This article delves into the power of multi-biometric fusion for individual identification. a new feature-level algorithm is proposed that is the Dis-Eigen algorithm. Here, a feature-fusion framework is proposed for attaining better accuracy when identifying individuals for multiple biometrics. The framework, therefore, underpins the new multi-biometric system as it guides multi-biometric fusion applications at the feature phase for identifying individuals. In this regard, the Face-fingerprints of 20 individuals represented by 160 images were used in this framework . Experimental resultants of the proposed approach show 93.70 % identification rate with feature-level fusion multi-biometric individual identification.
Background: Coronary heart disease refers to different condition of failing circulation of the heart and includes myocardial infarction (MI). Cardiac catheterization (CC) is the inserting of a thin, hollow catheter into a chamber or vessel; it is done for diagnostic and intervention purposes. Death charge from coronary heart disease have decreased in recent decennium, however coronary heart disease is still a major cause of morbidity and mortality worldwide especially in developed country. In this study, we assessed the patients' knowledge regarding CC. Materials and Methods: A descriptive study was conducted with a purposive sample of 250 patients were selected and included from Cardiac Specialty Hospital in Slemani City, Iraq. This study was carried out in between November 2017 and October 2018. A self-conductive questionnaire was used for data collection. Results: Totally 250 patients were included in this study. Among 250 patients, 176 (70.4%) were males and 74 (29.6%) females. The validity of questionnaire was estimated through a panel of experts related to the field of the study, and its reliability was determined through a pilot study which was carried out on 105 patients who were selected purposively from the patient were admitted those who were undergone the procedure at Cardiac Specialty Hospital in Slemani city. The majority of the participants were Kurdish 212 (84.8%) and more than a quarter of the patient's age was in group 60 years and above. Among 250 patients, 202 (80.8%) were married and 117 (46.8 %) of study participants were illiterate, 171 (68.4%) of them were unemployed, and 148 (59.2%) were lived in urban area. Conclusion: Our present study showed that the majority of participants had low level of knowledge regarding CC as well as level of knowledge from post-CC was higher than pre-CC procedure.
The study on twins is an important form of study in the forensic and biometrics field as twins share similar genetic traits. A biometric is one of the common types of pattern recognition which acquires biometric data from a person. From these data, a feature is established and extracted where these features can be used to identify individual. Exiting works in biometric identification concentrate on unimodal biometric identification. The high similarity in a pair of twin’s biometric may lead to miss performance. Hence, due to their great accurateness, multimodal biometric systems have become more favored than unimodal biometric systems in identical twins identification. However, these systems are highly complex. We proposed Mean-Discrete feature based fusion algorithm for Kurdish handwriting and fingerprint for identical twins detection. Its viability and advantage over the unimodal biometric systems are highlighted. This paper employed 800 images from 50 pairs of identical twins from Kurdistan Region to carry out the experiment.
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