Search results

1 – 10 of 640
To view the access options for this content please click here
Article

Shuangshuang Liu and Xiaoling Li

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In…

Abstract

Purpose

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order to solve such problems, the purpose of this paper is to propose a novel image super-resolution algorithm based on improved generative adversarial networks (GANs) with Wasserstein distance and gradient penalty.

Design/methodology/approach

The proposed algorithm first introduces the conventional GANs architecture, the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction (SRWGANs-GP). In addition, a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction. The content loss is extracted from the deep model’s feature maps, and such features are introduced to calculate mean square error (MSE) for the loss calculation of generators.

Findings

To validate the effectiveness and feasibility of the proposed algorithm, a lot of compared experiments are applied on three common data sets, i.e. Set5, Set14 and BSD100. Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence. Compared with the baseline deep models, the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction. The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.

Originality/value

Compared with the state-of-the-art algorithms, the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.

Details

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

Keywords

To view the access options for this content please click here
Book part

Santi Furnari

Research has highlighted the cognitive nature of the business model intended as a cognitive representation describing a business’ value creation and value capture…

Abstract

Research has highlighted the cognitive nature of the business model intended as a cognitive representation describing a business’ value creation and value capture activities. Although the content of the business model has been extensively investigated from this perspective, less attention has been paid to the business model’s causal structure – that is the pattern of cause-effect relations that, in top managers’ or entrepreneurs’ understandings, link value creation and value capture activities. Building on the strategic cognition literature, this paper argues that conceptualizing and analysing business models as cognitive maps can shed light on four important properties of a business model’s causal structure: the levels of complexity, focus and clustering that characterize the causal structure and the mechanisms underlying the causal links featured in that structure. I use examples of business models drawn from the literature as illustrations to describe these four properties. Finally, I discuss the value of a cognitive mapping approach for augmenting extant theories and practices of business model design.

Details

Business Models and Modelling
Type: Book
ISBN: 978-1-78560-462-1

Keywords

To view the access options for this content please click here
Article

Minghua Wei and Feng Lin

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this…

Abstract

Purpose

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

Design/methodology/approach

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

Findings

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

Originality/value

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

To view the access options for this content please click here
Article

Minghua Wei

In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background…

Abstract

Purpose

In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.

Design/methodology/approach

The algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.

Findings

Compared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.

Originality/value

The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

To view the access options for this content please click here
Book part

Eric R. Sims

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear…

Abstract

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

Details

DSGE Models in Macroeconomics: Estimation, Evaluation, and New Developments
Type: Book
ISBN: 978-1-78190-305-6

Keywords

To view the access options for this content please click here
Article

Asma Mejri, Sonia Ayachi-Ghannouchi and Ricardo Martinho

The purpose of this paper is to measure the flexibility of business process models. The authors give the notions of flexible process distance, which corresponds to the…

Abstract

Purpose

The purpose of this paper is to measure the flexibility of business process models. The authors give the notions of flexible process distance, which corresponds to the number of change operations needed for transforming one process model into another, considering the different perspectives (functional, operational, behavioral, informational, and organizational).

Design/methodology/approach

The proposed approach is a quantitative-based approach to measure the flexibility of business process models. In this context, the authors presented a method to compute the distance between two process models. The authors measured the distance between a process model and a process variant in terms of the number of high-level change operations (e.g. to insert or delete actors) needed to transform the process model into the respective variant when a change occurred, considering the different perspectives and the flexible features.

Findings

To evaluate the flexibility-measurement approach, the authors performed a comprehensive simulation using an emergency care (EC) business process model and its variants. The authors used a real-world EC process and illustrated the possible changes faced in the emergency department (possible variants). Simulation results were promising because they fit the flexibility needs of the EC process users. This was validated using the authors’ previous work which consists in a guidance approach for business process flexibility.

Research limitations/implications

The authors defined six different distances between business process models, which are summarized in the definition of total process distance. However, changes in one perspective may lead to changes in other perspectives. For instance, adding a new activity may lead to adding a new actor.

Practical implications

The results of this study would help companies to obtain important information about their processes and to compare the desired level of flexibility with their actual process flexibility.

Originality/value

This study is probably the first flexibility-measurement approach which incorporates features for capturing changes affecting the functional, operational, informational, organizational, and behavioral perspectives as well as elements related to approaches enhancing flexibility.

Details

Business Process Management Journal, vol. 24 no. 4
Type: Research Article
ISSN: 1463-7154

Keywords

To view the access options for this content please click here
Article

Li Xiaoling

In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel…

Abstract

Purpose

In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks (TS-CNN) model.

Design/methodology/approach

According to the proposed algorithm, a five-layer neural network model was constructed to classify the electroencephalogram (EEG) signals. Firstly, the author designed a motor imagery-based BCI experiment, and four subjects were recruited to participate in the experiment for the recording of EEG signals. Then, after the EEG signals were preprocessed, the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels, respectively. Finally, the classification of motor imagery was completed by using two fully connected layers.

Findings

To validate the classification performance and efficiency of the proposed algorithm, the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm. Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery, reflecting on the introduced accuracy, precision, recall, ROC curve and F-score indexes.

Originality/value

The proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery, and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

To view the access options for this content please click here
Article

Ahmad Heidary-Sharifabad, Mohsen Sardari Zarchi, Sima Emadi and Gholamreza Zarei

This paper proposes a novel deep learning based method towards the identification of a pistachio tree cultivar from its image.

Abstract

Purpose

This paper proposes a novel deep learning based method towards the identification of a pistachio tree cultivar from its image.

Design/methodology/approach

The investigated scope of this study includes Iranian commercial pistachios (Jumbo, Long, Round and Super long) trees. Effective use of high-resolution images with standard deep models is addressed in this study. A novel image patches extraction method is also used to boost the number of samples and dataset augmentation. In the proposed method, handcrafted ORB features are used to detect and extract patches which may contain identifiable information. An innovative algorithm is proposed for searching and extracting these patches. After extracting patches from initial images, a Convolutional Neural Network, named EfficientNet-B1, was fine-tuned on it. In the testing phase, several patches were extracted from the prompted image using the ORB-based method, and the results of their prediction were consolidated. In this method, patch prediction scores were in descending order, sorted by the highest score in a list, and finally, the average of a few list tops was calculated and the final decision was made.

Findings

Examining the proposed method on the test images led to an achievement of a recognition rate of 97.2% accuracy. Investigation of decision-making in the test dataset could reveal that this method outperformed human experts.

Originality/value

Cultivar identification using deep learning methods, due to their high recognition speed, lack of specialist requirement, and independence from human decision-making error is considered as a breakthrough in horticultural science. Variety cultivars of pistachio trees possess variant characteristics or traits, accordingly recognising cultivars is crucial to reduce the costs, prevent damages and harvest the optimal yields.

Details

British Food Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0007-070X

Keywords

To view the access options for this content please click here
Article

Lukman E. Mansuri and D.A. Patel

Heritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals…

Abstract

Purpose

Heritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.

Design/methodology/approach

The artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”

Findings

This study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.

Practical implications

The study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.

Originality/value

For ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

To view the access options for this content please click here
Article

Jim McLoughlin, Jaime Kaminski, Babak Sodagar, Sabina Khan, Robin Harris, Gustavo Arnaudo and Sinéad Mc Brearty

The purpose of this paper is to develop a coherent and robust methodology for social impact measurement of social enterprises (SEs) that would provide the conceptual and…

Abstract

Purpose

The purpose of this paper is to develop a coherent and robust methodology for social impact measurement of social enterprises (SEs) that would provide the conceptual and practical bases for training and embedding.

Design/methodology/approach

The paper presents a holistic impact measurement model for SEs, called social impact for local economies (SIMPLEs). The SIMPLE impact model and methodology have been tried and tested on over 40 SEs through a series of three day training courses, and a smaller number of test cases for embedding. The impact model offers a five‐step approach to impact measurement called SCOPE IT; MAP IT; TRACK IT; TELL IT and EMBED IT. These steps help SE managers to conceptualise the impact problem; identify and prioritise impacts for measurement; develop appropriate impact measures; report impacts and embed the results in management decision making.

Findings

Preliminary qualitative feedback from participants reveals that while the SIMPLE impact training delivers positive learning experiences on impact measurement and prompts, in the majority of cases, the intensions to implement impact systems, the majority feels the need for follow up embedding support. Paricipant's see value in adopting the SIMPLE approach to support business planning processes. Feedback from two SEs which has receives in‐house facilitates embedding support clearly demonstrates the benefits of working closely with an organisation's staff team to enable effective implementation.

Research limitations/implications

Some key future research challenges are identified as follows: systematically research progress in implementation after training for those participants that do not have facilitated embedding; to further test and develop embedding processes and models (using SIMPLE and other methods) with more SE organisations to identify best practices.

Originality/value

The SIMPLE fills a gap as a tool for holistic impact thinking that offers try and test accessible steps, with robust measures. The innovative steps take SEs through all key impact thought processes from conceptualisation to embedding guidance, feeding into business planning and strategic decision‐making processes. The comparison between the limitations of stand alone impact training and the benefits of facilitated embedding processes is instructive.

Details

Social Enterprise Journal, vol. 5 no. 2
Type: Research Article
ISSN: 1750-8614

Keywords

1 – 10 of 640