Nature-inspired hybrid deep learning for race detection by face shape features

2020 ◽  
Vol 13 (3) ◽  
pp. 365-388
Author(s):  
Asha Sukumaran ◽  
Thomas Brindha
Keyword(s):  
Feature Extraction ◽  
Hybrid Algorithm ◽  
Shape Features ◽  
Error Performance ◽  
Color Feature ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Face Shape ◽  
Race Detection ◽  
Better Than

PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).FindingsThe performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.Originality/valueThis paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.

Attack detection in medical Internet of things using optimized deep learning: enhanced security in healthcare sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
J Aruna Santhi ◽  
G Vijaya Saradhi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Hybrid Algorithm ◽  
Attack Detection ◽  
Hospital Environment ◽  
Healthcare Sector ◽  
Content Type ◽  
Accurate Detection ◽  
Iot Devices ◽  
Better Than

PurposeThis paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.Design/methodology/approachThis paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.FindingsFrom the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.Originality/valueThis paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network

2020 ◽  
Vol 13 (3) ◽  
pp. 283-310 ◽  
Author(s):  
Ambaji S. Jadhav ◽  
Pushpa B. Patil ◽  
Sunil Biradar
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Gray Level ◽  
Content Type ◽  
Blood Vessel Segmentation ◽  
Computer Aided ◽  
Better Than

PurposeDiabetic retinopathy (DR) is a central root of blindness all over the world. Though DR is tough to diagnose in starting stages, and the detection procedure might be time-consuming even for qualified experts. Nowadays, intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various diseases. Therefore, a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark dataset.Design/methodology/approachThe proposed DR diagnostic procedure involves four main steps: (1) image pre-processing, (2) blood vessel segmentation, (3) feature extraction, and (4) classification. Initially, the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filter. In the next step, the blood vessel segmentation is carried out using a segmentation process with optimized gray-level thresholding. Once the blood vessels are extracted, feature extraction is done, using Local Binary Pattern (LBP), Texture Energy Measurement (TEM based on Laws of Texture Energy), and two entropy computations – Shanon's entropy, and Kapur's entropy. These collected features are subjected to a classifier called Neural Network (NN) with an optimized training algorithm. Both the gray-level thresholding and NN is enhanced by the Modified Levy Updated-Dragonfly Algorithm (MLU-DA), which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the NN. Finally, this classification error can correctly prove the efficiency of the proposed DR detection model.FindingsThe overall accuracy of the proposed MLU-DA was 16.6% superior to conventional classifiers, and the precision of the developed MLU-DA was 22% better than LM-NN, 16.6% better than PSO-NN, GWO-NN, and DA-NN. Finally, it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting DR.Originality/valueThis paper adopts the latest optimization algorithm called MLU-DA-Neural Network with optimal gray-level thresholding for detecting diabetic retinopathy disease. This is the first work utilizes MLU-DA-based Neural Network for computer-aided Diabetic Retinopathy diagnosis.

BER performance analysis of FSO using hybrid-SIM technique with APD receiver over weak and strong turbulence channels

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Rajat Kumar Giri
Keyword(s):  
Performance Improvement ◽  
System Performance ◽  
Avalanche Photodiode ◽  
Free Space Optical ◽  
Error Performance ◽  
Strong Turbulence ◽  
Meijer G Function ◽  
Hermite Approximation ◽  
Ber Performance ◽  
Better Than

Abstract In this paper, a hybrid-subcarrier-intensity-modulation (hybrid-SIM) technique for the performance improvement of free-space-optical (FSO) communication system has been proposed. Subsequently, for further error performance improvement, avalanche photodiode (APD) based receiver is used in the proposed system. The system performance is analyzed at various atmospheric turbulence levels over weak and strong turbulence channels. The bit error rate (BER) is theoretically derived using Gauss–Hermite approximation and Meijer-G function and it is simulated in the MATLAB environment. The simulation result shows that the BER performance of hybrid-SIM is better than BPSK-SIM technique irrespective of the channel types and also the significant BER performance improvement is observed by APD receiver.

A novel GNSS deformation feature extraction method based on ensemble improved LMD threshold denoising

2020 ◽  
Vol 14 (4) ◽  
pp. 445-453
Author(s):  
Qian Fan ◽  
Yiqun Zhu
Keyword(s):  
Feature Extraction ◽  
Spline Interpolation ◽  
Simulated Data ◽  
Mining Area ◽  
Deformation Monitoring ◽  
Denoising Method ◽  
Feature Extraction Method ◽  
Deformation Feature ◽  
Mode Decomposition ◽  
Better Than

AbstractIn order to solve the problem that the moving span of basic local mean decomposition (LMD) method is difficult to choose reasonably, an improved LMD method (ILMD), which uses three cubic spline interpolation to replace the sliding average, is proposed. On this basis, with the help of noise aided calculation, an ensemble improved LMD method (EILMD) is proposed to effectively solve the modal aliasing problem in original LMD. On the basis of using EILMD to effectively decompose the data of GNSS deformation monitoring series, GNSS deformation feature extraction model based on EILMD threshold denoising is given by means of wavelet soft threshold processing mode and threshold setting method in empirical mode decomposition denoising. Through the analysis of simulated data and the actual GNSS monitoring data in the mining area, the results show that denoising effect of the proposed method is better than EILMD, ILMD and LMD direct coercive denoising methods. It is also better than wavelet analysis denoising method, and has good adaptability. This fully demonstrates the feasibility and effectiveness of the proposed method in GNSS feature extraction.

Current CITE-ings from the popular and trade computing press: telework and telecommuting

2016 ◽  
Vol 33 (1) ◽  
pp. 21-22 ◽  
Author(s):  
Martin Kesselman
Keyword(s):  
Literature Review ◽  
Design Methodology ◽  
Virtual Reference ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Better Than

Purpose – This article examines Current CITE-ings from the Popular and Trade Computing Press, Telework and Telecommuting Design/methodology/approach – The methodology adopted is a literature review. Findings – Readily available technologies now allow librarians to perform most of their work-offsite. Some traditional building-based services such as reference, have been taken over by virtual reference and now even instruction offers options on par with or even better than classroombased questions such as a webinar that can be viewed and reviewed at any time or by having librarians embedded into various courseware packages. Researchlimitations/implications – Librarians no longer need be limited to a single library; groups of subject librarians can work together in the cloud to provide services to multiple universities. Originality/value – This article collates some articles from the non-library literature that mayprovide some ideas and review advantages and disadvantages for both the library and employee

Synthetic individual binaural audio delivery by pinna image processing

2014 ◽  
Vol 10 (3) ◽  
pp. 239-254 ◽  
Author(s):  
Simone Spagnol ◽  
Michele Geronazzo ◽  
Davide Rocchesso ◽  
Federico Avanzini
Keyword(s):  
Sound Source ◽  
Low Complexity ◽  
Head Orientation ◽  
Sound Sources ◽  
Content Type ◽  
Localization Test ◽  
Subjective Measurements ◽  
Head Related Transfer Function ◽  
Practical Implications ◽  
Better Than

Purpose – The purpose of this paper is to present a system for customized binaural audio delivery based on the extraction of relevant features from a 2-D representation of the listener’s pinna. Design/methodology/approach – The most significant pinna contours are extracted by means of multi-flash imaging, and they provide values for the parameters of a structural head-related transfer function (HRTF) model. The HRTF model spatializes a given sound file according to the listener’s head orientation, tracked by sensor-equipped headphones, with respect to the virtual sound source. Findings – A preliminary localization test shows that the model is able to statically render the elevation of a virtual sound source better than non-individual HRTFs. Research limitations/implications – Results encourage a deeper analysis of the psychoacoustic impact that the individualized HRTF model has on perceived elevation of virtual sound sources. Practical implications – The model has low complexity and is suitable for implementation on mobile devices. The resulting hardware/software package will hopefully allow an easy and low-tech fruition of custom spatial audio to any user. Originality/value – The authors show that custom binaural audio can be successfully deployed without the need of cumbersome subjective measurements.

Decision fusion-based approach for content-based image classification

2017 ◽  
Vol 10 (3) ◽  
pp. 310-331 ◽  
Author(s):  
Sudeep Thepade ◽  
Rik Das ◽  
Saurav Ghosh
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Image Recognition ◽  
Extraction Techniques ◽  
Data Set ◽  
Content Type ◽  
Knowledge Fusion ◽  
Knn Classifier ◽  
Query Classification ◽  
First Time

Purpose Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques. Design/methodology/approach Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. Findings The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose. Originality/value To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

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