Tensor Product of the Power Graphs of Some Finite Rings

Biometrics plays a pivotal role in enhancing security, ensuring accurate identification, and offering convenient solutions across diverse industries. Its uniqueness, reliability, and potential for future advancements establish it as a crucial and valuable field in today's digital landscape. Fingerprint authentication in ATMs presents primary advantages such as heightened security through distinctive identification and user convenience by eliminating the reliance on PINs or passwords. This research paper focuses on conducting a comprehensive review and comparative analysis of various approaches for fingerprint identification, aiming to contribute to the understanding of effective and efficient methods in the context of ATM authentication.

Section: Identification Methodsmentioning

confidence: 99%

The Role of Biometric in Banking: A Review

Marani,

Soltani,

Bahadori

et al. 2023

“…An illustration of the architecture of the proposed method is presented in Figure ??. The dataset used for experimental purposes was downloaded from the University of California Irvine (UCI) repository [29]. This dataset consists of 3772 samples, with 3481 samples belonging to the healthy group, 194 samples belonging to the hypothyroidism category, 95 samples belonging to the primary hypothyroidism category, and 2 cases belonging to the secondary hypothyroidism category.…”

Section: Proposed Methodsmentioning

confidence: 99%

Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network

Soleimani,

Forouzanfar,

Soltani

et al. 2023

Data classification in the real world is often faced with the challenge of data imbalance, where there is asignificant difference in the number of instances among different classes. Dealing with imbalanced data isrecognized as a challenging problem in data mining, as it involves identifying minority-class data with ahigh number of errors. Therefore, the selection of unique and appropriate features for classifying data withsmaller classes poses a fundamental challenge in this research. Nowadays, due to the widespread presenceof imbalanced medical data in many real-world problems, the processing of such data has gained attentionfrom researchers. The objective of this research is to propose a method for classifying imbalanced medicaldata. In this paper, the hypothyroidism dataset from the UCI repository is used. In the feature selection stage,a support vector machine algorithm is used as a cost function, and the wrapper algorithm is employed asa search strategy to achieve an optimal subset of features. The proposed method achieves high accuracy,reaching 99.6% accuracy for data classification through the optimization of a neural network using learningautomata.

“…By representing the data instances as nodes and their relationships as edges, graph-based feature extraction [28] methods can capture the structural information and connectivity patterns in the data. These extracted features can be used to enhance the classification performance on imbalanced data [29].…”

Section: Proposed Methodsmentioning

confidence: 99%

Multi-class Classification of Imbalanced Intelligent Data using Deep Neural Network

Soleimani,

Mirshahzadeh

2023

In recent years, studies in the field of deep learning have made significant progress. These studies have focusedmore on datasets with balanced classification, and less research has been done on imbalanced datasets, whichare of great importance in the real world and present significant challenges for classification. This articlestudies the problem of classifying imbalanced data, introduces dynamic sampling for deep neural networks,investigates the imbalanced multiclass problem, and proposes a dynamic sampling method for deep learning.In our proposed method, all samples are fed to the current deep neural network for each training iteration,and the accuracy, precision, and mean error of the deep neural network are estimated. The proposed methoddynamically selects informative data for training the deep neural network. Comprehensive experiments wereconducted to evaluate and understand its strengths and weaknesses. The results of 13 imbalanced multiclassdatasets show that the proposed method outperforms other methods, such as initial sampling techniques,active learning, cost-sensitive learning, and reinforcement learning.