3,668 results match your criteria framework automatic


Biologically-Inspired Pulse Signal Processing for Intelligence at the Edge.

Front Artif Intell 2021 8;4:568384. Epub 2021 Sep 8.

Computational NeuroEngineering Laboratory (CNEL), Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.

There is an ever-growing mismatch between the proliferation of data-intensive, power-hungry deep learning solutions in the machine learning (ML) community and the need for agile, portable solutions in resource-constrained devices, particularly for intelligence at the edge. In this paper, we present a fundamentally novel approach that leverages data-driven intelligence with biologically-inspired efficiency. The proposed Sparse Embodiment Neural-Statistical Architecture (SENSA) decomposes the learning task into two distinct phases: a training phase and a hardware embedment phase where prototypes are extracted from the trained network and used to construct fast, sparse embodiment for hardware deployment at the edge. Read More

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September 2021

Research on Taproots Identification Technology in Quality Intelligent Management System.

Comput Intell Neurosci 2021 16;2021:8292535. Epub 2021 Sep 16.

School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China.

In the quality intelligent management system, the big roots and fibrous roots cannot be cut automatically because the machine cannot distinguish the taproot, big roots, and fibrous roots of , resulting in the automatic cutting mechanism unable to obtain the control trajectory coordinate reference of the tool feed. To solve this problem, this paper proposes a visual optimal network model detection method, which uses the image detection method of marking anchor frames to improve the detection accuracy. A variety of deep learning network models are modified by the TensorFlow framework, and the best training model is optimized by comparing the results of training, testing, and verification data. Read More

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September 2021

Species Delimitation of Asteropyrum (Ranunculaceae) Based on Morphological, Molecular, and Ecological Variation.

Front Plant Sci 2021 10;12:681864. Epub 2021 Sep 10.

Laboratory of Subtropical Biodiversity, Jiangxi Agricultural University, Nanchang, China.

Objectively evaluating different lines of evidence within a formalized framework is the most efficient and theoretically grounded approach for defining robust species hypotheses. Asteropyrum Drumm. et Hutch. Read More

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September 2021

On the Efficacy of Handcrafted and Deep Features for Seed Image Classification.

J Imaging 2021 Aug 31;7(9). Epub 2021 Aug 31.

Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.

Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. Read More

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Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net.

Comput Methods Programs Biomed 2021 Sep 15;211:106419. Epub 2021 Sep 15.

School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. Electronic address:

Background And Objective: Accurately and reliably defining organs at risk (OARs) and tumors are the cornerstone of radiation therapy (RT) treatment planning for lung cancer. Almost all segmentation networks based on deep learning techniques rely on fully annotated data with strong supervision. However, existing public imaging datasets encountered in the RT domain frequently include singly labelled tumors or partially labelled organs because annotating full OARs and tumors in CT images is both rigorous and tedious. Read More

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September 2021

Automatic multi-plaque tracking and segmentation in ultrasonic videos.

Med Image Anal 2021 Sep 22;74:102201. Epub 2021 Sep 22.

School of Information Science and Technology, Fudan University, Shanghai, China. Electronic address:

Carotid plaque tracking and segmentation in ultrasound videos is the premise for subsequent plaque property evaluation and treatment plan development. However, the task is quite challenging, as it needs to address the problems of poor image quality, plaque shape variations among frames, the existence of multiple plaques, etc. To overcome these challenges, we propose a new automatic multi-plaque tracking and segmentation (AMPTS) framework. Read More

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September 2021

MIDGET:Detecting differential gene expression on microarray data.

Comput Methods Programs Biomed 2021 Sep 16;211:106418. Epub 2021 Sep 16.

Department of Automatic Control and Industrial Informatics, Faculty of Automatic Control and Computer Science, University "Politehnica" of Bucharest, Splaiul Independentei nr. 313, Sector 6, Bucuresti, 060042, Romania. Electronic address:

Backgound and Objective: Detecting differentially expressed genes is an important step in genome wide analysis and expression profiling. There are a wide array of algorithms used in today's research based on statistical approaches. Even though the current algorithms work, they sometimes miss-predict. Read More

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September 2021

A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images.

Phys Eng Sci Med 2021 Sep 22. Epub 2021 Sep 22.

Tripura University, Agartala, India.

Brain-computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. Read More

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September 2021

Semantic-oriented Labeled-to-unlabeled Distribution Translation for Image Segmentation.

IEEE Trans Med Imaging 2021 Sep 20;PP. Epub 2021 Sep 20.

Automatic medical image segmentation plays a crucial role in many medical applications, such as disease diagnosis and treatment planning. Existing deep learning based models usually regarded the segmentation task as pixel-wise classification and neglected the semantic correlations of pixels across different images, leading to vague feature distribution. Moreover, pixel-wise annotated data is rare in medical domain, and the scarce annotated data usually exhibits the biased distribution against the desired one, hindering the performance improvement under the supervised learning setting. Read More

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September 2021

Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy.

Nat Mach Intell 2021 Sep 9;3(9):799-811. Epub 2021 Aug 9.

Computer-assisted Applications in Medicine, Computer Vision Lab, ETH Zurich, Switzerland.

Fluorescence microscopy allows for a detailed inspection of cells, cellular networks, and anatomical landmarks by staining with a variety of carefully-selected markers visualized as color channels. Quantitative characterization of structures in acquired images often relies on automatic image analysis methods. Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited. Read More

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September 2021

Automatic deep learning system for COVID-19 infection quantification in chest CT.

Multimed Tools Appl 2021 Sep 13:1-15. Epub 2021 Sep 13.

College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait.

The paper proposes an automatic deep learning system for COVID-19 infection areas segmentation in chest CT scans. CT imaging proved its ability to detect the COVID-19 disease even for asymptotic patients, which make it a trustworthy alternative for PCR. Coronavirus disease spread globally and PCR screening is the adopted diagnostic testing method for COVID-19 detection. Read More

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September 2021

An Efficient High-Order Meshless Method for Advection-Diffusion Equations on Time-Varying Irregular Domains.

J Comput Phys 2021 Nov 12;445. Epub 2021 Aug 12.

Departments of Mathematics and Biomedical Engineering, University of Utah, UT, USA.

We present a high-order radial basis function finite difference (RBF-FD) framework for the solution of advection-diffusion equations on time-varying domains. Our framework is based on a generalization of the recently developed Overlapped RBF-FD method that utilizes a novel automatic procedure for computing RBF-FD weights on stencils in variable-sized regions around stencil centers. This procedure eliminates the overlap parameter , thereby enabling tuning-free assembly of RBF-FD differentiation matrices on moving domains. Read More

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November 2021

In-depth learning of automatic segmentation of shoulder joint magnetic resonance images based on convolutional neural networks.

Comput Methods Programs Biomed 2021 Jul 31;211:106325. Epub 2021 Jul 31.

Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, 166 Yulong Road West, Tinghu District, China; The First People's Hospital of Yancheng, 166 Yulong Road West, Tinghu District, China.

Objective: Magnetic resonance imaging (MRI) is gradually replacing computed tomography (CT) in the examination of bones and joints. The accurate and automatic segmentation of the bone structure in the MRI of the shoulder joint is essential for the measurement and diagnosis of bone injuries and diseases. The existing bone segmentation algorithms cannot achieve automatic segmentation without any prior knowledge, and their versatility and accuracy are relatively low. Read More

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Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks.

Proc SPIE Int Soc Opt Eng 2021 15;11596. Epub 2021 Feb 15.

Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37212.

Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations are highly dependent on manual efforts, which are time-consuming and tedious. Hence, developing an automatic framework for the segmentation of renal cortex, medulla and pelvicalyceal system is an important quantitative assessment of renal morphometry. Read More

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February 2021

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms.

Artif Intell Med 2021 09 28;119:102156. Epub 2021 Aug 28.

Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Egypt. Electronic address:

COVID-19 (Coronavirus) went through a rapid escalation until it became a pandemic disease. The normal and manual medical infection discovery may take few days and therefore computer science engineers can share in the development of the automatic diagnosis for fast detection of that disease. The study suggests a hybrid COVID-19 framework (named HMB-HCF) based on deep learning (DL), genetic algorithm (GA), weighted sum (WS), and majority voting principles in nine phases. Read More

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September 2021

Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics.

Artif Intell Med 2021 Sep 11;119:102140. Epub 2021 Aug 11.

School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK. Electronic address:

Combining biomechanical modelling of left ventricular (LV) function and dysfunction with cardiac magnetic resonance (CMR) imaging has the potential to improve the prognosis of patient-specific cardiovascular disease risks. Biomechanical studies of LV function in three dimensions usually rely on a computerized representation of the LV geometry based on finite element discretization, which is essential for numerically simulating in vivo cardiac dynamics. Detailed knowledge of the LV geometry is also relevant for various other clinical applications, such as assessing the LV cavity volume and wall thickness. Read More

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September 2021

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science.

J Chem Inf Model 2021 Sep 16;61(9):4280-4289. Epub 2021 Sep 16.

Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.

The ever-growing abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. While in the past, in the physical sciences domain, the focus has been on the precise extraction of individual properties, attention has recently been devoted to the extraction of higher-level relationships. Here, we present a framework for an automated population of ontologies. Read More

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September 2021

SGI: Automatic clinical subgroup identification in omics datasets.

Bioinformatics 2021 Sep 16. Epub 2021 Sep 16.

Department of Physiology and Biophysics, Institute for Computational Biomedicine.

: The 'Subgroup Identification' (SGI) toolbox provides an algorithm to automatically detect clinical subgroups of samples in large-scale omics datasets. It is based on hierarchical clustering trees in combination with a specifically designed association testing and visualization framework that can process an arbitrary number of clinical parameters and outcomes in a systematic fashion. A multi-block extension allows for the simultaneous use of multiple omics datasets on the same samples. Read More

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September 2021

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning.

J Chem Inf Model 2021 Sep 16;61(9):4303-4320. Epub 2021 Sep 16.

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.

One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Read More

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September 2021

A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing.

Neural Comput Appl 2021 Sep 10:1-15. Epub 2021 Sep 10.

College of Engineering, Alfaisal University, Riyadh, Kingdom of Saudi Arabia.

Coronavirus (COVID-19) is a very contagious infection that has drawn the world's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. Read More

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September 2021

Tensor electrical impedance myography identifies clinically relevant features in amyotrophic lateral sclerosis.

Physiol Meas 2021 Sep 14. Epub 2021 Sep 14.

The University of Sheffield SITraN, Sheffield, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.

Objective: Electrical impedance myography (EIM) shows promise as an effective biomarker in amyotrophic lateral sclerosis (ALS). EIM applies multiple input frequencies to characterise muscle properties, often via multiple electrode configurations. Herein, we assess if non-negative tensor factorisation can provide a framework for identifying clinically relevant features within a high dimensional EIM dataset. Read More

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September 2021

An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography.

Math Biosci Eng 2021 06;18(5):5321-5346

Faculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, District Swabi, KPK, Pakistan.

Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its early stages. Deep Convolutional Neural Networks (Deep CNNs) and its variants have provided superior performance in glaucoma classification, segmentation, and detection. In this paper, we propose a two-staged glaucoma classification scheme based on Deep CNN architectures. Read More

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Fully Convolutional Network and Visual Saliency-Based Automatic Optic Disc Detection in Retinal Fundus Images.

J Healthc Eng 2021 31;2021:3561134. Epub 2021 Aug 31.

School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

We present in this paper a novel optic disc detection method based on a fully convolutional network and visual saliency in retinal fundus images. Firstly, we employ the morphological reconstruction-based object detection method to locate the optic disc region roughly. According to the location result, a 400 × 400 image patch that covers the whole optic disc is obtained by cropping the original retinal fundus image. Read More

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A CAUSAL DEEP LEARNING FRAMEWORK FOR CLASSIFYING PHONEMES IN COCHLEAR IMPLANTS.

Proc IEEE Int Conf Acoust Speech Signal Process 2021 Jun 13;2021:6498-6502. Epub 2021 May 13.

Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.

Speech intelligibility in cochlear implant (CI) users degrades considerably in listening environments with reverberation and noise. Previous research in automatic speech recognition (ASR) has shown that phoneme-based speech enhancement algorithms improve ASR system performance in reverberant environments as compared to a global model. However, phoneme-specific speech processing has not yet been implemented in CIs. Read More

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Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets.

Resuscitation 2021 Sep 9. Epub 2021 Sep 9.

Department of Emergency Medicine Ohio State University Columbus, OH, United States.

Background: Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services.

Aim: To introduce and evaluate a methodology to automatically extract cardiopulmonary resuscitation (CPR) quality data in a uniform and systematic way from OHCA datasets from multiple heterogeneous sources.

Methods: A dataset of 2236 OHCA cases from multiple defibrillator models and manufacturers was analyzed. Read More

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September 2021

Skin lesion image retrieval using transfer learning-based approach for query-driven distance recommendation.

Comput Biol Med 2021 Sep 3;137:104825. Epub 2021 Sep 3.

Higher Institute of Technological Studies of Mahdia, 5111, Hiboun, Mahdia, Tunisia.

Content-Based Dermatological Lesion Retrieval (CBDLR) systems retrieve similar skin lesion images, with a pathology-confirmed diagnosis, for a given query image of a skin lesion. By producing an intuitive support to both inexperienced and experienced dermatologists, the early diagnosis through CBDLR screening can significantly enhance the patients' survival, while reducing the treatment cost. To deal with this issue, a CBDLR system is proposed in this study. Read More

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September 2021

SFPD: Simultaneous Face and Person Detection in Real-Time for Human-Robot Interaction.

Sensors (Basel) 2021 Sep 2;21(17). Epub 2021 Sep 2.

Neuro-Information Technology Group, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany.

Face and person detection are important tasks in computer vision, as they represent the first component in many recognition systems, such as face recognition, facial expression analysis, body pose estimation, face attribute detection, or human action recognition. Thereby, their detection rate and runtime are crucial for the performance of the overall system. In this paper, we combine both face and person detection in one framework with the goal of reaching a detection performance that is competitive to the state of the art of lightweight object-specific networks while maintaining real-time processing speed for both detection tasks together. Read More

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September 2021

Computationally Efficient Nonlinear Model Predictive Control Using the L Cost-Function.

Sensors (Basel) 2021 Aug 30;21(17). Epub 2021 Aug 30.

Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.

Model Predictive Control (MPC) algorithms typically use the classical L2 cost function, which minimises squared differences of predicted control errors. Such an approach has good numerical properties, but the L1 norm that measures absolute values of the control errors gives better control quality. If a nonlinear model is used for prediction, the L1 norm leads to a difficult, nonlinear, possibly non-differentiable cost function. Read More

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Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems.

Sensors (Basel) 2021 Aug 30;21(17). Epub 2021 Aug 30.

HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico.

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. Read More

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