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1.
This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa. 相似文献
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Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods. 相似文献
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针对基于局部熵进行加密图像视觉安全性评估存在块效应的局限性,引入图像的边缘特征,通过共有边缘来衡量加密图像与原始图像的边缘相似度,消除了块效应。由于局部熵对加密等级低的图像不敏感,边缘相似度对加密等级高的图像不敏感,将两个评估方法进行自适应融合,提出[SLEES](Local Entropy and Edge Similarity,[SLEES])指标。通过改变图像像素位置和图像像素值的加密方式处理图像和视频帧进行测试,实验结果表明,[SLEES]指标相比传统评估指标有更好的鲁棒性,评估范围更广。 相似文献
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We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency. 相似文献
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Social media has been widely used for emergency communication both in disaster-affected areas and unaffected areas. Comparing emotional reaction and information propagation between on-site users and off-site users from a spatiotemporal perspective can help better comprehend collective human behavior during natural disasters. In this study, we investigate sentiment and retweet patterns of disaster-affected areas and disaster-unaffected areas at different stages of Hurricane Harvey. The results show that off-site tweets were more negative than on-site tweets, especially during the disaster. As for retweet patterns, indifferent-neutral and positive tweets spread broader than mixed-neutral and negative tweets. However, negative tweets spread faster than positive tweets, which reveals that social media users were more sensitive to negative information in disaster situations. With the development of the disaster, social media users were more sensitive to on-site positive messages than off-site negative posts. This data-driven study reveals the significant effect of sentiment expression on the publication and re-distribution of disaster-related messages. It generates implications for emergency communication and disaster management. 相似文献
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Aishwarya
Srivastava Siddhant Aggarwal Amy Apon Edward Duffy Ken Kennedy Andre Luckow Brandon Posey Marcin Ziolkowski 《Software》2020,50(6):868-898
We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system. 相似文献
10.
《CIRP Annals》2019,68(1):145-148
Many condition monitoring systems use data analytics processes such as anomaly detection to understand machine conditions. Such data analytics processes have been designed by data scientists. However, domain knowledge is indispensable for designing the process, and data scientists have difficulty in acquiring such knowledge from domain engineers. This paper proposes a design method of data analytics processes and an engineering tool. In this method, data scientists propose hypotheses about each step of the process to domain engineers. Then, data scientists update the process on the basis of feedback from domain engineers. The engineering tool helps data scientists to interact with domain engineers. 相似文献