Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

A novel epileptic seizure prediction method based on synchroextracting transform and 1-dimensional convolutional neural network

BACKGROUND AND OBJECTIVE

Epilepsy is a serious brain disorder affecting more than 50 million people worldwide. If epileptic seizures can be predicted in advance, patients can take measures to avoid unfortunate consequences. Important approaches for epileptic seizure predictions are often signal transformation and classification using electroencephalography (EEG) signals. A time-frequency (TF) transformation, such as the short-term Fourier transform (STFT), has been widely used over many years but curtailed by the Heisenberg uncertainty principle. This research focuses on decomposing epileptic EEG signals with a higher resolution so that an epileptic seizure can be predicted accurately before its episodes.

METHODS

This study applies a synchroextracting transformation (SET) and singular value decomposition (SET-SVD) to improve the time-frequency resolution. The SET is a more energy-concentrated TF representation than classical TF analysis methods.

RESULTS

The pre-seizure classification method employing a 1-dimensional convolutional neural network (1D-CNN) reached an accuracy of 99.71% (the CHB-MIT database) and 100% (the Bonn University database). The experiments on the CHB-MIT show that the accuracy, sensitivity and specificity from the SET-SVD method, compared with the results of the STFT, are increased by 8.12%, 6.24% and 13.91%, respectively. In addition, a multi-layer perceptron (MLP) was also used as a classifier. Its experimental results also show that the SET-SVD generates a higher accuracy, sensitivity and specificity by 5.0%, 2.41% and 11.42% than the STFT, respectively.

CONCLUSIONS

The results of two classification methods (the MLP and 1D-CNN) show that the SET-SVD has the capacity to extract more accurate information than the STFT. The 1D-CNN model is suitable for a fast and accurate patient-specific EEG classification.

Robust, imperceptible and optimized watermarking of DICOM image using Schur decomposition, LWT-DCT-SVD and its authentication using SURF

In this proposed work, a dual image watermarking algorithm is used to protect the data against copyright violations. In this work, the DICOM image is used as a host image. Two watermark images used are the MNNIT logo and the personal data of the patient. This method utilizes the advantages of Schur decomposition, lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scaling factor is a vital parameter of watermarking technique. The firefly optimization technique is used to get the optimized scaling factor. The Speeded-up robust features (SURF) are used for watermarking authentication. To evaluate the performance of the proposed algorithm, peak signal-to-noise ratio (PSNR), normalized correlation coefficient (NCC), and structural similarity index measurement (SSIM) are used. The proposed method is tested against various attacks such as Salt and Pepper noise, Gaussian noise, Gaussian low pass filter, Average filter, Median filter, Histogram equalization, Sharpening, Rotation and Region of interest filtering. The proposed algorithm shows a high level of robustness and imperceptibility. It is found that the features of the input host image and the watermarked image are matching correctly on applying the SURF technique.

A sophisticated and provably grayscale image watermarking system using DWT-SVD domain

Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today's digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high-high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.

An efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic system and Otsu threshold segmentation

This paper proposes an efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic systems and Otsu threshold segmentation. In our method, first, the stereoscopic medical image is divided into the image top, middle, and lower parts. Moreover, each part is divided into background areas and regions of interest utilizing Otsu threshold segmentation, increasing about 40% the encryption efficiency when the background area is discarded. Second, compared with existing chaotic systems, the proposed coupled chaotic system has better ergodicity and randomness, with all NIST SP800-22 test data exceeding 0.01. Third, we develop a robust watermarking algorithm based on forwarding Meyer wavelet transform and singular value decomposition. Furthermore, the watermark algorithm embedded the two-dimensional code doctor-patient information in the region of interest. Finally, the experimental results demonstrate that the proposed algorithm has appealing encryption and watermark performance, the histogram and scatter graphs are governed by approximately uniform distribution, the NPCR and UACI of plaintext sensitivity and the key sensitivity are close to 99.6094% and 33.4635%, affording robustness to noise and clipping attacks.

An intelligent railway surveillance framework based on recognition of object and railway track using deep learning

In high speed railways, the intelligent railway safety system is necessary to avoid the accidents due to collision between trains and obstacles on the railway track. The unceasing research work is being performed to reinforce the railway safety and to diminish the accident rates. The rapid development in the field of deep learning has prompted new research opportunities in this area. In this paper, a novel and efficient approach is proposed to recognize the objects (obstacles) on the railway track ahead the train using deep classifier network. The 2-D Singular Spectrum Analysis (SSA) is utilized as decomposition tool that decomposes the image in useful components. That component is further applied to the deep classifier network. The obstacle recognition performance is enhanced by the combination of 2D-SSA and deep network. This method also presents a novel measure to identify the railway tracks. In addition, the performance of this approach is analyzed under different illumination conditions using OSU thermal pedestrian benchmark database. This system can be a tremendous support to curtail rail accidental rate and monetary loads. The results of proposed approach present good accuracy as well as can effectively recognize the objects (obstacles) on the railway track which helps to the railway safety. It also achieves a better performance with 85.2% accuracy, 84.5% precision and 88.6% recall.

Optimized Attribute Selection Using Artificial Plant (AP) Algorithm with ESVM Classifier (AP-ESVM) and Improved Singular Value Decomposition (ISVD)-Based Dimensionality Reduction for Large Micro-array Biological Data

In the tremendous field of the bioinformatics look into, enormous volume of genetic information has been produced. Higher throughput gadgets are made accessible at lower cost made the age of Big data. In a time of developing information multifaceted nature and volume and the approach of huge information, feature selection has a key task to carry out in decreasing high dimensionality in AI issues. Dealing with such huge data has turned out to be incredibly testing strategy for choosing the exact features in enormous medical databases. Large clinical data frequently comprise of an enormous number of identifiers of the disease. Data mining when applied to clinical data for identification of diseases, a few identifiers are will not be much useful and sometimes may even have negative impacts. Consequently, when the FS is applied, it is vital as it can expel those insignificant disease identifiers. It likewise builds the adequacy of decision by a physician emotionally supportive network by viably diminishing the time of learning of the framework. In this paper, a unique approach is presented for the feature selection utilizing the Artificial Plant algorithm which uses the Enhanced Support Vector Machine classifier. The features got are additionally dimensionally decreased by presenting the Improved Singular Value Decomposition strategy; finally, enhancement is done by the outstanding BAT streamlining method. The examinations are completed with real-time large cervical cancer data and it demonstrated to be more effective than the current methods.

A hybrid approach based on multiple Eigenvalues selection (MES) for the automated grading of a brain tumor using MRI

BACKGROUND AND OBJECTIVE

The manual segmentation, identification, and classification of brain tumor using magnetic resonance (MR) images are essential for making a correct diagnosis. It is, however, an exhausting and time consuming task performed by clinical experts and the accuracy of the results is subject to their point of view. Computer aided technology has therefore been developed to computerize these procedures.

METHODS

In order to improve the outcomes and decrease the complications involved in the process of analysing medical images, this study has investigated several methods. These include: a Local Difference in Intensity - Means (LDI-Means) based brain tumor segmentation, Mutual Information (MI) based feature selection, Singular Value Decomposition (SVD) based dimensionality reduction, and both Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) based brain tumor classification. Also, this study has presented a new method named Multiple Eigenvalues Selection (MES) to choose the most meaningful features as inputs to the classifiers. This combination between unsupervised and supervised techniques formed an effective system for the grading of brain glioma.

RESULTS

The experimental results of the proposed method showed an excellent performance in terms of accuracy, recall, specificity, precision, and error rate. They are 91.02%,86.52%, 94.26%, 87.07%, and 0.0897 respectively.

CONCLUSION

The obtained results prove the significance and effectiveness of the proposed method in comparison to other state-of-the-art techniques and it can have in the contribution to an early diagnosis of brain glioma.

An Efficient Watermarking Approach Based on LL and HH Edges of DWT-SVD

Digital watermarking is playing a vital role in the improvement of authentication, security, and copyright protection in today’s digital transformation. The performance of this technique is shown to be impressive around the globe. Text, audio, video, and image data are acted as watermarks in the digital platform. In this article, a hybrid watermarking scheme is proposed to furnish the robustness and protection of digital data. This hybrid scheme is a form of discrete wavelet transform (DWT) and singular value decomposition (SVD). The embedding and extracting features are carried out through multi-level operations of DWT and SVD. Various attacks are added to the proposed method to justify the robustness of the watermark. In the end, the suggested approach is contrasted with existing methods to confirm the supremacy.

Identification of correlated inter-residue interactions in protein complex based on the fragment molecular orbital method

A theoretical scheme to systematically describe correlated (network-like) interactions between molecular fragments is proposed within the framework of the fragment molecular orbital (FMO) method. The method is mathematically based on the singular value decomposition (SVD) of the inter-fragment interaction energy (IFIE) matrix obtained by the FMO calculation, and can be applied to a comprehensive description of protein-protein interactions in the context of molecular recognition. In the present study we apply the proposed method to a complex of measles virus hemagglutinin and human SLAM receptor, thus finding a usefulness for efficiently eliciting the correlated interactions among the amino-acid residues involved in the two proteins. Additionally, collective interaction networks by amino-acid residues important for mutation experiments can be clearly visualized.

The spatially heterogeneous response of aerosol properties to anthropogenic activities and meteorology changes in China during 1980-2018 based on the singular value decomposition method

The unsustainable and rapid economy development brings air pollution prominently in China. In the last decade, the haze weather and its influencing mechanism across China have received increasingly attention. Although previous research has extensively focused on the characteristics of aerosols, better understanding of long-term variation in aerosols and their determinants since the Reform and Opening-up still lack in China. Furthermore, the previous studies exploring the influencing mechanism behind haze episodes by using statistical method only reflect correlation between pollutant concentration and indicators at single station, which cannot consider the remote influences resulting from atmosphere transport. In this research, we investigated the spatiotemporal pattern of aerosol optical depth (AOD) and aerosol species in China during 1980-2018 and explored the spatially heterogeneous response of AOD and aerosol component to meteorological conditions and urbanization based on singular value decomposition (SVD) method. The results indicated that AOD exhibited an upward trend in nearly 40 years, especially in eastern China with the fastest growth of sulfate aerosol. The heterogeneity of determinants revealed a great gap in anthropogenic activities and meteorological influences on aerosol varing regions. In eastern China, anthropogenic activities should be closely monitored. Besides, scientific desert governance and urban construction exert positive impact on air pollution in Xinjiang province.

Characterization of nanofibers for tissue engineering: Chemical mapping by Confocal Raman microscopy

Nanofiber scaffolds are used in bioengineering for functional support of growing tissues. To fine tune nanofiber properties for specific applications, it is often necessary to characterize the spatial distribution of their chemical content. Raman spectroscopy is a common tool used to characterize chemical composition of various materials, including nanofibers. In combination with a confocal microscope, it allows simultaneous mapping of both spectral and spatial features of inhomogeneous structures, also known as hyperspectral imaging. However, such mapping is usually performed on microscopic scale, due to the resolution of the scanning system being diffraction limited (about 0.2-0.5 micron, depending on the excitation wavelength). We present an application of confocal Raman microscopy to hyperspectral mapping of nanofibers, where nanoscale features are resolved by means of oversampling and extensive data processing, including Singular Value Decomposition and Classical Least Squares decomposition techniques. Oversampling and data processing facilitated evaluation of the spatial distribution of different chemical components within multi-component nanofibers.

On a scale as a sum of manifest variables

The most common approach for a scale construction is to create a scale as a sum of manifest variables (a "sum scale"). When we use the sum scale for analysis, we implicitly assume that there is a one-dimensional latent structure representing the manifest data on a multidimensional space. In this commentary, we review basics of identifying a latent structure using measured variables with a minimum linear algebra. We demonstrate the technique using Fisher's iris data as an illustration. We examine the relationships between resulting latent variables and the sum scale to evaluate goodness of the sum scale. As a practical solution, in general, we could create a sum scale using a set of positively and highly correlated measured variables. More care is needed when the data are not unidimensional.

Quantitative Determination of Interacting Protein Surfaces in Prokaryotes and Eukaryotes by Using In-Cell NMR Spectroscopy

This paper describes three protocols for identifying interacting surfaces on ¹⁵N-labeled target proteins of known structure by using in-cell NMR spectroscopy. The first protocol describes how to identify protein quinary structure interaction surfaces in prokaryotes by using cross-relaxation-induced polarization transfer, CRIPT, based in-cell NMR. The second protocol describes how to introduce labeled protein into eukaryotic (HeLa) cells via electroporation for CRIPT-based in-cell studies. The third protocol describes how to quantitatively map protein interacting surfaces by utilizing singular value decomposition, SVD, analysis of STructural INTeractions by in-cell NMR, STINT-NMR, data.

Unsupervised analysis reveals two molecular subgroups of serous ovarian cancer with distinct gene expression profiles and survival

Purpose

Ovarian cancer is typically diagnosed at late stages, and thus, patients’ prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets.

Methods

An unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR.

Results

We found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them—DSPG3 and LOX—were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival.

Conclusions

We distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.

Electronic supplementary material

The online version of this article (doi:10.1007/s00432-016-2147-y) contains supplementary material, which is available to authorized users.

Seizure detection approach using S-transform and singular value decomposition

Automatic seizure detection plays a significant role in the diagnosis of epilepsy. This paper presents a novel method based on S-transform and singular value decomposition (SVD) for seizure detection. Primarily, S-transform is performed on EEG signals, and the obtained time-frequency matrix is divided into submatrices. Then, the singular values of each submatrix are extracted using singular value decomposition (SVD). Effective features are constructed by adding the largest singular values in the same frequency band together and fed into Bayesian linear discriminant analysis (BLDA) classifier for decision. Finally, postprocessing is applied to obtain higher sensitivity and lower false detection rate. A total of 183.07 hours of intracranial EEG recordings containing 82 seizure events from 20 patients were used to evaluate the system. The proposed method had a sensitivity of 96.40% and a specificity of 99.01%, with a false detection rate of 0.16/h.

Dynamic PET and Optical Imaging and Compartment Modeling using a Dual-labeled Cyclic RGD Peptide Probe

Purpose: The aim of this study is to determine if dynamic optical imaging could provide comparable kinetic parameters to that of dynamic PET imaging by a near-infrared dye/⁶⁴Cu dual-labeled cyclic RGD peptide.

Methods: The integrin α_vβ₃ binding RGD peptide was conjugated with a macrocyclic chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) for copper labeling and PET imaging and a near-infrared dye ZW-1 for optical imaging. The in vitro biological activity of RGD-C(DOTA)-ZW-1 was characterized by cell staining and receptor binding assay. Sixty-min dynamic PET and optical imaging were acquired on a MDA-MB-435 tumor model. Singular value decomposition (SVD) method was applied to compute the dynamic optical signal from the two-dimensional optical projection images. Compartment models were used to quantitatively analyze and compare the dynamic optical and PET data.

Results: The dual-labeled probe ⁶⁴Cu-RGD-C(DOTA)-ZW-1 showed integrin specific binding in vitro and in vivo. The binding potential (Bp) derived from dynamic optical imaging (1.762 ± 0.020) is comparable to that from dynamic PET (1.752 ± 0.026).

Conclusion: The signal un-mixing process using SVD improved the accuracy of kinetic modeling of 2D dynamic optical data. Our results demonstrate that 2D dynamic optical imaging with SVD analysis could achieve comparable quantitative results as dynamic PET imaging in preclinical xenograft models.

Pathway modeling of microarray data: a case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.