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
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
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
The efficiency of training visual attention in the central and peripheral visual field was investigated by means of a visual detection task that was performed in a naturalistic visual environment including numerous, time-varying visual distractors. We investigated the minimum number of repetitions of the training required to obtain the top performance and whether intra-day training improved performance as efficiently as inter-day training. Additionally, our research aimed to find out whether exposure to a demanding task such as a microsurgical intervention may cancel out the effects of training.
Results showed that performance in visual attention peaked within three (for tasks in the central visual field) to seven (for tasks in the periphery) days subsequent to training. Intra-day training had no significant effect on performance. When attention training was administered after exposure to stress, improvement of attentional performance was more pronounced than when training was completed before the exposure. Our findings support the implementation of training in situ at work for more efficient results.
Practitioner Summary: Visual attention is important in an increasing number of workplaces, such as with surveillance, inspection, or driving. This study shows that it is possible to train visual attention efficiently within three to seven days. Because our study was executed in a naturalistic environment, training results are more likely to reflect the effects in the real workplace. 相似文献
The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process. 相似文献
To survive in a dynamic and hyper-competitive business environment, firms are compelled to simultaneously introduce incremental and radical innovations. While it is recognised that business intelligence and analytics (BI&A) can support innovation and provide organisational value, the literature provides a limited understanding of its impact on balancing different innovation activities and ensuring performance gains. In this study, we examine the relationship between BI&A use, innovation ambidexterity, and firm performance by relying on the process theory of IS value creation as well as the dynamic capabilities perspective. We test our model using data collected from medium- and large-sized firms in Slovenia, applying partial least squares modelling. The results support the notion that BI&A use is positively associated with successful balancing between explorative and exploitative innovation activities, which in turn enhances firm performance. Our results also indicate that innovation ambidexterity is enhanced in two ways: indirectly through interaction with the firm’s absorptive capacity, and directly by increasing the possibilities of faster experimentation with offerings of products or services and improved predictability of the value of new products or services. 相似文献
The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset. 相似文献