name ‘Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.

scholarly journals Evaluation of Selected Performance Properties of Nanoclay-Modified Asphalt Binders

2019 ◽  
Vol 271 ◽  
pp. 03006
Author(s):  
Mohammad N. Hassan ◽  
M M Tariq Morshed ◽  
Zahid Hossain
Keyword(s):  
Colloidal Stability ◽  
Asphalt Binder ◽  
Weather Conditions ◽  
Good Alternative ◽  
Asphalt Binders ◽  
Chemical Compositions ◽  
Modified Asphalt ◽  
Adverse Weather ◽  
Modified Asphalt Binder ◽  
Improved Performance

Asphalt binders are often modified with additives such as acid, polymer, or a combination of multiple additives to achieve improved performance to sustain heavy loads and adverse weather conditions. According to some previous researches, nanoclay can be a good alternative of currently practiced Styrene-Butadiene-Styrene (SBS) modification, and the former is expected to reduce the overall cost of the asphalt binder. Three types of nanoclay (Cloisite 10A, 11B, and 15A) were blended with asphalt binders prepared from two different sources (Arabian Crude and Canadian Crude). A blending protocol has been developed to blend nanoclay with the base binders. Mechanical properties including viscosity, rutting parameter have undergone significant changes after the nanoclay modification. It was also observed that nanoclay modified binders offer different moisture susceptibility while bonding with different aggregates; the nanoclay modified asphalt binder exhibits better bonding with gravel than sandstone. Mechanistic properties such as viscosity and rutting parameter are found to be highly correlated with the chemical compositions. Binders from the Canadian crude showed more colloidal stability than binders from the Arabian crude after nanoclay modification.

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