Publications by authors named "Hilal Tayara"

18 Publications

  • Page 1 of 1

Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation.

Comput Struct Biotechnol J 2021 19;19:1612-1619. Epub 2021 Mar 19.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.

DNA N4-methylcytosine (4mC), an epigenetic modification found in prokaryotic and eukaryotic species, is involved in numerous biological functions, including host defense, transcription regulation, gene expression, and DNA replication. To identify 4mC sites, previous computational studies mostly focused on finding hand-crafted features. This area of research, therefore, would benefit from the development of a computational approach that relies on automatic feature selection to identify relevant sites. We here report 4mC-w2vec, a computational method that learned automatic feature discrimination in the genomes, especially in and , based on distributed feature representation and through the word embedding technique 'word2vec'. While a few bioinformatics tools are currently employed to identify 4mC sites in these , their prediction performance is inadequate. Our system processed 4mC and non-4mC sites through a word embedding process, including sub-word information of its biological words through k-mer, which then served as features that were fed into a double layer of convolutional neural network (CNN) to classify whether the sample sequences contained 4mCs or non-4mCs sites. Our tool demonstrated performance superior to current tools that use the same genomic datasets. Additionally, 4mC-w2vec is effective for balanced and imbalanced class datasets alike, and the online web-server is currently available at: http://nsclbio.jbnu.ac.kr/tools/4mC-w2vec/.
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http://dx.doi.org/10.1016/j.csbj.2021.03.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042287PMC
March 2021

4mCPred-CNN-Prediction of DNA N4-Methylcytosine in the Mouse Genome Using a Convolutional Neural Network.

Genes (Basel) 2021 Feb 20;12(2). Epub 2021 Feb 20.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea.

Among DNA modifications, N4-methylcytosine (4mC) is one of the most significant ones, and it is linked to the development of cell proliferation and gene expression. To know different its biological functions, the accurate detection of 4mC sites is required. Although we have several techniques for the prediction of 4mC sites in different genomes based on both machine learning (ML) and convolutional neural networks (CNNs), there is no CNN-based tool for the identification of 4mC sites in the mouse genome. In this article, a CNN-based model named 4mCPred-CNN was developed to classify 4mC locations in the mouse genome. Until now, we had only two ML-based models for this purpose; they utilized several feature encoding schemes, and thus still had a lot of space available to improve the prediction accuracy. Utilizing only a single feature encoding scheme-one-hot encoding-we outperformed both of the previous ML-based techniques. In a ten-fold validation test, the proposed model, 4mCPred-CNN, achieved an accuracy of 85.71% and Matthews correlation coefficient (MCC) of 0.717. On an independent dataset, the achieved accuracy was 87.50% with an MCC value of 0.750. The attained results exhibit that the proposed model can be of great use for researchers in the fields of biology and bioinformatics.
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http://dx.doi.org/10.3390/genes12020296DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924022PMC
February 2021

Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.

Sci Rep 2021 Jan 12;11(1):706. Epub 2021 Jan 12.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.

Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and binding modes. The understanding and analysis of these binding modes are expected to support the discovery of kinase-targeting drugs. The huge amounts of data made it possible to utilize computational techniques, including machine learning, to help in the discovery of kinase-targeting drugs. Machine learning gave reasonable predictions when applied to differentiate between the binding modes of kinase inhibitors, promoting a wider application in that domain. In this study, we applied machine learning supported by feature selection techniques to classify kinase inhibitors according to their binding modes. We represented inhibitors as a large number of molecular descriptors, as features, and systematically reduced these features in a multi-step manner while trying to attain high classification accuracy. Our predictive models could satisfy both goals by achieving high accuracy while utilizing at most 5% of the modeling features. The models could differentiate between binding mode types with MCC values between 0.67 and 0.92, and balanced accuracy values between 0.78 and 0.97 for independent test sets.
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http://dx.doi.org/10.1038/s41598-020-80758-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804204PMC
January 2021

DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine.

Sci Rep 2021 Jan 8;11(1):212. Epub 2021 Jan 8.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.

N4-methylcytosine is a biochemical alteration of DNA that affects the genetic operations without modifying the DNA nucleotides such as gene expression, genomic imprinting, chromosome stability, and the development of the cell. In the proposed work, a computational model, 4mCNLP-Deep, used the word embedding approach as a vector formulation by exploiting deep learning based CNN algorithm to predict 4mC and non-4mC sites on the C.elegans genome dataset. Diversity of ranges employed for the experimental such as corpus k-mer and k-fold cross-validation to obtain the prevailing capabilities. The 4mCNLP-Deep outperform from the state-of-the-art predictor by achieving the results in five evaluation metrics by following; Accuracy (ACC) as 0.9354, Mathew's correlation coefficient (MCC) as 0.8608, Specificity (Sp) as 0.89.96, Sensitivity (Sn) as 0.9563, and Area under curve (AUC) as 0.9731 by using 3-mer corpus word2vec and 3-fold cross-validation and attained the increment of 1.1%, 0.6%, 0.58%, 0.77%, and 4.89%, respectively. At last, we developed the online webserver http://nsclbio.jbnu.ac.kr/tools/4mCNLP-Deep/ , for the experimental researchers to get the results easily.
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http://dx.doi.org/10.1038/s41598-020-80430-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794489PMC
January 2021

pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters.

Genes (Basel) 2020 Dec 21;11(12). Epub 2020 Dec 21.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea.

A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.
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http://dx.doi.org/10.3390/genes11121529DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767505PMC
December 2020

XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials.

Sci Rep 2020 12 1;10(1):20942. Epub 2020 Dec 1.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.

N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA which plays a major role in the stability and regulation of mRNA translation. The working mechanism of ac4C modification in mRNA is still unclear and traditional laboratory experiments are time-consuming and expensive. Therefore, we propose an XG-ac4C machine learning model based on the eXtreme Gradient Boost classifier for the identification of ac4C sites. The XG-ac4C model uses a combination of electron-ion interaction pseudopotentials and electron-ion interaction pseudopotentials of trinucleotide of the nucleotides in ac4C sites. Moreover, Shapley additive explanations and local interpretable model-agnostic explanations are applied to understand the importance of features and their contribution to the final prediction outcome. The obtained results demonstrate that XG-ac4C outperforms existing state-of-the-art methods. In more detail, the proposed model improves the area under the precision-recall curve by 9.4% and 9.6% in cross-validation and independent tests, respectively. Finally, a user-friendly web server based on the proposed model for ac4C site identification is made freely available at http://nsclbio.jbnu.ac.kr/tools/xgac4c/ .
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http://dx.doi.org/10.1038/s41598-020-77824-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708984PMC
December 2020

Identification of Functional piRNAs Using a Convolutional Neural Network.

IEEE/ACM Trans Comput Biol Bioinform 2020 Oct 29;PP. Epub 2020 Oct 29.

Piwi-interacting RNAs (piRNAs) are a distinct sub-class of small non-coding RNAs that are mainly responsible for germline stem cell maintenance, gene stability, and maintaining genome integrity by repression of transposable elements. piRNAs are also expressed aberrantly and associated with various kinds of cancers. To identify piRNAs and their role in guiding target mRNA deadenylation, the currently available computational methods require urgent improvements in performance. To facilitate this, we propose a robust predictor based on a lightweight and simplified deep learning architecture using a convolutional neural network (CNN) to extract significant features from raw RNA sequences without the need for more customized features. The proposed model's performance is comprehensively evaluated using k-fold cross-validation on a benchmark dataset. The proposed model significantly outperforms existing computational methods in the prediction of piRNAs and their role in target mRNA deadenylation. In addition, a user-friendly and publicly-accessible web server is available at http://nsclbio.jbnu.ac.kr/tools/2S-piRCNN/.
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http://dx.doi.org/10.1109/TCBB.2020.3034313DOI Listing
October 2020

i6mA-stack: A stacking ensemble-based computational prediction of DNA N6-methyladenine (6mA) sites in the Rosaceae genome.

Genomics 2021 Jan 1;113(1 Pt 2):582-592. Epub 2020 Oct 1.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea. Electronic address:

DNA N6-methyladenine (6 mA) is an epigenetic modification that plays a vital role in a variety of cellular processes in both eukaryotes and prokaryotes. Accurate information of 6 mA sites in the Rosaceae genome may assist in understanding genomic 6 mA distributions and various biological functions such as epigenetic inheritance. Various studies have shown the possibility of identifying 6 mA sites through experiments, but the procedures are time-consuming and costly. To overcome the drawbacks of experimental methods, we propose an accurate computational paradigm based on a machine learning (ML) technique to identify 6 mA sites in Rosa chinensis (R.chinensis) and Fragaria vesca (F.vesca). To improve the performance of the proposed model and to avoid overfitting, a recursive feature elimination with cross-validation (RFECV) strategy is used to extract the optimal number of features (ONF) subset from five different DNA sequence encoding schemes, i.e., Binary Encoding (BE), Ring-Function-Hydrogen-Chemical Properties (RFHC), Electron-Ion-Interaction Pseudo Potentials of Nucleotides (EIIP), Dinucleotide Physicochemical Properties (DPCP), and Trinucleotide Physicochemical Properties (TPCP). Subsequently, we use the ONF subset to train a double layers of ML-based stacking model to create a bioinformatics tool named 'i6mA-stack'. This tool outperforms its peer tool in general and is currently available at http://nsclbio.jbnu.ac.kr/tools/i6mA-stack/.
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http://dx.doi.org/10.1016/j.ygeno.2020.09.054DOI Listing
January 2021

ncRDeep: Non-coding RNA classification with convolutional neural network.

Comput Biol Chem 2020 Oct 27;88:107364. Epub 2020 Aug 27.

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea. Electronic address:

A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involved in many biological processes, diseases, and cancers. Numerous ncRNAs have been identified and classified with high throughput sequencing technology. Hence, accurate ncRNAs class prediction is important and necessary for further study of their functions. Several computation techniques have been employed to predict the class of ncRNAs. Recent classification methods used the secondary structure as their primary input. However, the computational tools of RNA secondary structure are not accurate enough which affects the final performance of ncRNAs predictors. In this paper, we propose a simple yet efficient method, called ncRDeep, for ncRNAs prediction. It uses a simple convolutional neural network and RNA sequence information only. The ncRDeep was evaluated on benchmark datasets and the comparison results showed that the ncRDeep outperforms the state-of-the-art methods significantly. More specifically, the average accuracy was improved by 8.32%. Finally, we built a freely accessible web server for the developed tool ncRDeep at http://home.jbnu.ac.kr/NSCL/ncRDeep.htm.
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http://dx.doi.org/10.1016/j.compbiolchem.2020.107364DOI Listing
October 2020

Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning.

IEEE/ACM Trans Comput Biol Bioinform 2020 Mar 18;PP. Epub 2020 Mar 18.

RNA-binding proteins (RBPs) have a significant role in various regulatory tasks. However, the mechanism by which RBPs identify the subsequence target RNAs is still not clear. In recent years, several machine and deep learning-based computational models have been proposed for understanding the binding preferences of RBPs. These methods required integrating multiple features with raw RNA sequences such as secondary structure and their performances can be further improved. In this paper, we propose an efficient and simple convolution neural network, RBPCNN, that relies on the combination of the raw RNA sequence and evolutionary information. We show that conservation scores (evolutionary information) for the RNA sequences can significantly improve the overall performance of the proposed predictor. In addition, the automatic extraction of the binding sequence motifs can enhance our understanding of the binding specificities of RBPs. The experimental results show that RBPCNN outperforms significantly the current state-of-the-art methods. More specifically, the average area under the receiver operator curve was improved by 2.67% and the mean average precision was improved by 8.03%. The datasets, and results can be downloaded from https://home.jbnu.ac.kr/NSCL/RBPCNN.htm.
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http://dx.doi.org/10.1109/TCBB.2020.2981335DOI Listing
March 2020

Improving the Quantification of DNA Sequences Using Evolutionary Information Based on Deep Learning.

Cells 2019 12 14;8(12). Epub 2019 Dec 14.

Advanced Electronics and Information Research Center, Chonbuk National University, Jeonju 54896, Korea.

It is known that over 98% of the human genome is non-coding, and 93% of disease associated variants are located in these regions. Therefore, understanding the function of these regions is important. However, this task is challenging as most of these regions are not well understood in terms of their functions. In this paper, we introduce a novel computational model based on deep neural networks, called DQDNN, for quantifying the function of non-coding DNA regions. This model combines convolution layers for capturing regularity motifs at multiple scales and recurrent layers for capturing long term dependencies between the captured motifs. In addition, we show that integrating evolutionary information with raw genomic sequences improves the performance of the predictor significantly. The proposed model outperforms the state-of-the-art ones using raw genomics sequences only and also by integrating evolutionary information with raw genomics sequences. More specifically, the proposed model improves 96.9% and 98% of the targets in terms of area under the receiver operating characteristic curve and the precision-recall curve, respectively. In addition, the proposed model improved the prioritization of functional variants of expression quantitative trait loci (eQTLs) compared with the state-of-the-art models.
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http://dx.doi.org/10.3390/cells8121635DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952993PMC
December 2019

Identification of prokaryotic promoters and their strength by integrating heterogeneous features.

Genomics 2020 03 19;112(2):1396-1403. Epub 2019 Aug 19.

Department of Electronics and Information Engineering, Chonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Chonbuk National University, Jeonju 54896, South Korea. Electronic address:

The promoter is a regulatory DNA region and important for gene transcriptional regulation. It is located near the transcription start site (TSS) upstream of the corresponding gene. In the post-genomics era, the availability of data makes it possible to build computational models for robustly detecting the promoters as these models are expected to be helpful for academia and drug discovery. Until recently, developed models focused only on discriminating the sequences into promoter and non-promoter. However, promoter predictors can be further improved by considering weak and strong promoter classification. In this work, we introduce a hybrid model, named iPSW(PseDNC-DL), for identification of prokaryotic promoters and their strength. It combines a convolutional neural network with a pseudo-di-nucleotide composition (PseDNC). The proposed model iPSW(PseDNC-DL) has been evaluated on the benchmark datasets and outperformed the current state-of-the-art models in both tasks namely promoter identification and promoter strength identification. The developed tool iPSW(PseDNC-DL) has been constructed in a web server and made freely available at https://home.jbnu.ac.kr/NSCL/PseDNC-DL.htm.
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http://dx.doi.org/10.1016/j.ygeno.2019.08.009DOI Listing
March 2020

Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning.

Genes (Basel) 2019 08 1;10(8). Epub 2019 Aug 1.

Department of Electronics and Information Engineering, Chonbuk National University, Jeonju 54896, Korea.

Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavior and regulation using computational models known as "Splicing Codes". The final goal of these algorithms is to make an in-silico prediction of AS outcome from genomic sequence. Here, we develop a deep learning approach, called Deep Splicing Code (DSC), for categorizing the well-studied classes of AS namely alternatively skipped exons, alternative 5ss, alternative 3ss, and constitutively spliced exons based only on the sequence of the exon junctions. The proposed approach significantly improves the prediction and the obtained results reveal that constitutive exons have distinguishable local characteristics from alternatively spliced exons. Using the motif visualization technique, we show that the trained models learned to search for competitive alternative splice sites as well as motifs of important splicing factors with high precision. Thus, the proposed approach greatly expands the opportunities to improve alternative splicing modeling. In addition, a web-server for AS events prediction has been developed based on the proposed method and made available at https://home.jbnu.ac.kr/NSCL/dsc.htm.
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http://dx.doi.org/10.3390/genes10080587DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722613PMC
August 2019

iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks.

Mol Ther Nucleic Acids 2019 Jun 11;16:463-470. Epub 2019 Apr 11.

Advanced Electronics and Information Research Center, Chonbuk National University, Jeonju 54896, South Korea. Electronic address:

Pseudouridine is the most prevalent RNA modification and has been found in both eukaryotes and prokaryotes. Currently, pseudouridine has been demonstrated in several kinds of RNAs, such as small nuclear RNA, rRNA, tRNA, mRNA, and small nucleolar RNA. Therefore, its significance to academic research and drug development is understandable. Through biochemical experiments, the pseudouridine site identification has produced good outcomes, but these lab exploratory methods and biochemical processes are expensive and time consuming. Therefore, it is important to introduce efficient methods for identification of pseudouridine sites. In this study, an intelligent method for pseudouridine sites using the deep-learning approach was developed. The proposed prediction model is called iPseU-CNN (identifying pseudouridine by convolutional neural networks). The existing methods used handcrafted features and machine-learning approaches to identify pseudouridine sites. However, the proposed predictor extracts the features of the pseudouridine sites automatically using a convolution neural network model. The iPseU-CNN model yields better outcomes than the current state-of-the-art models in all evaluation parameters. It is thus highly projected that the iPseU-CNN predictor will become a helpful tool for academic research on pseudouridine site prediction of RNA, as well as in drug discovery.
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http://dx.doi.org/10.1016/j.omtn.2019.03.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488737PMC
June 2019

DeePromoter: Robust Promoter Predictor Using Deep Learning.

Front Genet 2019 5;10:286. Epub 2019 Apr 5.

Advanced Research Center of Information and Electronics Engineering, Chonbuk National University, Jeonju, South Korea.

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm.
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http://dx.doi.org/10.3389/fgene.2019.00286DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460014PMC
April 2019

iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components.

J Theor Biol 2019 03 24;465:1-6. Epub 2018 Dec 24.

Advanced Electronics and Information Research Center, Chonbuk National University, Jeonju 54896, South Korea. Electronic address:

The 2'-O-methylation transferase is involved in the process of 2'-O-methylation. In catalytic processes, the 2-hydroxy group of the ribose moiety of a nucleotide accept a methyl group. This methylation process is a post-transcriptional modification, which occurs in various cellular RNAs and plays a vital role in regulation of gene expressions at the post-transcriptional level. Through biochemical experiments 2'-O-methylation sites produce good results but these biochemical process and exploratory techniques are very expensive. Thus, it is required to develop a computational method to identify 2'-O-methylation sites. In this work, we proposed a simple and precise convolution neural network method namely: iRNA-PseKNC(2methyl) to identify 2'-O-methylation sites. The existing techniques use handcrafted features, while the proposed method automatically extracts the features of 2'-O-methylation using the proposed convolution neural network model. The proposed prediction iRNA-PseKNC(2methyl) method obtained 98.27% of accuracy, 96.29% of sensitivity, 100% of specificity, and 0.965 of MCC on Home sapiens dataset. The reported outcomes present that our proposed method obtained better outcomes than existing method in terms of all evaluation parameters. These outcomes show that iRNA-PseKNC(2methyl) method might be beneficial for the academic research and drug design.
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http://dx.doi.org/10.1016/j.jtbi.2018.12.034DOI Listing
March 2019

Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network.

Sensors (Basel) 2018 Oct 6;18(10). Epub 2018 Oct 6.

Advanced Electronics and Information Research Center, Chonbuk National University, Jeonju 54896, Korea.

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.
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http://dx.doi.org/10.3390/s18103341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210269PMC
October 2018

A Real-Time Marker-Based Visual Sensor Based on a FPGA and a Soft Core Processor.

Sensors (Basel) 2016 Dec 15;16(12). Epub 2016 Dec 15.

Advanced Electronics and Information Research Center, Chonbuk National University, Jeonju 54896, Korea.

This paper introduces a real-time marker-based visual sensor architecture for mobile robot localization and navigation. A hardware acceleration architecture for post video processing system was implemented on a field-programmable gate array (FPGA). The pose calculation algorithm was implemented in a System on Chip (SoC) with an Altera Nios II soft-core processor. For every frame, single pass image segmentation and Feature Accelerated Segment Test (FAST) corner detection were used for extracting the predefined markers with known geometries in FPGA. Coplanar PosIT algorithm was implemented on the Nios II soft-core processor supplied with floating point hardware for accelerating floating point operations. Trigonometric functions have been approximated using Taylor series and cubic approximation using Lagrange polynomials. Inverse square root method has been implemented for approximating square root computations. Real time results have been achieved and pixel streams have been processed on the fly without any need to buffer the input frame for further implementation.
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http://dx.doi.org/10.3390/s16122139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191119PMC
December 2016