Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited ...Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited by the high computing requirements.The spatial resolution of HSI can be enhanced by utilizing Deep Learning(DL)based Super-resolution(SR).A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI,without losing the spectral content.The 3DCNNHSR model was tested for the Hyperion HSI.The pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the errors.The key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral features.By clustering contiguous spectral content together,a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is realized.The 3D-CNNHSR model was compared with a 2D-CNN model,additionally,the assessment was based on higherresolution data from the Sentinel-2 satellite.Based on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed,which is significantly less than previous studies.展开更多
An unsteady viscous fluid flow with Dufour and Soret effect,which results in heat and mass transfer due to upward and downward motion of flexible rotating disk,has been studied.The upward or downward motion of non rot...An unsteady viscous fluid flow with Dufour and Soret effect,which results in heat and mass transfer due to upward and downward motion of flexible rotating disk,has been studied.The upward or downward motion of non rotating disk results in two dimensional flow,while the vertical action and rotation of the disk results in three dimensional flow.By using an appropriate transformation the governing equations are transformed into the system of ordinary differential equations.The system of ordinary differential equations is further converted into first order differential equation by selecting suitable variables.Then,we generalize the model by using the Caputo derivative.The numerical result for the fractional model is presented and validated with Runge Kutta order 4 method for classical case.The compared results are presented in Table and Figures.It is concluded that the fractional model is more realistic than that of classical one,because it simulates the fluid behavior at each fractional value rather than the integral values.展开更多
With the advancement in cellular biology,the use of antimicrobial peptides(AMPs)against many drug-resistant pathogens has increased.AMPs have a broad range of activity and can work as antibacterial,antifungal,antivira...With the advancement in cellular biology,the use of antimicrobial peptides(AMPs)against many drug-resistant pathogens has increased.AMPs have a broad range of activity and can work as antibacterial,antifungal,antiviral,and sometimes even as anticancer peptides.The traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab experiments.Such experiments are both time-consuming and expensive.With the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such problems.This study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and non-AMPs.The proposed methodology uses machine learning algorithms to predict such sequences.A dataset was formulated based on 1902 samples of AMPs and 3997 samples of non-AMPs.Machine learning algorithms are trained on a xed number of succinct coefcients retaining sequence and composition information of primary structures.The features are extracted using position relative incidence and statistical moments.System performance is validated via various validation tests including a 10-fold cross-validation approach.An overall accuracy of 95.43%was achieved.A comparison of results with existing methodologies shows that the proposed methodology outperformed existing methodologies in terms of prediction accuracy.展开更多
Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatin...Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.展开更多
基金Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under Grant Number RGP2/80/44.
文摘Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited by the high computing requirements.The spatial resolution of HSI can be enhanced by utilizing Deep Learning(DL)based Super-resolution(SR).A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI,without losing the spectral content.The 3DCNNHSR model was tested for the Hyperion HSI.The pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the errors.The key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral features.By clustering contiguous spectral content together,a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is realized.The 3D-CNNHSR model was compared with a 2D-CNN model,additionally,the assessment was based on higherresolution data from the Sentinel-2 satellite.Based on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed,which is significantly less than previous studies.
文摘An unsteady viscous fluid flow with Dufour and Soret effect,which results in heat and mass transfer due to upward and downward motion of flexible rotating disk,has been studied.The upward or downward motion of non rotating disk results in two dimensional flow,while the vertical action and rotation of the disk results in three dimensional flow.By using an appropriate transformation the governing equations are transformed into the system of ordinary differential equations.The system of ordinary differential equations is further converted into first order differential equation by selecting suitable variables.Then,we generalize the model by using the Caputo derivative.The numerical result for the fractional model is presented and validated with Runge Kutta order 4 method for classical case.The compared results are presented in Table and Figures.It is concluded that the fractional model is more realistic than that of classical one,because it simulates the fluid behavior at each fractional value rather than the integral values.
基金funded by the Deanship of Scientic Research(DSR),King Abdulaziz University(https://www.kau.edu.sa/),Jeddah,under Grant No.(DF-794-611-1441).The authors,therefore,gratefully acknowledge DSR technical and nancial support.
文摘With the advancement in cellular biology,the use of antimicrobial peptides(AMPs)against many drug-resistant pathogens has increased.AMPs have a broad range of activity and can work as antibacterial,antifungal,antiviral,and sometimes even as anticancer peptides.The traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab experiments.Such experiments are both time-consuming and expensive.With the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such problems.This study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and non-AMPs.The proposed methodology uses machine learning algorithms to predict such sequences.A dataset was formulated based on 1902 samples of AMPs and 3997 samples of non-AMPs.Machine learning algorithms are trained on a xed number of succinct coefcients retaining sequence and composition information of primary structures.The features are extracted using position relative incidence and statistical moments.System performance is validated via various validation tests including a 10-fold cross-validation approach.An overall accuracy of 95.43%was achieved.A comparison of results with existing methodologies shows that the proposed methodology outperformed existing methodologies in terms of prediction accuracy.
基金Institutional Fund Projects under Grant No.(IFPIP:801-830-1443)The author gratefully acknowledges technical and financial support provided by the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.
