This study focus on minutiae based fingerprint template protection. In this research, the reaearcher propose three minutiae-based fingerprint template protection methods,namely Random Triangle Hashing (RTH),Minutiae Pair based Indexing and Binarization (MPIB) and Polar Grid based 3-Tuple Quantization (PGTQ).

Fingerprint Template Protection With Minutiae Based Bit String

Cryptography and Tay-Grey wolf optimization based multimodal biometrics for effective security

: The human blood is a health indicator; it delivers necessary substances such as oxygen and substance that provides nourishment is necessary. Hence, segmentation of blood cells and identification of blood type is very important. The human blood consists of the RBCs, WBCs, Platelets and Plasma. Presently, lab technicians’ tests blood groups manually and they use a device called Hemocytometer and microscope to count blood cells. But this method is extremely time consuming, monotonous and leads to the inaccurate results due to human errors. To overcome the problems regarding time, accuracy and cost, a method is proposed based on processing of images acquired from laboratory. The image processing techniques such as Segmentation, Morphological operations and Circular Hough Transform will test with some samples. Fast and reliable desktop application platform for blood image analysis and malaria diagnosis from Giemsa-stained thin blood film images. The application is based on novel Annular Ring Ratio Method which will test and validate in IMAGE PROCESSING TOOL. The method detects the blood components such as the Red Blood Cells (RBCs), White Blood Cells (WBCs), and identifies the parasites in the infected RBCs. The application also recognizes the different life stages of the parasites and calculates the parasitemia which is a measure of the extent of infection.

An Application of Machine Learning to Detect Blood Diseases

Diabetes mellitus is a clinically and genetically heterogeneous group of disorders that can be distinguished by high blood glucose levels caused by either insulin deficiency or the body's resilience to the action of insulin. Diabetes detection at an early stage is critical for preventing damage to internal organs and the immune system. Most of the applications focus on optimizing the model based on accuracy rather than recall; a good recall score is required because it tells us how accurately the relevant class is being predicted, and a wrong prediction would lead to undiagnosed treatment. This paper proposes the most optimal prediction model to diagnose diabetes. The dataset from BRFSS includes a person's health indicators, which possess key features for us to analyze the effectiveness of different machine learning algorithms and observe the best performing model.

Enhancing Diabetes Detection using Machine Learning: A Focus on Optimizing Recall Performance

Diabetes among one of the most common diseases occurs in human beings due to imbalance of insulin level in blood. The early detection of diabetes is very necessary as it can affect many internal parts and immune system of human body silently. In this paper, we are comparing various machine learning and neural network based approaches that are applied on publically available datasets. Here, we have used two datasets for experiments 1st dataset is UCI dataset and other is PIMA Indian dataset then we have performed lots of experiments using different machine learning classifiers and neural network models to observe the performance of each classifier. After experiments, the highest accuracy of identification obtained from decision tree method which is 99.8% for dataset1 and for dataset 2 the highest accuracy was obtained from back propagation neural network model which is 80.8 %.

Identification of Diabetes Disease from Human Blood Using Machine Learning Techniques

Biometric recognition system plays a vital role for identifying the person automatically using the characteristic vector from its behavioral or physiological characteristics In recent days, recognition system based on vein image is a facilitating and promising biometric technology Vein recognition uses various modalities, like hand, palm and finger images for the identification of person This article introduces the model for vein-based biometric recognition using the rider- based score level fusion method The developed method includes four steps, which are pre-processing, vein extraction, feature extraction and biometric recognition The first step is to input the training images, like finger, hand, and palm into pre-processing step In the pre-processing level, noises are removed from the input image and the Region of Interest (ROI) extraction is also executed in pre-processing module based on neighbourhood search method After that, veins are extracted for each modality using the holoentropy, enlightening of image, and the circular averaging filter After vein extraction, the features are acquired, and then, Entropy- based Euclidean Distance (EED) is developed for score level fusion At last, the biometric identification is carried out based on the proposed Fuzzy Rider Optimization Algorithm (Fuzzy ROA) The proposed Fuzzy ROA is newly designed by combining the Fuzzy logic and the Rider Optimization Algorithm (ROA) The performance metrics, such as False Rejection Ratio (FRR), False Acceptance Ratio (FAR) and accuracy is analyzed, and then compared with other methods The evaluation of the developed method attains the maximum accuracy of 9019%, minimum FAR and FRR of 1009% and 087% respectively

Fuzzy Rider Optimization Algorithm for vein-based biometric recognition

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Identification of Diabetes Disease from Human Blood Using Machine Learning Techniques

Fuzzy Rider Optimization Algorithm for vein-based biometric recognition