A Novel Method for Validating Addresses Using String Distance Metrics

Address validation is vital since it confirms the quality and geographical precision of addresses used by organizations that rely on location-dependent and delivery-based services. Suppose addresses need to be thoroughly checked in advance. In that case, there may be difficulties with them, such as missing components or geographical defi-ciencies, which may lead to severe problems with logistics. When doing address valida-tion, discovering missing or incorrect address components is a beneficial aspect in mini-mizing the likelihood of service problems while saving time and money for organiza-tions. When it comes to addressing validation, using statistical metrics like correlation coefficients and measures of central tendency has been discovered to have a significant amount of untapped potential. In order to obtain a normalized score that is based on statistical similarities, the approach that is suggested in this study makes use of a mix-ture of several string-matching metrics. This score may then be used to exclude authen-ticated addresses based on the needed minimum level of similarity, which can be calcu-lated. Experiments have been carried out on a healthcare dataset taken from the actual world to show the efficacy of the suggested method in terms of accuracy and precision

PALATAL PATTERNS BASED RGB TECHNIQUE FOR PERSONAL IDENTIFICATION

Biometric system is an alternative way to the traditional identity verification methods. Thisresearch article provides an overview of recently / currently used single and multiple biometrics basedpersonal identification systems which are based on human physiological (such as fingerprint, handgeometry, head recognition, iris, retina, face recognition, DNA recognition, palm prints, heartbeat, fingerveins, footprints and palates) and behavioral (such as body language, facial expression, signatureverification and speech recognition) characteristic

EFFECTIVE ONLINE IRIS IMAGE REDUCTION AND RECOGNITION METHOD BASED ON EIGEN VALUES

The efficient Eigen values-based approach for online iris image compression and humanidentification, including the situation of identical twins, is introduced in this study. The iris image isretrieved after eliminating the pupil, eyebrow, skin, and other noise disturbances from an accuratepicture. The retrieved iris picture is partitioned into several blocks, each 16 by 16 pixels. Eigenvaluesare now generated for each block to identify each block better and save it in the smart card memory.Therefore, all that is required to determine whether two iris pictures are the same is to compare thestored Eigenvalues with the online-calculated Eigenvalues. The identical Eigenvalues between twoiris scans indicate they belong to the same individual. According to our study, various people's irisimages—including those of identical twins—have distinct Eigenvalues. We tested our Eigen ValuesBased Iris Image Identification Technique using datasets of iris images from CASIA and MultimediaUniversity, and we discovered that it provides 99.99% accuracy for matching identical twin andindividual photos. According to the implementation, our strategy seems to give the most extraordinarymatching results for identical twins and people. It is a practical, cost-effective, and effective methodfor online personal identification