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Article
Publication date: 15 April 2020

Xiaoliang Qian, Jing Li, Jianwei Zhang, Wenhao Zhang, Weichao Yue, Qing-E Wu, Huanlong Zhang, Yuanyuan Wu and Wei Wang

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract…

Abstract

Purpose

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

Design/methodology/approach

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Findings

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

Originality/value

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

Details

Sensor Review, vol. 40 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 24 March 2022

Elavaar Kuzhali S. and Pushpa M.K.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more…

Abstract

Purpose

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

Design/methodology/approach

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

Findings

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

Originality/value

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 20 October 2021

Jayalaxmi Anem, G. Sateeshkumar and R. Madhu

The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially…

26

Abstract

Purpose

The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially, pre-processing is done on EEG signal for quality improvement. Then, by using wavelet transform (WT) feature extraction is done. The artefacts present in the EEG are removed using deep convLSTM. This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.

Design/methodology/approach

Nowadays' EEG signals play vital role in the field of neurophysiologic research. Brain activities of human can be analysed by using EEG signals. These signals are frequently affected by noise during acquisition and other external disturbances, which lead to degrade the signal quality. Denoising of EEG signals is necessary for the effective usage of signals in any application. This paper proposes a new technique named as flower pollination fractional calculus optimisation (FPFCO) algorithm for the removal of artefacts from EEG signal through deep learning scheme. FPFCO algorithm is the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM. The existed FPO algorithm is used for solution update through global and local pollinations. In this case, the fractional calculus (FC) method attempts to include the past solution by including the second order derivative. As a result, the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization (FPO) method. Initially, 5 EEG signals are contaminated by artefacts such as EMG, EOG, EEG and random noise. These contaminated EEG signals are pre-processed to remove baseline and power line noises. Further, feature extraction is done by using WT and extracted features are applied to deep convLSTM, which is trained by proposed fractional calculus based flower pollination optimisation algorithm. FPFCO is used for the effective removal of artefacts from EEG signal. The proposed technique is compared with existing techniques in terms of SNR and MSE.

Findings

The proposed technique is compared with existing techniques in terms of SNR, RMSE and MSE.

Originality/value

100%.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 18 June 2019

Xianjin Zha, Haijuan Yang, Yalan Yan, Guanxiang Yan, Chengsong Huang and Kunfeng Liu

Microblogging as one kind of social media application provides an important information sharing platform. Adaptive information sharing is the combination of adaptive

Abstract

Purpose

Microblogging as one kind of social media application provides an important information sharing platform. Adaptive information sharing is the combination of adaptive information technologies (IT) use behavior and information sharing behavior and subsequently refers to adaptive use of IT oriented to information sharing. The purpose of this paper is to understand adaptive information sharing in the context of microblogging from the perspective of cognitive switching.

Design/methodology/approach

A research model was developed and survey data were collected. The partial least squares structural equation modeling was employed to verify the research model.

Findings

Adaptive information sharing is positively impacted by other people’s use, discrepancies and deliberate initiatives among which other people’s use is the key determinant. Meanwhile, task self-efficacy positively moderates the effect of other people’s use on adaptive information sharing.

Practical implications

Developers of microblogging should as far as possible create learning atmosphere and learning culture. With learning atmosphere and culture, more and more users could keep on learning from observing other people. Consequently, more and more users would be willing to try new features of microblogging to share information.

Originality/value

This study examines adaptive information sharing by extending adaptive IT use behavior from the levels of technology, system and feature to the information level, presenting a new lens for adaptive IT use and information sharing alike.

Details

Aslib Journal of Information Management, vol. 71 no. 4
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 26 January 2022

K. Venkataravana Nayak, J.S. Arunalatha, G.U. Vasanthakumar and K.R. Venugopal

The analysis of multimedia content is being applied in various real-time computer vision applications. In multimedia content, digital images constitute a significant part…

Abstract

Purpose

The analysis of multimedia content is being applied in various real-time computer vision applications. In multimedia content, digital images constitute a significant part. The representation of digital images interpreted by humans is subjective in nature and complex. Hence, searching for relevant images from the archives is difficult. Thus, electronic image analysis strategies have become effective tools in the process of image interpretation.

Design/methodology/approach

The traditional approach used is text-based, i.e. searching images using textual annotations. It consumes time in the manual process of annotating images and is difficult to reduce the dependency in textual annotations if the archive consists of large number of samples. Therefore, content-based image retrieval (CBIR) is adopted in which the high-level visuals of images are represented in terms of feature vectors, which contain numerical values. It is a commonly used approach to understand the content of query images in retrieving relevant images. Still, the performance is less than optimal due to the presence of semantic gap among the image content representation and human visual understanding perspective because of the image content photometric, geometric variations and occlusions in search environments.

Findings

The authors proposed an image retrieval framework to generate semantic response through the feature extraction with convolution network and optimization of extracted features using adaptive moment estimation algorithm towards enhancing the retrieval performance.

Originality/value

The proposed framework is tested on Corel-1k and ImageNet datasets resulted in an accuracy of 98 and 96%, respectively, compared to the state-of-the-art approaches.

Details

International Journal of Intelligent Unmanned Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 6 June 2019

Shuang-Shuang Liu

The conventional pedestrian detection algorithms lack in scale sensitivity. The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian…

Abstract

Purpose

The conventional pedestrian detection algorithms lack in scale sensitivity. The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection, based on deep residual network (DRN), to address such lacks.

Design/methodology/approach

First, the “Edge boxes” algorithm is introduced to extract region of interests from pedestrian images. Then, the extracted bounding boxes are incorporated to different DRNs, one is a large-scale DRN and the other one is the small-scale DRN. The height of the bounding boxes is used to classify the results of pedestrians and to regress the bounding boxes to the entity of the pedestrian. At last, a weighted self-adaptive scale function, which combines the large-scale results and small-scale results, is designed for the final pedestrian detection.

Findings

To validate the effectiveness and feasibility of the proposed algorithm, some comparison experiments have been done on the common pedestrian detection data sets: Caltech, INRIA, ETH and KITTI. Experimental results show that the proposed algorithm is adapted for the various scales of the pedestrians. For the hard detected small-scale pedestrians, the proposed algorithm has improved the accuracy and robustness of detections.

Originality/value

By applying different models to deal with different scales of pedestrians, the proposed algorithm with the weighted calculation function has improved the accuracy and robustness for different scales of pedestrians.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Abstract

Details

Rewriting Leadership with Narrative Intelligence: How Leaders Can Thrive in Complex, Confusing and Contradictory Times
Type: Book
ISBN: 978-1-78756-776-4

Article
Publication date: 11 November 2021

Sandeep Kumar Hegde and Monica R. Mundada

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio…

Abstract

Purpose

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

Design/methodology/approach

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

Findings

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Research limitations/implications

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

Practical implications

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Social implications

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

Originality/value

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 9 January 2020

Vishwanath. C. Burkapalli and Priyadarshini C. Patil

Indian food recognition can be considered as a case of fine-grained type visual recognition, where the several photos of same category generally have significant…

Abstract

Purpose

Indian food recognition can be considered as a case of fine-grained type visual recognition, where the several photos of same category generally have significant variability. Therefore, effective segmentation and classification technique is required to identify the particular cuisines and fine-grained analysis. The paper aims to discuss this issue.

Design/methodology/approach

In this paper, the authors provided an effective segmentation approach through the proposed edge adaptive (EA)-deep convolutional neural networks (DCNNs) model, where each input images are divided into patches in order to provide much efficient and accurate structural description of data.

Findings

EA-DCNNs starts with developing a coarse map of feature that obtained through DCNN, afterwards EA model is applied to construct the final segmented image.

Originality/value

The training model of EA-DCNN consists of pooling, rectified linear unit and convolution, which help convolutional network to optimize the performance of segmentation in a significant extent, which is much practical and relevant in the context of food image segmentation.

Details

International Journal of Intelligent Unmanned Systems, vol. 8 no. 4
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 28 February 2022

Rui Zhang, Na Zhao, Liuhu Fu, Lihu Pan, Xiaolu Bai and Renwang Song

This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic…

Abstract

Purpose

This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters.

Design/methodology/approach

To express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model.

Findings

The experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications.

Originality/value

This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

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