Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenoty...
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With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Mac...
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With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human ***,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].
Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for i...
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Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overl...
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Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text cont...
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Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu
Recently, deep learning neural networks have been widely used in object classification. The process of object classification typically involves extracting features from the point cloud using neural networks and integr...
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Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizabilit...
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Alzheimer's disease(AD)is the most frequent cause of dementia,however,and it is caused by a number of different *** regard to the elderly population all over the world,Alzheimer's disease is the seventh larges...
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Alzheimer's disease(AD)is the most frequent cause of dementia,however,and it is caused by a number of different *** regard to the elderly population all over the world,Alzheimer's disease is the seventh largest cause of mortality,disability,and ***,social isolation,inactivity,alcohol,smoking,obesity,diabetes,high blood pressure,and age are all variables that can increase the likelihood of getting *** risk factors include social isolation,depression,and smoking.A diagnosis of Alzheimer's disease at an earlier stage may improve the odds of receiving care and *** professionals often diagnose AD based on a limited number of *** the other hand,it is now possible to identify and categorize Alzheimer's disease(AD)because of technological advancements such as artificial intelligence(AI).However,to identify the current AI-enabled approaches,we must conduct an investigation into the state of the *** breakthrough in diagnosis methodologies will enable the development of the Clinical Decision Support System(CDSS),capable of automatically diagnosing Alzheimer's disease(AD)without human *** this publication,we conduct a systematic review of sixty research articles previously reviewed by other *** systematic review sheds light on the synthesis of new knowledge and *** study discusses the current approaches for machine learning,deep learning methods,ensemble models,transfer learning,and methods used for early Alzheimer's disease *** paper provides answers to a large number of research issues and synthesizes fresh information that is helpful to the reader on many elements of AI-enabled approaches for Alzheimer's disease *** addition,it has the potential to stimulate additional research into more effective methods of computer-based intelligent identification of Alzheimer's disease.
This article proposes a finite-time proportional-integral-derivative(FT-PID) control method to fast stabilize the control system to achieve the desired performance within the predesignated time *** a considered nonaff...
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This article proposes a finite-time proportional-integral-derivative(FT-PID) control method to fast stabilize the control system to achieve the desired performance within the predesignated time *** a considered nonaffined nonlinear system, we develop a new dynamic linearization approach to reformulate the system model as a linear data model(LDM) whose arguments are consistent with that used in the PID control law. Then, a projection algorithm is presented to estimate the unknown pseudo gradient vector of the LDM. Subsequently, an adaptive tuning algorithm is designed to update the three PID parameters by solving linear matrix inequalities in terms of the predesignated error precision and the finite-time instant. The finite-time convergence of the proposed FT-PID control system is shown mathematically, which guarantees a pre-specified error precision to be achieved within the predesignated finite-time instants. As a result, not only can the proposed FT-PID control save the control cost but it also improves the production efficiency. The simulation study verifies the results.
A novel synthesis method for wideband bandpass filter (BPF) with two in-band conjugate complex transmission zeros is proposed for realizing frequency- and attenuation-reconfigurable in-band notch. A new characteristic...
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