Smart and Future Applications of Internet of Multimedia Things (IoMT) Using Big Data Analytics.

This Special Issue is focused on breakthrough developments in the field of Internet of Multimedia Things (IoMT), particularly on smart and future applications of IoMT using big data analytics [...].

Adaptive Aggregate Stereo Matching Network with Depth Map Super-Resolution.

In order to avoid the direct depth reconstruction of the original image pair and improve the accuracy of the results, we proposed a coarse-to-fine stereo matching network combining multi-level residual optimization and depth map super-resolution (ASR-Net). First, we used the u-net feature extractor to obtain the multi-scale feature pair. Second, we reconstructed global disparity in the lowest resolution. Then, we regressed the residual disparity using the higher-resolution feature pair. Finally, the lowest-resolution depth map was refined by using the disparity residual. In addition, we introduced deformable convolution and group-wise cost volume into the network to achieve adaptive cost aggregation. Further, the network uses ABPN instead of the traditional interpolation method. The network was evaluated on three datasets: scene flow, kitti2015, and kitti2012 and the experimental results showed that the speed and accuracy of our method were excellent. On the kitti2015 dataset, the three-pixel error converged to 2.86%, and the speed was about six times and two times that of GC-net and GWC-net.

Adjusting protein graphs based on graph entropy.

Measuring protein structural similarity attempts to establish a relationship of equivalence between polymer structures based on their conformations. In several recent studies, researchers have explored protein-graph remodeling, instead of looking a minimum superimposition for pairwise proteins. When graphs are used to represent structured objects, the problem of measuring object similarity become one of computing the similarity between graphs. Graph theory provides an alternative perspective as well as efficiency. Once a protein graph has been created, its structural stability must be verified. Therefore, a criterion is needed to determine if a protein graph can be used for structural comparison. In this paper, we propose a measurement for protein graph remodeling based on graph entropy. We extend the concept of graph entropy to determine whether a graph is suitable for representing a protein. The experimental results suggest that when applied, graph entropy helps a conformational on protein graph modeling. Furthermore, it indirectly contributes to protein structural comparison if a protein graph is solid.

A diagnosis model of dementia via machine learning.

As the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aims at the diagnosis method of questionnaire survey, hoping to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating (CDR) is used as the data source, and various methods are used for modeling and feature selection, so as to combine similar attributes in the data set, reduce the categories, and finally use the confusion matrix to judge the effect. The experimental results show that the model established by the bagging method has the best effect, and the accuracy rate can reach 80% of the true diagnosis rate; in terms of feature selection, the principal component analysis (PCA) has the best effect compared with other methods.

Proportional fault-tolerant data mining with applications to bioinformatics.

The mining of frequent patterns in databases has been studied for several years, but few reports have discussed for fault-tolerant (FT) pattern mining. FT data mining is more suitable for extracting interesting information from real-world data that may be polluted by noise. In particular, the increasing amount of today's biological databases requires such a data mining technique to mine important data, e.g., motifs. In this paper, we propose the concept of proportional FT mining of frequent patterns. The number of tolerable faults in a proportional FT pattern is proportional to the length of the pattern. Two algorithms are designed for solving this problem. The first algorithm, named FT-BottomUp, applies an FT-Apriori heuristic and finds all FT patterns with any number of faults. The second algorithm, FT-LevelWise, divides all FT patterns into several groups according to the number of tolerable faults, and mines the content patterns of each group in turn. By applying our algorithm on real data, two reported epitopes of spike proteins of SARS-CoV can be found in our resulting itemset and the proportional FT data mining is better than the fixed FT data mining for this application.

Optical detection of human papillomavirus type 16 and type 18 by sequence sandwich hybridization with oligonucleotide-functionalized Au nanoparticles.

The importance of detecting and subtyping human papillomaviruses (HPVs) in clinical and epidemiological studies has been well addressed. In detecting the most common types of HPV, type 16 (HPV-16) and type 18 (HPV-18), in the cervical mucous of patients in a simple and rapid manner, the assay of a label-free colorimetric DNA sensing method based on sequence sandwich hybridization with oligonucleotide-functionalized Au nanoparticles (AuNPs) was fabricated in this study. Specific oligonucleotide probes were designed for the sequence detection within the L1 gene of HPV-16 and HPV-18, and the probes were capped onto AuNPs, as AuNP probes. The target HPV sequences in clinical specimens were obtained by an asymmetric polymerase chain reaction (PCR) with universal primers, which can amplify the target sequences from several HPV serotypes, including HPV-16 and HPV-18. The DNA sandwich hybridization between the target sequences and the specific AuNP probes was performed at a temperature closer to the theoretical melting temperature of the DNA hybridization. Next, the procedure of increasing salt concentration and cooling the hybridizing solution was immediately utilized to discriminate the target sequences of HPV-16 or HPV-18. If the target sequences were not complementary to sequences of AuNP probes, the AuNPs would aggregate because no duplex DNA formation occurred such that the color of the reaction solution changed from red to purple. If the AuNP probes were a perfect match to the target sequences and a full DNA sandwich hybridization occurred, the reaction solution maintained its red color. A total of 70 mucous specimens from patients with cervical intraepithelial neoplasia were tested by the AuNP probes sandwich hybridization. The results show that there were 33, 16, 5, and 16 cases detected with HPV-16, HPV-18, both HPV-16 and HPV-18 (HPV-16/HPV-18), and neither HPV-16 nor HPV-18, respectively.