Novel similarity measure-based random forest for fingerprint recognition using dual-tree complex wavelet transform and ring projection

Kaur, Harinder; Kaur, Gaganpreet; Pannu, Husanbir Singh

doi:10.1142/s0217984920500220

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…The first stage of their suggested three-phase technique had been an attribute selection procedure that was carried out by maintaining an organized list of characteristics that have been maintained in diminishing rank ordering [34][35][36][37][38][39]. New characteristics were generated in the second stage of applying the method of selecting additional characteristics from each subtype of the characteristics of the original database [40][41][42]. The tests were performed in the last step using a neighboring k-nearest & SVM classifier [43].…”

Section: Related Workmentioning

confidence: 99%

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Talari,

Kaur

et al. 2024

PLoS ONE

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Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model’s first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system’s result is to enhance the classifier’s performance in spotting illness early.

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Section: Related Workmentioning

confidence: 99%

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Talari,

Kaur

et al. 2024

PLoS ONE

Self Cite

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“…7. For the purpose of patient survival, the convolutional 1-D model was applied in our experimental investigation to identify cancer [31][32][33]. A deep neural network may be trained to classify sequence data using a 1D CNN.…”

Section: • Recurrent Neural Networkmentioning

confidence: 99%

Rectal Cancer Prediction and Performance Based on Intelligent Variational Autoencoders Machine Using Deep Learning on CDAS Dataset

Kaur

Keshta

Shabaz

et al. 2023

JAIT

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A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival. However, there isn't yet a valid prediction model available. An efficient feature extraction technique is also required to increase a prediction model's precision. CDAS (cancer data access system) program is a great place to look for cancer along with images or biospecimens. In this study, we look at data from the CDAS system, specifically Bowel cancer (colorectal cancer) datasets. This study suggested a survival prediction method for rectal cancer. In addition, determines which deep learning algorithm works best by comparing their performance in terms of prediction accuracy. The initial job that leads to correct findings is corpus cleansing. Moving forward, the data pre-processing activity will be performed, which will comprise "exploratory data analysis and pruning and normalization or experimental study of data, which is required to obtain data features to design the model for cancer detection at an early stage." Aside from that, the data corpus is separated into two sub-corpora: training data and test data, which will be utilized to assess the correctness of the constructed model. This study will compare our auto-encoder accuracy to that of other deep learning algorithms, such as ANN, CNN, and RBM, before implementing the suggested methodology and displaying the model's accuracy graphically after the suggested new methodology or algorithm for patients with rectal cancer. Various criteria, including true positive rate, ROC curve, and accuracy scores, are used in the experiments to determine the model's high accuracy. In the end, we determine the accuracy score for each model. The outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction models. It is shown that variational deep encoders have excellent accuracy of 94% in this cancer prediction and 95% for ROC curve regions. The findings demonstrate that automated prediction algorithms are capable of properly estimating rectal cancer patients’ chances of survival. The best results, with 95% accuracy, were generated by deep autoencoders.

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“…According to experimental consequences, the system attained 96.5% of CVR. Kaur et al [15], proposed a novel similarity measurebased random forest (NRF). Additionally, a dual-tree complex wavelet transform (D-TCWT) is used for feature extraction.…”

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confidence: 99%

Fingerprint recognition based on shark smell optimization and genetic algorithm

Ahmed

Abdulhameed

2020

Int. J. Adv. Intell. Informatics

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Fingerprint recognition is a dominant form of biometric due to its distinctiveness. The study aims to extract and select the best features of fingerprint images, and evaluate the strength of the Shark Smell Optimization (SSO) and Genetic Algorithm (GA) in the search space with a chosen set of metrics. The proposed model consists of seven phases namely, enrollment, image preprocessing by using weighted median filter, feature extraction by using SSO, weight generation by using Chebyshev polynomial first kind (CPFK), feature selection by using GA, creation of a user’s database, and matching features by using Euclidean distance (ED). The effectiveness of the proposed model’s algorithms and performance is evaluated on 150 real fingerprint images that were collected from university students by the ZKTeco scanner at Sulaimani city, Iraq. The system’s performance was measured by three renowned error rate metrics, namely, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Correct Verification Rate (CVR). The experimental outcome showed that the proposed fingerprint recognition model was exceedingly accurate recognition because of a low rate of both FAR and FRR, with a high CVR percentage gained which was 0.00, 0.00666, and 99.334%, respectively. This finding would be useful for improving biometric secure authentication based fingerprint. It is also possibly applied to other research topics such as fraud detection, e-payment, and other real-life applications authentication.

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Novel similarity measure-based random forest for fingerprint recognition using dual-tree complex wavelet transform and ring projection

Cited by 8 publications

References 27 publications

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Rectal Cancer Prediction and Performance Based on Intelligent Variational Autoencoders Machine Using Deep Learning on CDAS Dataset

Fingerprint recognition based on shark smell optimization and genetic algorithm

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