A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework

Students’ interaction and collaboration with the fellows and teachers using the Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of the educational assessment for students’ interaction. As new learning styles develop, new tools and assessment methods are also needed. The focus in this paper is to develop IoT based interaction framework and analysis of the student experience in electronic learning (eLearning) so that the students can take full advantage of the modern interaction technology and their learning can increase to a high level. This setup has a data collection module, which is implemented using Visual C# programming language and computer vision library. The number of faces, number of eyes, and status of eyes are extracted from the video stream, which is taken from a video camera. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to consider student-learning behaviors when assessing electronic learning strategies. The tools are also developed for the data collection on both student and teacher ends. Correlation of data and hidden meaning are extracted to make the learning experience and teaching performance better and adaptable. IoT based infrastructure provides the facilities to fellow students about location awareness, fellows’ accessibility, social behavior and helping hand.     

Abstractive Text Summarization based on Improved Semantic Graph Approach

The goal of abstractive summarization of multi-documents is to automatically produce a condensed version of the document text and maintain the significant information. Most of the graph-based extractive methods represent sentence as bag of words and utilize content similarity measure, which might fail to detect semantically equivalent redundant sentences. On other hand, graph based abstractive method depends on domain expert to build a semantic graph from manually created ontology, which requires time and effort. This work presents a semantic graph approach with improved ranking algorithm for abstractive summarization of multi-documents. The semantic graph is built from the source documents in a manner that the graph nodes denote the predicate argument structures (PASs)—the semantic structure of sentence, which is automatically identified by using semantic role labeling; while graph edges represent similarity weight, which is computed from PASs semantic similarity. In order to reflect the impact of both document and document set on PASs, the edge of semantic graph is further augmented with PAS-to-document and PAS-to-document set relationships. The important graph nodes (PASs) are ranked using the improved graph ranking algorithm. The redundant PASs are reduced by using maximal marginal relevance for re-ranking the PASs and finally summary sentences are generated from the top ranked PASs using language generation. Experiment of this research is accomplished using DUC-2002, a standard dataset for document summarization. Experimental findings signify that the proposed approach shows superior performance than other summarization approaches.     

Unveiling the dark side of social networking sites: Personal and work-related consequences of social networking site addiction

Although previous research has explored the effects of social networking site (SNS) use in organizations, researchers have focused little on its negative consequences. This article attempts to fill this void by examining, through the lens of social cognitive theory, the extent SNS addiction impacts personal and work environments. The results, based on 276 questionnaires completed by employees in a large information technology corporation, show that addiction to SNSs has negative consequences on the personal and work environments. SNS addiction reduces positive emotions that augment performance and enhance health. SNS addiction fosters task distraction, which inhibits performance. Theoretical and practical implications are discussed.     

Finite-Element Analysis of Inclined Piezocone Penetration Test in Clays

A three-dimensional finite-element analysis was performed to analyze the effect of soil anisotropy on the inclined piezocone penetration test in normally consolidated clay. The piezocone penetration was numerically simulated based on a large strain formulation using the commercial finite-element code ABAQUS, and the anisotropic modified cam clay model (AMCCM) was chosen and implemented into ABAQUS through the user subroutine UMAT. For verification purposes, numerical simulations were first performed on previously conducted calibration chamber tests, and the predicted results were compared with the measured values. For different initial stress conditions and different penetration angles, the cone tip resistance profile; excess pore pressure profile at the cone tip; typical stress, strain and excess pore pressure distributions around the cone; and excess pore pressure dissipation at the cone tip are provided. This study shows that when the initial stress state is anisotropic, the soil behavior is different under different angles of penetration.     

Numerical parametric study of geosynthetic reinforced soil integrated bridge system (GRS-IBS)

A 2-D finite flement model was developed in this study to conduct a FE parametric study on the effects of some variables in the performance of geosynthetic reinforced soil integrated bridge system (GRS-IBS). The variables investigated in this study include the effect of internal friction angle of backfill material, width of reinforced soil foundation (RSF), secondary reinforcement within bearing bed, setback distance, bearing width and length of reinforcement. Other important parameters such as reinforcement stiffness and spacing were previously investgated by the authors. The performance of GRS-IBS were investgated in terms of lateral facing displacement, strain distribution along reinforcement, and location of potential failure zone. The results showed that the internal friction angle of backfill material has a significant impact on the performance of GRS-IBS. The secondary reinforcement, setback distance, and bearing width have low impact on the performance of GRS-IBS. However, it was found that the width of RSF and length of reinforcement have negligible effect on the performance of GRS-IBS. Finally, the potential failure envelope of the GRS-IBS abutment was found to be a combination of punching shear failure envelope (top) that starts under the inner edge of strip footing and extends vertically downward to intersect with Rankine active failure envelope (bottom).     

Breast cancer detection by using associative classifier with rule refinement method based on relevance feedback

Neural Computing and Applications - Computer-aided diagnosis system that uses classification process for an automated detection of breast cancer could provide a second opinion that improves...     

Impact of the Sampling Rate on the Estimation of the Parameters of Fractional Brownian Motion

Abstract. Fractional Brownian motion is a mean‐zero self‐similar Gaussian process with stationary increments. Its covariance depends on two parameters, the self‐similar parameter H and the variance C. Suppose that one wants to estimate optimally these parameters by using n equally spaced observations. How should these observations be distributed? We show that the spacing of the observations does not affect the estimation of H (this is due to the self‐similarity of the process), but the spacing does affect the estimation of the variance C. For example, if the observations are equally spaced on [0, n] (unit‐spacing), the rate of convergence of the maximum likelihood estimator (MLE) of the variance C is . However, if the observations are equally spaced on [0, 1] (1/n‐spacing), or on [0, n²] (n‐spacing), the rate is slower, . We also determine the optimal choice of the spacing Δ when it is constant, independent of the sample size n. While the rate of convergence of the MLE of C is in this case, irrespective of the value of Δ, the value of the optimal spacing depends on H. It is 1 (unit‐spacing) if H = 1/2 but is very large if H is close to 1.     

Institutionalization and internationalization of research on the applications of the geographical information systems in health planning

Recently, geographical information systems have been very intensively applied in social life and in public health in particular. A retrospective problem-oriented search on their use in health planning was performed in Web of Science of Web of Knowledge, three versions of MEDLINE, Scopus, EMBASE, and ProQuest Medical in 1990–2010. The annual dynamics of a set of scientometric parameters characterizing several aspects of the abstracted publications, authors’ scientific institutions, journals, authors, citations, and languages was comparatively analyzed. It was established that world publication output on such a relatively narrow topic was reflected to a different extent in these data-bases. MEDLINE (PubMed) presented with 484 papers published in 243 journals followed by MEDLINE (WoK) with 360 papers in 215 journals. The abstracted publications were mainly in English, but 14 other languages were present in significant numbers. Publications by authors from 44 countries were abstracted in WoS but from 29 countries in MEDLINE (Ebsco). The most productive authors and institutions as well as the ‘core’ journals were identified. The International Journal of Health Geography occupied the leading position. The Center for Disease Control and Prevention (USA) was one of the most productive research institutions in WoS and in Scopus. Scientific institutions and journals belonged to problem-oriented and to mono-, two- and three-disciplinary thematic profiles as well. Some essential peculiarities of the dynamics of research institutionalization and internationalization in this interdisciplinary field were illustrated. The constellation of specific semantically-loaded indicators could be applied for the purposes of problem-oriented analyses as it could timely identify the essential patterns of scientific advances in rapidly expanding interdisciplinary topics.     

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred due to overwhelming image analysis. The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches. This research study proposed an automatic model for tumor segmentation in MRI images. The proposed model has a few significant steps, which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative (NIFTI) volumes into the 3D NumPy array. In the second step, the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters. In the third step, the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention (MICCAI) BRATS 2018 dataset with MRI modalities such as T1, T1Gd, T2, and Fluid-attenuated inversion recovery (FLAIR). Tumour types in MRI images are classified according to the tumour masses. Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour (label 4), edema (label 2), necrotic and non-enhancing tumour core (label 1), and the remaining region is label 0 such that edema (whole tumour), necrosis and active. The proposed model is evaluated and gets the Dice Coefficient (DSC) value for High-grade glioma (HGG) volumes for their test set-a, test set-b, and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-grade glioma (LGG) volumes for the test set is 0.9950, which shows the proposed model has achieved significant results in segmenting the tumour in MRI using deep learning approaches. The proposed model is fully automatic that can implement in clinics where human experts consume maximum time to identify the tumorous region of the brain MRI. The proposed model can help in a way it can proceed rapidly by treating the tumor segmentation in MRI.