A New Divide and Conquer Based Classification for OCR

Parvïn, Hamïd; Alizadeh, Hosein; Minaei‐Bidgoli, Behrouz

doi:10.5772/9633

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…The future study involves examining multiple forms of human variables such as personality characteristics, age, and gender that affect the efficiency of emotion detection. Because of the increasing availability of large medical data, machine learning techniques are being used to reveal hidden healthcare trends [61][62][63][64][65][66][67] . Deep neural networks, in particular, have recently been employed www.nature.com/scientificreports/ in healthcare applications 68 .…”

Section: Discussionmentioning

confidence: 99%

Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity

Debnath

Reza

Rahman

et al. 2022

Sci Rep

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Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human–computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human–robot communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. The features extracted by the Local Binary Pattern (LBP), region based Oriented FAST and rotated BRIEF (ORB) and Convolutional Neural network (CNN) from facial expressions images were fused to develop the classification model through training by our proposed CNN model (ConvNet). Our method can converge quickly and achieves good performance which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this study focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases at first, and then apply the generalization techniques to the JAFFE and CK+ datasets respectively in the testing stage to evaluate the performance of the model. In the generalization approach on the JAFFE dataset, we get a 92.05% accuracy, while on the CK+ dataset, we acquire a 98.13% accuracy which achieve the best performance among existing methods. We also test the system’s success by identifying facial expressions in real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. However, when compared to other validation methods, the suggested technique was more accurate. ConvNet also achieved validation accuracy of 91.01% for the FER2013 dataset. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

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Section: Discussionmentioning

confidence: 99%

Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity

Debnath

Reza

Rahman

et al. 2022

Sci Rep

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show abstract

“…Clustering of sequences is the process of bringing similar motifs together as a cluster. Different clustering approaches have been previously used to group biological and nonbiological data [ 17 , 18 , 19 , 20 ]. Clustering of sequences gives the user the ability to observe each group separately, identify similar patterns among samples in the same group, identify differences between different clusters, and find the most important motifs in each group.…”

Section: Methodsmentioning

confidence: 99%

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Rajaei

Jahanian

Beheshti

et al. 2021

Genes

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Bioinformatics and computational biology have significantly contributed to the generation of vast and important knowledge that can lead to great improvements and advancements in biology and its related fields. Over the past three decades, a wide range of tools and methods have been developed and proposed to enhance performance, diagnosis, and throughput while maintaining feasibility and convenience for users. Here, we propose a new user-friendly comprehensive tool called VIRMOTIF to analyze DNA sequences. VIRMOTIF brings different tools together as one package so that users can perform their analysis as a whole and in one place. VIRMOTIF is able to complete different tasks, including computing the number or probability of motifs appearing in DNA sequences, visualizing data using the matplotlib and heatmap libraries, and clustering data using four different methods, namely K-means, PCA, Mean Shift, and ClusterMap. VIRMOTIF is the only tool with the ability to analyze genomic motifs based on their frequency and representation (D-ratio) in a virus genome.

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“…Our literature searches to identify cancer-associated genes were focused on human studies and English language publications available in the PubMed, Scopus, and Web of Science. We also used data and text mining techniques to extract additional related studies [29][30][31][32][33][34][35]. A decision tree approach and a knowledge-based filtering system technique have been also used to categorize the texts from the literatures search [33, 36].…”

Section: Literature Search For Non-coding Interacting Genesmentioning

confidence: 99%

An Investigation of Point Mutations Discovers Novel Genes and Their Corresponding Motifs in Pancreatic Cancer

Ghareyazi¹,

Mohseni²,

Dashti³

et al. 2021

Preprint

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It has now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4,211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

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A New Divide and Conquer Based Classification for OCR

Cited by 5 publications

References 24 publications

Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity

Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

An Investigation of Point Mutations Discovers Novel Genes and Their Corresponding Motifs in Pancreatic Cancer

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