Proposing a hybrid technique of feature fusion and convolutional neural network for melanoma skin cancer detection.

Skin cancer is among the most common cancer types worldwide. Automatic identification of skin cancer is complicated because of the poor contrast and apparent resemblance between skin and lesions. The rate of human death can be significantly reduced if melanoma skin cancer could be detected quickly using dermoscopy images. This research uses an anisotropic diffusion filtering method on dermoscopy images to remove multiplicative speckle noise. To do this, the fast-bounding box (FBB) method is applied here to segment the skin cancer region. We also employ 2 feature extractors to represent images. The first one is the Hybrid Feature Extractor (HFE), and second one is the convolutional neural network VGG19-based CNN. The HFE combines 3 feature extraction approaches namely, Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF) into a single fused feature vector. The CNN method is also used to extract additional features from test and training datasets. This 2-feature vector is then fused to design the classification model. The proposed method is then employed on 2 datasets namely, ISIC 2017 and the academic torrents dataset. Our proposed method achieves 99.85%, 91.65%, and 95.70% in terms of accuracy, sensitivity, and specificity, respectively, making it more successful than previously proposed machine learning algorithms.

A fast parallelized DBSCAN algorithm based on OpenMp for detection of criminals on streaming services.

Introduction: Streaming services are highly popular today. Millions of people watch live streams or videos and listen to music. Methods: One of the most popular streaming platforms is Twitch, and data from this type of service can be a good example for applying the parallel DBSCAN algorithm proposed in this paper. Unlike the classical approach to neighbor search, the proposed one avoids redundancy, i.e., the repetition of the same calculations. At the same time, this algorithm is based on the classical DBSCAN method with a full search for all neighbors, parallelization by subtasks, and OpenMP parallel computing technology. Results: In this work, without reducing the accuracy, we managed to speed up the solution based on the DBSCAN algorithm when analyzing medium-sized data. As a result, the acceleration rate tends to the number of cores of a multicore computer system and the efficiency to one. Discussion: Before conducting numerical experiments, theoretical estimates of speed-up and efficiency were obtained, and they aligned with the results obtained, confirming their validity. The quality of the performed clustering was verified using the silhouette value. All experiments were conducted using different percentages of medium-sized datasets. The prospects of applying the proposed algorithm can be obtained in various fields such as advertising, marketing, cybersecurity, and sociology. It is worth mentioning that datasets of this kind are often used for detecting fraud on the Internet, making an algorithm capable of considering all neighbors a useful tool for such research.

Method Presentation of a New Integrated Orthodontic-Conservative Approach for Minimally Invasive Full Mouth Rehabilitation: Speed Up Therapy.

The materials available today allow for extensive oral rehabilitations in a non-invasive way, and often an orthodontic preparation is useful and, thanks to the use of clear aligners, is predictable and comfortable. A preliminary study of the wax-up, mock-up, and set-up allow the clinician to plan every aspect of the treatment in detail. Furthermore, the procedure offers the patient an intuitive and understandable view of the expected final result. The new proposed method, called "speed up therapy", allows for the integration of the orthodontic set-up with the mock-up technique, simulating the occlusal and aesthetic components of the planned restoration, in all details. The clinical case presented, demonstrates step by step the predictability and clinical reliability of the proposed procedure. The final clinical result coincides exactly with the initial mock-up and demonstrates that the proposed method is predictable and reliable. The correct execution of the technique is rigorously customized, and its success is operator dependent, both for the clinical aspects and for the dental laboratory. Thus, the visualization of the objectives of the treatment constitutes a decisive support for the clinician and provides the patient with the possibility of benefiting from an immediate improvement by making it easier for them to accept a treatment plan. The visualization also includes an orthodontic phase that potentially lengthens the treatment but makes the realization more conservative and predictable.

Long-Term Evolution Experiment with Genetic Programming.

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.

A novel ramp loss-based multi-task twin support vector machine with multi-parameter safe acceleration.

Direct multi-task twin support vector machine (DMTSVM) is an effective algorithm to deal with multi-task classification problems. However, the generated hyperplane may shift to outliers since the hinge loss is used in DMTSVM. Therefore, we propose an improved multi-task model RaMTTSVM based on ramp loss to handle noisy points more effectively. It could limit the maximal loss value distinctly and put definite restrictions on the influences of noises. But RaMTTSVM is non-convex which should be solved by CCCP, then a series of approximate convex problems need to be solved. So, it may be time-consuming. Motivated by the sparse solution of our RaMTTSVM, we further propose a safe acceleration rule MSA to accelerate the solving speed. Based on optimality conditions and convex optimization theory, MSA could delete a lot of inactive samples corresponding to 0 elements in dual solutions before solving the model. Then the computation speed can be accelerated by just solving reduced problems. The rule contains three different parts that correspond to different parameters and different iteration phases of CCCP. It can be used not only for the first approximate convex problem of CCCP but also for the successive problems during the iteration process. More importantly, our MSA is safe in the sense that the reduced problem can derive an identical optimal solution as the original problem, so the prediction accuracy will not be disturbed. Experimental results on one artificial dataset, ten Benchmark datasets, ten Image datasets and one real wine dataset confirm the generalization and acceleration ability of our proposed algorithm.

[Analysis of Acidic Tar Dyes in High-Protein Foods: Achievement of Improved Recoveries and Shortened Operation Time by Loading Sample Extract onto Polyamide Columns at pH 8.5].

The recoveries of xanthene dyes in the analysis of acidic tar-dyes in high-protein foods were improved by loading them onto polyamide columns at pH 8.5, instead of using the conventional pH 3-4 solution. The experimental scale was reduced to approximately half that of the conventional method. Furthermore, instead of eliminating the organic solvent in the extract by evaporation, the extract was diluted with water prior to PA column cleanup in order to reduce the ratio of organic solvent so that acidic tar-dyes would be better retained on the column. The above two procedures shortened the operation time and allowed for a simpler protocol. With this method, the recoveries of erythrosine, phloxine, and rose bengal from salted cod roe were 82, 88, and 74%, respectively. The recovery percentages were greatly improved compared to those achieved by conventional column loading at pH 3.5 (26, 44, and 18%, respectively). The recoveries of azo-dyes (Amaranth, New Coccine, Allura Red AC, Tartrazine, Sunset Yellow FCF) were also improved from 41-66 to 79-99%.

Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.

Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

Region of Interest-Based Tamper Detection and Lossless Recovery Watermarking Scheme (ROI-DR) on Ultrasound Medical Images.

Tampering on medical image will lead to wrong diagnosis and treatment, which is life-threatening; therefore, digital watermarking on medical image was introduced to protect medical image from tampering. Medical images are divided into region of interest (ROI) and region of non-interest (RONI). ROI is an area that has a significant impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper has proposed ROI-based tamper detection and recovery watermarking scheme (ROI-DR) that embeds ROI bit information into RONI least significant bits, which will be extracted later for authentication and recovery process. The experiment result has shown that the ROI-DR has achieved a good result in imperceptibility with peak signal-to-noise ratio (PSNR) values approximately 48 dB, it is robust against various kinds of tampering, and the tampered ROI was able to recover to its original form. Lastly, a comparative table with the previous research (TALLOR and TALLOR-RS watermarking schemes) has been derived, where these three watermarking schemes were tested under the same testing conditions and environment. The experiment result has shown that ROI-DR has achieved speed-up factors of 22.55 and 26.65 in relative to TALLOR and TALLOR-RS watermarking schemes, respectively.

Design and Analysis of Localized DNA Hybridization Chain Reactions.

Theoretical models of localized DNA hybridization reactions on nanoscale substrates indicate potential benefits over conventional DNA hybridization reactions. Recently, a few approaches have been proposed to speed-up DNA hybridization reactions; however, experimental confirmation and quantification of the acceleration factor have been lacking. Here, a system to investigate localized DNA hybridization reactions on a nanoscale substrate is presented. The system consists of six metastable DNA hairpins that are tethered to a long DNA track. The localized DNA hybridization reaction of the proposed system is triggered by a DNA strand which initiates the subsequent self-assembly. Fluorescence kinetics indicates that the half-time completion of a localized DNA hybridization chain reaction is six times faster than the same reaction in the absence of the substrate. The proposed system provides one of the first known quantification of the speed-up of DNA hybridization reactions due to the locality effect.

The Integration of the Image Sensor with a 3-DOF Pneumatic Parallel Manipulator.

The study aims to integrate the image sensor for a three-axial pneumatic parallel manipulator which can pick and place objects automatically by the feature information of the image processed through the SURF algorithm. The SURF algorithm is adopted for defining and matching the features of a target object and an object database. In order to accurately mark the center of target and strengthen the feature matching results, the random sample and consensus method (RANSAC) is utilized. The ASUS Xtion Pro Live depth camera which can directly estimate the 3-D location of the target point is used in this study. A set of coordinate estimation calibrations is developed for enhancing the accuracy of target location estimation. This study also presents hand gesture recognition exploiting skin detection and noise elimination to determine the active finger count for input signals of the parallel manipulator. The end-effector of the parallel manipulator can be manipulated to the desired poses according to the measured finger count. Finally, the proposed methods are successfully to achieve the feature recognition and pick and place of the target object.

Precocious flowering of juvenile citrus induced by a viral vector based on Citrus leaf blotch virus: a new tool for genetics and breeding.

The long juvenile period of citrus trees (often more than 6 years) has hindered genetic improvement by traditional breeding methods and genetic studies. In this work, we have developed a biotechnology tool to promote transition from the vegetative to the reproductive phase in juvenile citrus plants by expression of the Arabidopsis thaliana or citrus FLOWERING LOCUS T (FT) genes using a Citrus leaf blotch virus-based vector (clbvINpr-AtFT and clbvINpr-CiFT, respectively). Citrus plants of different genotypes graft inoculated with either of these vectors started flowering within 4-6 months, with no alteration of the plant architecture, leaf, flower or fruit morphology in comparison with noninoculated adult plants. The vector did not integrate in or recombine with the plant genome nor was it pollen or vector transmissible, albeit seed transmission at low rate was detected. The clbvINpr-AtFT is very stable, and flowering was observed over a period of at least 5 years. Precocious flowering of juvenile citrus plants after vector infection provides a helpful and safe tool to dramatically speed up genetic studies and breeding programmes.

QuBiLS-MIDAS: a parallel free-software for molecular descriptors computation based on multilinear algebraic maps.

The present report introduces the QuBiLS-MIDAS software belonging to the ToMoCoMD-CARDD suite for the calculation of three-dimensional molecular descriptors (MDs) based on the two-linear (bilinear), three-linear, and four-linear (multilinear or N-linear) algebraic forms. Thus, it is unique software that computes these tensor-based indices. These descriptors, establish relations for two, three, and four atoms by using several (dis-)similarity metrics or multimetrics, matrix transformations, cutoffs, local calculations and aggregation operators. The theoretical background of these N-linear indices is also presented. The QuBiLS-MIDAS software was developed in the Java programming language and employs the Chemical Development Kit library for the manipulation of the chemical structures and the calculation of the atomic properties. This software is composed by a desktop user-friendly interface and an Abstract Programming Interface library. The former was created to simplify the configuration of the different options of the MDs, whereas the library was designed to allow its easy integration to other software for chemoinformatics applications. This program provides functionalities for data cleaning tasks and for batch processing of the molecular indices. In addition, it offers parallel calculation of the MDs through the use of all available processors in current computers. The studies of complexity of the main algorithms demonstrate that these were efficiently implemented with respect to their trivial implementation. Lastly, the performance tests reveal that this software has a suitable behavior when the amount of processors is increased. Therefore, the QuBiLS-MIDAS software constitutes a useful application for the computation of the molecular indices based on N-linear algebraic maps and it can be used freely to perform chemoinformatics studies.

Graphics processing unit accelerated one-dimensional blood flow computation in the human arterial tree.

One-dimensional blood flow models have been used extensively for computing pressure and flow waveforms in the human arterial circulation. We propose an improved numerical implementation based on a graphics processing unit (GPU) for the acceleration of the execution time of one-dimensional model. A novel parallel hybrid CPU-GPU algorithm with compact copy operations (PHCGCC) and a parallel GPU only (PGO) algorithm are developed, which are compared against previously introduced PHCG versions, a single-threaded CPU only algorithm and a multi-threaded CPU only algorithm. Different second-order numerical schemes (Lax-Wendroff and Taylor series) are evaluated for the numerical solution of one-dimensional model, and the computational setups include physiologically motivated non-periodic (Windkessel) and periodic boundary conditions (BC) (structured tree) and elastic and viscoelastic wall laws. Both the PHCGCC and the PGO implementations improved the execution time significantly. The speed-up values over the single-threaded CPU only implementation range from 5.26 to 8.10 × , whereas the speed-up values over the multi-threaded CPU only implementation range from 1.84 to 4.02 × . The PHCGCC algorithm performs best for an elastic wall law with non-periodic BC and for viscoelastic wall laws, whereas the PGO algorithm performs best for an elastic wall law with periodic BC.