Internet cross-media retrieval based on deep learning

With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources , has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval

Knowledge Discovery and Machine Learning: Research in Gingivitis Detection

Gingivitis is a high-risk condition that causes dietary issues in older people. The study of gingivitis is more difficult in the realm of medical image analysis due to the absence and complexity of dental image analysis. As traditional clinical diagnosis takes time and money and necessitates a lot of physical effort on the part of competent clinicians. In contrast, deep learning allows for automated medicine via picture analysis. However, several obstacles remain in medicine, such as poor machine model performance, inadequate training data, and expensive labeling costs, to name a few. These difficulties encourage the development of data- and knowledge-aware deep learning approaches that can be used for a variety of medical activities without requiring considerable human labeling and that incorporate domain-specific information throughout the learning process. This paper reviews and analyses research in computer-aided diagnosis and medical image deep learning, with a focus on the challenges in the field of gingivitis image detection, and proposes model performance achieved by combining different image extraction methods and different classification methods. At the same time, some traditional feature extraction methods and standard computer-aided diagnosis methods are introduced. In this paper, a feature extraction model based on fractional Fourier entropy and wavelet energy entropy is proposed for gingival image segmentation, and various classification and optimization techniques are combined. By evaluating the reintegrated medical images, the experimental results of the gingivitis detection model based on fractional Fourier entropy feature extraction combined with particle swarm optimization neural network show that the detection method significantly reduces the detection space and the complexity of image information. The improved algorithm can cluster the sample data efficiently and accurately, and the accuracy is higher than that of advanced gingival image diagnosis technology.</p

Silicon-Based Tunnel Diode Technology

Tunnel diodes have received interest because of their remarkable multivalued I-V characteristic and inherent high switching speeds. The exploration of tunnel diode applications was impeded by the incompatibility of tunnel diode fabrication technology with integrated-circuit processing. Rapid thermal diffusion from spin-on diffusants is the particular focus of this work as a basis for establishing a rapid thermal processing method compatible with commercial foundry processes. Vertical tunnel diodes formed on high resistivity substrates have been demonstrated to allow S-parameter measurements and extraction of a full ac device model. A current density of 2.7 Ì_å_A/Ì_å_m2 and peak-to-valley current ratio (PVR) of 2.25 has been achieved, which is the best result ever achieved using spin-on diffusants. Several other approaches were also explored such as flash-lamp annealing and rapid thermal annealing from doped metal sources. Phosphorus activation was improved using flash-lamp annealing. Tunnel diodes were formed by rapid thermal annealing from an Al:B:Si source on Si with a peak current density of 2.7 Ì_å_A/Ì_å_m2. Backward diodes were formed by evaporating 50 and 100 nm undoped Ge layer as well as 100 nm Al on an n+ Ge. This indicates that the undoped amorphous Ge was successfully transformed into heavily doped crystalline Ge. Transmission Electron Microscopy (TEM) was taken which allow characterization of the regrown layer thickness. TEM also showed that the regrown layer is clearly epitaxial and free of defects. The potential and limitations of each approach is discussed in this work

Flowchart of the research process.

Flowchart of the research process.</p

Research case.

Research case.</p

Coordination assessment system for science popularization.

Coordination assessment system for science popularization.</p

Overall performance of the higher education system.

(DOCX)</p

DataSheet1_Association of all Cause and Cause-Specific Mortality With Hearing Loss Among US Adults: A Secondary Analysis Study.docx

Objectives: Previous research revealed the relationship between hearing loss (HL) and all cause mortality. The aim of this study was to determine the association between HL and all causes and cause-specific mortality based on US adults.Methods: Data were obtained by linking National Health Interview Survey (NHIS) (2004–2013) with linkage to a mortality database to 31 December 2015. HL were categorized into four groups: good hearing, a little hearing difficulty, a lot of hearing difficulty, profoundly deaf. The relationship between HL and mortality risk was analyzed using Cox proportional hazards regression model.Results: Compared with the reference group (Good), those who had light or moderate hearing problems were at an increased risk of mortality for all causes (A little trouble—HR: 1.17; 95% confidence interval [CI]: 1.13 to 1.20; A lot of trouble—HR: 1.45; 95% CI: 1.40–1.51); deaf—HR: 1.54; 95% CI: 1.38–1.73) respectively.Conclusion: In addition, those in the deaf category have the highest risk of death from all causes and cause-specific cancer. More older adults are associated with an increased risk of all-cause mortality in American adults.</p

Flowchart of the analysis processes.

Flowchart of the analysis processes.</p

Overall performance of the science popularization system.

(DOCX)</p