Development of deep learning neural network for ecological and medical images

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology also shows its effective in shape representation, contour detection and image smoothing. The experimental results in the morphological neural network shows this type of deep learning model is also effective in ecology datasets and medical dataset. Compared with CNN, the MNN could achieve a similar or better result in the following datasets. The chest X-ray images are notoriously difficult to analyze for the radiologists due to their noisy nature. The existing models based on convolutional neural networks contain a giant number of parameters and thus require multi-advanced GPUs to deploy. In this research, the morphological neural networks are developed to classify chest X-ray images, including the Pneumonia Dataset and the COVID-19 Dataset. A novel structure, which can self-learn a morphological dilation or erosion, is proposed for determining the most suitable depth of the adaptive layer. Experimental results on the chest X-ray dataset and the COVID-19 dataset show that the proposed model achieves the highest classification rate as comparing against the existing models. More significant improvement is that the proposed model reduces around 97% computational parameters of the existing models. Automatic identification of pneumonia on medical images has attracted intensive studies recently. The model for detecting pneumonia requires both a precise classification model and a localization model. A joint-task joint learning model with shared parameters is proposed to combine the classification model and segmentation model. To accurately classify and localize pneumonia area. Experimental results using the massive dataset of Radiology Society of North America have confirmed the efficiency of showing a test mean interception over union (IoU) of 89.27% and a mean precision of area detection result of 58.45% in segmentation model. Then, two new models are proposed to improve the performance of the original joint-task learning model. Two new modules are developed to improve both classification and segmentation accuracies in the first model. These modules including an image preprocessing module and an attention module. In the second model, a novel design is used to combine both convolutional layers and morphological layers with an attention mechanism. Experimental results performed on the massive dataset of the Radiology Society of North America have confirmed its superiority over other existing methods. The classification test accuracy is improved from 0.89 to 0.95, and the segmentation model achieves an improved mean precision result from 0.58 to 0.78. Finally, two weakly-supervised learning methods: class-saliency map and grad-cam, are used to highlight corresponding pixels or areas which have significant influence on the classification model, such that the refined segmentation can focus on the correct areas with high confidence

Gathering Customer’s Demand Data through Web 2.0 Community: Process and Architecture

It’s one of the most critical tasks for businesses to keep track customer’s responses to their products and services in this competitive business environment. With the emergence of Web 2.0 communities and social networking websites, a relatively new media in personal communication and knowledge sharing websites, firms can leverage this additional channel to their advantage by implementing a system to monitor and collect customer’s response data. The purpose of this paper is to introduce a data collection process and a system design architecture which can be used for such purpose

26P. Enterprise Blog Categorization and Business Value

As more enterprises adopt Web 2.0 technologies, enterprise blog (EB) has become a popular and an important business tool not only for internal management but also for external interfacing with suppliers, business partners, and customers. For customer management, EB brings together two contemporary business developments, enhanced customer involvement and new forms of customer experience management. EB has been adopted by many organizations for the purpose of involving customers in product development, acquiring new customers, and providing customer with interactive experiences. However, in addition to the EB infrastructure, the effectiveness and success of such tool is largely dependent on the content. The purpose of this paper is to present a framework categorizing a rather complex and fragmented EB content domain. The framework was verified using data of 78 large multinational corporation’s enterprise blogs. A systematic overview of EB’s value and business model is explored

The Impact of IOS Use and Interpersonal Ties on Digital Innovation: Insights from Boundary Spanning and Institutional Theories

Drawing upon the boundary spanning and institutional theories, this study investigates the influence of interorganizational systems (IOS) use and interpersonal ties between a firm and its suppliers on a firm’s digital innovation and how such effects are moderated by institutional distance between the firm and its suppliers. Based on a pilot test of 123 Chinese firms, our results find that a firm’s use of IOS significantly improves its digital innovation, while interpersonal ties between the firm and its suppliers do not significantly improve the firm’s digital innovation. Further, we find that institutional distance between the firm and its suppliers differentially moderates the influences of IOS use and interpersonal ties on digital innovation. Specifically, institutional distance negatively moderates the impact of IOS use on digital innovation yet positively moderates the impact of interpersonal ties on digital innovation. We further discuss the theoretical contributions and managerial implications of the current study

Leveraging Work-Related Stressors for Employee Innovation: The Moderating Role of Enterprise Social Networking Use

Enterprise social networking (ESN) techniques have been widely adopted by firms to provide a platform for public communication among employees. This study investigates how the relationships between stressors (i.e., challenge and hindrance stressors) and employee innovation are moderated by task-oriented and relationship-oriented ESN use. Since challenge-hindrance stressors and employee innovation are individual-level variables and task-oriented ESN use and relationship-oriented ESN use are team-level variables, we thus use hierarchical linear model to test this cross-level model. The results of a survey of 191 employees in 50 groups indicate that two ESN use types differentially moderate the relationship between stressors and employee innovation. Specifically, task-oriented ESN use positively moderates the effects of the two stressors on employee innovation, while relationship-oriented ESN use negatively moderates the relationship between the two stressors and employee innovation. In addition, we find that challenge stressors significantly improve employee innovation. Theoretical and practical implications are discussed