A Comprehensive Overview of Quality Enhancement Approach-Based Biometric Fusion System Using Artificial Intelligence Techniques

Jindal, Gaurav; Kaur, Gaganpreet

doi:10.1007/978-981-16-1089-9_8

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Quality improvement programmes with a clinical, financial, or operational focus can all have a substantial influence on the overall cost of treatment, clinical outcomes, variation in care, decision support, length of stay, and other issues [ 4 , 5 ]. Clinical variability, unnecessary medical mistakes, hospital-acquired infections, patient discharge delays, and diminishing cash flow are just a few of the pressing concerns confronting hospital systems across the country [ 6 , 7 ]. With the advancement of science, information communication infrastructure and computer technology lead to a focus on more accurate diagnosis and prediction methods so that a promise to get good and healthy life in the future can be done [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition

Gangurde

Jagota

Khan

et al. 2023

BioMed Research International

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The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model’s accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition

Gangurde

Jagota

Khan

et al. 2023

BioMed Research International

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“…RBM modeling technique uses probability to make predictions about an unsupervised model [29,30]. The construction of an RBM model is shown in Fig.…”

Section: B Proposed Methodology Implementationmentioning

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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“…Of these, to the best of our knowledge, none have proposed a technique similar to our proposal ( Section 4 ). We have found two main related approaches, both based on Fusion of features [ 24 ]: At the score level, using scores of different classifiers [ 32 , 33 ]. At decision-level, with the voting scheme being the most frequently used [ 18 , 19 , 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…This proposal is based on several previous ideas: Fusion of features that can be implemented at distinct levels and used in biometrics to improve accuracy [ 24 ]. Here, we use score-level fusion.…”

Section: Introductionmentioning

confidence: 99%

A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices

Salvador-Ortega

Vivaracho-Pascual

Simon-Hurtado

2023

Sensors

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In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system.

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A Comprehensive Overview of Quality Enhancement Approach-Based Biometric Fusion System Using Artificial Intelligence Techniques

Cited by 7 publications

References 20 publications

[Retracted] Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition

[Retracted] Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition

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

A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices

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