A Heuristic Scalable Classifier Ensemble of Binary Classifier Ensembles

Parvïn, Hamïd; Alinejad‐Rokny, Hamid; Seyedaghaee, Naghi. R.; Parvin, Sajad

doi:10.1166/jbic.2013.1016

Cited by 12 publications

(5 citation statements)

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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

Self Cite

View full text Add to dashboard Cite

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“…The medical and biological datasets are increasing rapidly. To analyse such big and complex data, artificial intelligence and integrative pipelines become most popular (5,(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43). Here, we have developed SomaGene, a novel, user-friendly, interactive, open-access pipeline for identifying and annotating noncoding genes (or genomic coordinates) that significantly mutate in a cancer genome.…”

Section: Application Of Somagenementioning

confidence: 99%

A New R Package for Categorizing Coding and Non-Coding Genes

Bayati¹,

Rezaie²,

Hamidi³

et al. 2023

Preprint

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Previous studies demonstrate the critical importance of non-coding RNAs interfacing with chromatin-modifying machinery resulting in promoter-enhancer-based gene regulation and raise the possibility that many other enhancer-like RNAs may operate via similar mechanisms. Critically, more than 80% of the disease-linked variations identified in genome-wide studies are located in the non-coding regions of genomes, especially non-coding RNA, suggesting non-coding RNAs are relevant to disease. Thus, a critical path forward for understanding non-coding RNAs' role, especially long non-coding RNAs, is to understand the genomic regions' transcriptional regulation, especially non-coding regions. Here, we developed a user-friendly R package called SomaGene for studying and identifying enhancer-like non-coding RNAs with enriched somatic mutations in the cancer genome. SomaGene accepts different genomic variants (whole genome/exome somatic point mutations, structural variations, copy number variations) to identify those RNAs that significantly mutated in diseases (e.g., cancer). It then uses multiple publicly available genomics and epigenetics datasets including ENCODE epigenomics annotations, FANTOM5 tissue-specific expression profiles, disease-associated genome-wide association SNPs, and tissue-specific eQTL pairs to identify those RNAs with potentially enhancer function. SomaGene, as a powerful R package, can provide the opportunity to cancer scientists to study the roles of non-coding RNAs in different cancer genomes.

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“…Our literature searches were focused on human and mice English language papers available in the PubMed, Scopus, and Web of Science. We also used data and text mining techniques to extract additional related studies [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73]. A knowledgebased filtering system technique has been also used to categoriz e the texts from the literatures search [74][75][76][77][78][79].…”

Section: Literature Search Strategymentioning

confidence: 99%

An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer

Labani¹,

Beheshti²,

Argha³

et al. 2023

Preprint

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Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies showed that genomic alterations represent the most common mechanism for molecular alterations that cause the development and progression of PC. Great efforts have been done to identify common protein-coding genetic variations; however, the impact of non-coding variations including regulatory genetic variants is not still well understood. To gain an understanding of the functional impact of genetic variants, particularly, regulatory variants in PC, we developed an integrative pipeline (AGV) that used whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 of them significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants may affect 131 coding and non-coding genes. Interestingly, our study showed the 131 protein-coding genes are involved in disease-related pathways including Reactome and MSigDB in which for most of them targeted treatment options are currently available. Together, our results provide a comprehensive map of genomic variants in PC and revealed their potential contribution to prostate cancer progression and development.

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A Heuristic Scalable Classifier Ensemble of Binary Classifier Ensembles

Cited by 12 publications

References 0 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

A New R Package for Categorizing Coding and Non-Coding Genes

An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer

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