Situation recognition using image moments and recurrent neural networks

2021

IEEE Access

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Proto-oncogenes are a group of genes that cause normal cells to become cancerous as a result of mutations. Protooncogenes encode proteins that function to stimulate cell division, inhibit cell differentiation, and prevent cell death. While the identification of the proto-oncogene may occur at different phases of the cancer-causing processes, the accuracy of the identification method is always questionable. Prediction through in-vitro experimentations is considered sometimes a standard procedure but is very time taking, laborious, and costly. This problem is addressed by opting for computer-aided approaches established in bioinformatics and computational biology studies. Early prognosis of cancer is crucial towards the full recovery of the patient. Proto-oncogene proteins are an important biomarker that helps identify the onset of a specific type of cancer. Keeping this in mind, this study proposes an effective new method for the prediction of proto-oncogenes. The predictor proposed in this study calculates statistical moments and position-based features incorporated into pseudo amino-acid composition (PseAAC) based on Chou's 5step rules. Subsequently, the study proposes the use of a random forest classifier for the accurate prediction of proto-oncogenes. The method was validated using the 10 folds cross-validation, Jackknife testing, Self-Consistency, and Independent set testing, giving 95.44%, 94.89%, 97.38%, and 96.97% accurate results, respectively. These results depict that the proposed model can play a key role in the early prognosis of cancer and aid scientists in the discovery of mechanisms against cancer.

Section: Statistical Moment's Calculationmentioning

confidence: 99%

ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins

Malebary

2021

IEEE Access

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“…Due to such distinction, each sequence is to be described with different statistical parameters. In previous work, statistical moments are used for feature extraction 22,23 . In order to have feature extraction, raw, central and Hahn moments are used.…”

Section: Statistical Moment Calculationmentioning

confidence: 99%

Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification

Shah

2020

Sci Rep

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Glutamic acid is an alpha-amino acid used by all living beings in protein biosynthesis. One of the important glutamic acid modifications is post-translationally modified 4-carboxyglutamate. It has a significant role in blood coagulation. 4-carboxyglumates are required for the binding of calcium ions. On the contrary, this modification can also cause different diseases such as bone resorption, osteoporosis, papilloma, and plaque atherosclerosis. Considering its importance, it is necessary to predict the occurrence of glutamic acid carboxylation in amino acid stretches. As there is no computational based prediction model available to identify 4-carboxyglutamate modification, this study is, therefore, designed to predict 4-carboxyglutamate sites with a less computational cost. A machine learning model is devised with a Multilayered Perceptron (MLP) classifier using Chou’s 5-step rule. It may help in learning statistical moments and based on this learning, the prediction is to be made accurately either it is 4-carboxyglutamate residue site or detected residue site having no 4-carboxyglutamate. Prediction accuracy of the proposed model is 94% using an independent set test, while obtained prediction accuracy is 99% by self-consistency tests.

“…In this study, cascade forward back propagation neural network [3], [37] & [40] was used for training. There are different variants of back propagation algorithm such as gradient descent algorithm with adaptive learning rate, Fletch Reeves update conjugate gradient, Polak-Ribere update conjugate gradient, Powell-Beale restarts conjugate gradient and scaled conjugate gradient algorithm, etc.…”

Section: G Classifiermentioning

confidence: 99%

A Scale and Rotation Invariant Urdu Nastalique Ligature Recognition Using Cascade Forward Backpropagation Neural Network

Rehman

2019

IEEE Access

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In the emerging age of technologies, machines are becoming more and more skilled and capable just like humans. Despite the fact that machines do not have their own intelligence, but still due to advancement in Artificial Intelligence (AI), machines are rapidly advancing. The area of Pattern Recognition (PR) deals with bringing enhancements to identify obscure patterns corresponding to specific classes. Optical Character Recognition (OCR) is a subfield of PR which deals with the recognition of characters. A great work has been done for Japanese, Hindi, Arabic and Chinese scripts, but only a diminutive work has been done for Urdu script. The Urdu language is highly cursive and is written in different calligraphic styles like Naskh, Nastalique, Kofi, Devani and Riqa. The Nastalique font is very calligraphic with aesthetic beauty. The ligature segmentation of Urdu Nastalique is also more difficult as compared to other languages. Urdu Nastalique has some characteristics like stacking of ligatures and cursiveness which makes its ligature segmentation a difficult task. Cursiveness means ligatures are joined together to form a new shape. It contains connected ligatures which makes it more complicated as compared to other languages. The ligature recognition of Urdu text by an OCR is a strenuous task due to variants of scaling, rotation, orientation and font style. In this study, a scale and rotation invariant classifier for Urdu Nastalique OCR is proposed. A combination of scale and location invariant moments is used for feature extraction and the classification is performed using Cascade Forward Backpropagation Neural Network. The model is validated through independent dataset testing and 5-fold cross-validation which gave 96.474% and 96.922% accuracy. The results depict the adaptability of the proposed model due to its high accuracy for recognition of Urdu Nastalique Ligature.INDEX TERMS Deep neural network (DNN), optical character recognition (OCR), scale invariant classifier, rotation invariant classifier.