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A Multi-Label Classification with an Adversarial-Based Denoising Autoencoder for Medical Image Annotation
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Chai, Yidong; Liu, Hongyan; Xu, Jie; Samtani, Sagar; Jiang, Yuanchun; Liu, Haoxin

Abstract: Medical image annotation aims to automatically describe the content of medical i ... Expand

Abstract: Medical image annotation aims to automatically describe the content of medical images. It helps doctors to understand the content of medical images and make better informed decisions like diagnoses. Existing methods mainly follow the approach for natural images and fail to emphasize the object abnormalities, which is the essence of medical images annotation. In light of this, we propose to transform the medical image annotation to a multi-label classification problem, where object abnormalities are focused directly. However, extant multi-label classification studies rely on arduous feature engineering, or do not solve label correlation issues well in medical images. To solve these problems, we propose a novel deep learning model where a frequent pattern mining component and an adversarial-based denoising autoencoder component are introduced. Extensive experiments are conducted on a real retinal image dataset to evaluate the performance of the proposed model. Results indicate that the proposed model significantly outperforms image captioning baselines and multi-label classification baselines. Collapse

Semantic filters: residual neural network

Topics: workstation

Methods: data transformation autoencoder experiment deep learning literature study
A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

2023 | International Conference on Information Systems | Citations: 0
Authors: Dinh, Minh; Le, Mau Toan; Bui, Triet; Mai, Minh; Tran, Long; Nguyen, Nhan; Vo, Tan Hoang

Abstract: Machine learning techniques are successful for optical character recognition ta ... Expand

Abstract: Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively. Collapse

Semantic filters: residual neural network

Topics: healthcare data digital transformation database system mobile system data security

Methods: experiment image processing deep learning computational algorithm machine learning
LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Joshi, Amogh Manoj; Nayak, Deepak Ranjan; Das, Dibyasundar; Zhang, Yudong

Abstract: Recent years have witnessed a rise in employing deep learning methods, especiall ... Expand

Abstract: Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. Collapse

Semantic filters: residual neural network

Topics: covid 19

Methods: computed tomography ImageNet ablation analysis experiment machine learning
Visualizing Convolutional Neural Network Models’ Sensitivity to Nonnatural Data Order
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2023 | Information Systems Frontiers | Citations: 0
Authors: Klepetko, Randy; Krishnan, Ram

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition techno ... Expand

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition technology and found applications in various nonimage-related fields. For nonnatural data, such as cybersecurity packets, in which the data sample order is not defined by nature, some models trained on certain orderings of data perform better than when trained with other orderings. Some orderings create patterns from which the CNN extracts better features. Understanding how to best order the training data to improve CNN performance is not well-studied. In this study, we investigate this problem by examining malware infections using different CNN models and include visualization tools to enhance our analysis. We define a functional algorithm for ordering and show that order importance in CNNs is model dependent. In addition, we show that depending on the model, statistical relationships are crucial in establishing order with better performance. Collapse

Semantic filters: residual neural network

Topics: malware mobile system cloud computing infrastructure as a service

Methods: experiment descriptive statistic machine learning computational algorithm Keras
Explanatory Interactive Machine Learning
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2023 | Business & Information Systems Engineering | Citations: 1
Authors: Pfeuffer, Nicolas; Baum, Lorenz; Stammer, Wolfgang; Abdel-Karim, Benjamin M.; Schramowski, Patrick; Bucher, Andreas M.; Hügel, Christian; Rohde, Gernot; Kersting, Kristian; Hinz, Oliver

Abstract: The most promising standard machine learning methods can deliver highly accurate ... Expand

Abstract: The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research. Collapse

Semantic filters: residual neural network

Topics: explainable artificial intelligence database system crowdsourcing artificial intelligence cloud computing

Methods: machine learning action design research case study computational algorithm design science
How do you say it matters? A multimodal analytics framework for product return prediction in live streaming e-commerce
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2023 | Decision Support Systems | Citations: 0
Authors: Xu, Wei; Zhang, Xiaohan; Chen, Runyu; Yang, Zhan

Abstract: As a new shopping paradigm, live streaming e-commerce has undergone rapid develo ... Expand

Abstract: As a new shopping paradigm, live streaming e-commerce has undergone rapid development in recent years. However, a higher return rate than that of traditional e-commerce not only affects the consumption experience of users but also is detrimental to the development of the entire industry. Previous studies on product returns have mainly focused on traditional e-commerce, with few studies considering signals in unstructured data formats. The main contribution of our research is the design of a multimodal analytics framework to predict product returns in live streaming e-commerce settings. Multimodal features (e.g., visual, textual, and acoustic) and their correlations extracted from different data sources (product descriptions and live streaming records) are considered in our prediction model. In addition, to better study the impact of anchor speech during live streaming, we propose an acoustic feature extraction model called “OpenTransformer,” which extracts acoustic features of anchor audio and outperforms baselines. We have conducted experiments based on a real-world dataset collected from Taobao live streaming. The experimental results demonstrate the effectiveness of using multimodal signals from products and anchors for product return prediction. The managerial implications of our research are that live streaming platforms and potential consumers can prejudge which sold products will have high return rates in a timely fashion, and based on these predictions, merchants and anchors can better design their product descriptions and speech behaviors. This study thus supports more effective development of live streaming e-commerce markets. Collapse

Semantic filters: residual neural network

Topics: electronic commerce Python user behavior live streaming platform video streaming service

Methods: experiment transformer model machine learning residual neural network deep learning
Identification of Travel Styles by Learning from Consumer-generated Images in Online Travel Communities
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2022 | Information & Management | Citations: 0
Authors: Brusch, Ines

Abstract: A picture is worth a thousand words”: Never has this adage been more meaningful ... Expand

Abstract: A picture is worth a thousand words”: Never has this adage been more meaningful than it is today. Online social media is driving the growth of unstructured image data. Unstructured data must be structured to be informative and thereby contribute to user understanding and revenue generation. Hitherto, companies have only been able to accomplish this through tedious manual work. This paper demonstrates how image data can be analyzed automatically using a combination of image analysis methods and fuzzy cluster algorithms to predict user preferences, which companies can then use to make targeted offers. Several methods, including support vector machines (SVMs) and convolutional neural networks (CNNs), are benchmarked across various cases of image data taken from an online travel community. Depending on the images’ diversity either a SVM or a CNN provides the best basis for preference prediction. Collapse

Semantic filters: residual neural network

Topics: database system marketing management internet technology website analytical information system

Methods: cluster analysis support vector machine logistic regression computational algorithm convolutional neural network
AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches
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2022 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Chen, Mu-Yen; Fan, Min-Hsuan; Huang, Li-Xiang

Abstract: In recent years, vehicular networks have become increasingly large, heterogeneou ... Expand

Abstract: In recent years, vehicular networks have become increasingly large, heterogeneous, and dynamic, making it difficult to meet strict requirements of ultralow latency, high reliability, high security, and massive connections for next generation (6G) networks. Recently, deep learning (DL) has emerged as a powerful artificial intelligence (AI) technique to optimize the efficiency and adaptability of vehicle and wireless communication. However, rapidly increasing absolute numbers of vehicles on the roads are leading to increased automobile accidents, many of which are attributable to drivers interacting with their mobile phones. To address potentially dangerous driver behavior, this study applies deep learning approaches to image recognition to develop an AI-based detection system that can detect potentially dangerous driving behavior. Multiple convolutional neural network (CNN)-based techniques including VGG16, VGG19, Densenet, and Openpose were compared in terms of their ability to detect and identify problematic driving. Collapse

Semantic filters: residual neural network

Topics: extensible markup language enterprise architecture Python open source evaluation criteria

Methods: machine learning deep learning experiment convolutional neural network TensorFlow
Real-Time Purchase Prediction Using Retail Video Analytics

2022 | International Conference on Information Systems | Citations: 0
Authors: Ghose, Anindya; Li, Beibei; Li, Rubing; Xu, Kaiquan

Abstract: The proliferation of video data in retail marketing brings opportunities for re ... Expand

Abstract: The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape. Collapse

Semantic filters: residual neural network

Topics: consumer behavior marketing management shapley additive explanation application programming interface usability

Methods: deep learning machine learning image processing biometric measurement facial recognition system
Conspicuous display through photo sharing in online reviews: Evidence from an online travel platform
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2022 | Information & Management | Citations: 0
Authors: Hu, Xin; Luo, Jifeng; Wu, Zhiyan

Abstract: Compared to text, photos are more conspicuous and suited for self-presentation. ... Expand

Abstract: Compared to text, photos are more conspicuous and suited for self-presentation. Although other motivations, such as helping others, partly account for photo sharing in online reviews, we emphasize a model of social status in which a conspicuous display of distant tourism products is used as a signal of an individual’s status. Drawing on self-enhancement theory, we test hypotheses concerning exhibitionistic motivation in photo posting using a novel dataset of group tourism products. An instrumental variable method is used to address the endogeneity issue. We find that the distance between departure and destination, an indicator of cost and time spent, has a positive effect on the inclination to share photos. But people of higher status refrain from the conspicuous display of their photos. Moreover, the distance effect on photo sharing is mitigated by status within a website, suggesting that people assign different marginal values to the conspicuous display of tourism products at the same distance. This study contributes to the understanding of photo-sharing behavior in online reviews and can help platform managers build strategies to enhance reach and engagement. Collapse

Semantic filters: residual neural network

Topics: online review website application programming interface social media word of mouth

Methods: data transformation principal component analysis deep learning robustness check qualitative content analysis

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Found 23 articles

A Multi-Label Classification with an Adversarial-Based Denoising Autoencoder for Medical Image Annotation

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Visualizing Convolutional Neural Network Models’ Sensitivity to Nonnatural Data Order

Explanatory Interactive Machine Learning

How do you say it matters? A multimodal analytics framework for product return prediction in live streaming e-commerce

Identification of Travel Styles by Learning from Consumer-generated Images in Online Travel Communities

AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches

Real-Time Purchase Prediction Using Retail Video Analytics

Conspicuous display through photo sharing in online reviews: Evidence from an online travel platform