A Survey of Artificial Intelligence Techniques Used in Biometric Systems

Human resource management in defense is crucial for the efficiency, security, and readiness of military organizations. Emerging biometric recognition technologies and their application in defense HR management, facilitated by artificial intelligence (AI), play a pivotal role in advancing biometric identification systems. These systems incorporate technologies such as fingerprint recognition, facial recognition, iris scanning, and voice identification. When combined with AI-driven algorithms, they enhance the accuracy and efficiency of biometric data analysis and processing. This paper examines the mathematical algorithms underpinning the identification process, including support vector machines (SVM), deep neural networks (DNN), and biometric feature extraction methods. Special emphasis is placed on the challenges associated with implementing such technologies in the defense sector, encompassing ethical and legal considerations related to personal data protection.

The future of drug research is intrinsically connected to the continuous advancement and prudent integration of Artificial intelligence (AI) and mathematics. By addressing challenges and leveraging possibilities, the pharmaceutical industry may fully harness the potential of AI to develop innovative and effective treatments for diverse diseases. In our opinion, finding new drugs takes long time and funds, and in the past, it was mostly done manually. AI has changed this sector completely, making drug manufacturing faster, cheaper, and more specific. This review presents the pertinent literature on drug discovery utilizing mathematical modeling and AI tools and methodologies implemented at every stage of drug development to expedite the research process and mitigate risk and costs in clinical trials, it also presents that how mathematical modeling and AI algorithms can be used together at several stages of drug development. There is a lot of focus on how mathematical frameworks like Linear Algebra, optimization, statistical modeling, graph theory and differential equations may operate along with the techniques of AI like machine learning (ML), deep learning (DL), reinforcement learning (RL), natural language processing (NLP) and transfer learning (TL) and the problems that are now being faced, the tools and datasets that are accessible, and what the future holds for this field, which is changing quickly.

Biometrics based individual identification is observed as an effective technique for automatically knowing, with a high confidence a person's identity. Multi-modal biometric systems consolidate the evidence accessible by multiple biometric sources and normally better recognition performance associate to system based on a single biometric modality.Multi biometric systems are used to overcome this issue by providing multiple pieces of indication of the same identity. This system provides effective fusion structure that combines information provided by the multiple field experts based on decision-level and score-level fusion method, thereby increasing the efficiency which is not conceivable in uni-modal system.Multi-modal biometrics can be attained through a fusion of two or more images, where the subsequent fused image will be more protected. This paper discusses various fusion techniques, architecture of multi-modal biometric authentication and working of biometric fusion i.e. Iris and Fingerprint recognition that are used in multi-modal biometrics.

Nowadays, IT systems rely mainly on artificial intelligence (AI) algorithms to process data. AI is generally used to extract knowledge from stored information and, depending on the nature of data, it may be necessary to apply different AI algorithms. In this article, a novel perspective on the use of AI to ensure the cybersecurity through the study of network traffic is presented. This is done through the construction of a two-stage cyberattack classification ensemble model addressing class imbalance following a one-vs-rest (OvR) approach. With the growing trend of cyberattacks, it is essential to implement techniques that ensure legitimate access to information. To address this issue, this work proposes a network traffic classification system for different categories based on several AI techniques. In the first task, binary models are generated to clearly differentiate each type of traffic from the rest. With binary models generated, an ensemble model is developed in two phases, which allows the separation of legitimate and illegitimate traffic (phase 1) while also identifying the type of illegitimate traffic (phase 2). In this way, the proposed system allows a complete multiclass classification of network traffic. The estimation of global performance is done using a modern dataset (UNSW-NB15), evaluated using two approaches and compared with other state-of-art works. Our proposal, based on the construction of a two-step model, reaches an F1 of 0.912 for the first level of binary classification and 0.7754 for the multiclass classification. These results show that the proposed system outperforms other state-of-the-art approaches (+0.75% and +3.54% for binary and multiclass classification, respectively) in terms of F1, as demonstrated through comparison together with other relevant classification metrics.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Friction ridge impression from the areas of the fingers or hands that are not from the fingertips are called non-distal prints, which include palm prints, middle and proximal phalangeal prints, and interphalangeal prints. The presence of these fingerprints is less compared to distal print however, once retrieved from the scene of crime allow the identification of the individual who committed the crime. Therefore, this case report presents a burglary case where phalangeal prints from the finger retrieved from the site of crime matched the prints collected from the suspect. This case report also goes through advancements brought by Automated Biometric Identification System (ABIS) such as enhancing low-quality latent fingerprint, real-time picture and matching, and integration of other biometric systems such as Iris scan, Facial recognition.

Biometric systems based solely on one-modal biometrics are often not able to meet the desired performance requirements for large user population applications, due to problems such as noisy data, intra-class variations, restricted degrees of freedom, nonuniversity, spoof attacks, and unacceptable error rates. Multimodal biometrics refers to the use of a combination of two or more biometric modalities in a single identification system. The most compelling reason to combine different modalities is to improve the recognition accuracy. This can be done when features of different biometrics are statistically independent. This paper overviews and discusses the various scenarios that are possible in multimodal biometric systems using fingerprint, face and iris recognition, the levels of fusion that are possible and the integration strategies that can be adopted to fuse information and improve overall system accuracy. This paper also discusses how the image quality of fingerprint, face and iris used in the multimodal biometric systems affects the overall identification accuracy and the need of staffing for the secondary human validation. For a large user population identification system, which often has more than tens or hundreds of millions of subject images already enrolled in the matcher databases and has to process more than hundreds of thousands of identification requests, the system's identification accuracy and the need of staffing levels to properly operate the system are two of the most important factors in determining whether a system is properly designed and integrated

Background The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow. Objective The aim of this study is to test the performance of and physicians’ preferences regarding an AI algorithm during the evaluation of patients via teledermatology. Methods We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician’s diagnosis. Physicians also reported whether the AI algorithm was useful or not. Results The balanced (in-distribution) top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P=.049). Exposure to the AI algorithm results was considered useful in 11.8% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n=2) of cases. Algorithm performance was associated with patient skin type and image quality. Conclusions AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine. Conflicts of Interest None declared.

Traditional security systems use passwords or ID cards have been used to moderate access to restricted systems, but these kind of systems have a poor performance because the security can be easily breached. Based in this disadvantage, the biometrics systems have a great popularity, the mains biometrics systems are: face recognition, iris recognition, voice recognition, fingerprint recognition and sinning recognition. The fingerprint recognition is the oldest method used to recognition or verification of person. Our proposed a people recognition system with verification by invariant moments using two methodologies for the fingerprint enhancement. The goal in this work is to get a robust system in security issues. In this work a method for fingerprint verification is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters by image enhancement, both methods are first applied separately and later on an algebraic sum is done to obtain a single output. After that, a thinning algorithm is applied to get an image with the minimum thickness of 1 pixel. After this thinning algorithm, we apply an algorithm to look minutiae using a window of 3 by 3 pixels to scan the image. In this work we extract two types of minutiae, bifurcation and ending. Then, the feature vector is generated with the distance between minutiae, angle between minutiae and coordinates. In the recognition stage using the coordinates from the minutiae position on the image a comparison is done. After that, apply a verification stage using the invariant moments. With the invariant moments values a comparison is done. The comparison was done using the values obtained for the images into database and the test image for to get the output. The results obtained in this research are better when we used FFT and Gabor filters algorithms to image enhancement than we used separately.

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

Simple SummaryThe primary purpose of this review is to provide an in-depth analysis of existing Artificial Intelligence (AI) algorithms used in the field of prostate cancer (PC) for diagnosis and treatment. This review aims to show the research community that AI-enabled technologies have the potential for widespread growth and penetration of PC diagnostics and therapeutics to simplify and accelerate existing healthcare processes.As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

HADA: An automated tool for hardware dimensioning of AI applications

Artificial intelligence (AI) has become a significantly growing field in the environmental sector due to its ability to solve problems, make decisions, and recognize patterns. The significance of AI in wildlife acoustic monitoring is particularly important because of the vast amounts of data that are available in this field, which can be leveraged for computer vision and interpretation. Despite the increasing use of AI in wildlife ecology, its future in acoustic wildlife monitoring remains uncertain. To assess its potential and identify future needs, a scientific literature review was conducted on 54 works published between 2015 and March 2022. The results of the review showed a significant rise in the utilization of AI techniques in wildlife acoustic monitoring over this period, with birds (N = 26) gaining the most popularity, followed by mammals (N = 12). The most commonly used AI algorithm in this field was Convolutional Neural Network, which was found to be more accurate and beneficial than previous categorization methods in acoustic wildlife monitoring. This highlights the potential for AI to play a crucial role in advancing our understanding of wildlife populations and ecosystems. However, the results also show that there are still gaps in our understanding of the use of AI in wildlife acoustic monitoring. Further examination of previously used AI algorithms in bioacoustics research can help researchers better understand patterns and identify areas for improvement in autonomous wildlife monitoring. In conclusion, the use of AI in wildlife acoustic monitoring is a rapidly growing field with a lot of potential. While significant progress has been made in recent years, there is still much to be done to fully realize the potential of AI in this field. Further research is needed to better understand the limitations and opportunities of AI in wildlife acoustic monitoring, and to develop new algorithms that can improve the accuracy and usefulness of this technology.

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Biometric systems have been using widely and these systems have improved substantially which are being used for persons Authentication and verification. This Biometrics is playing a key role in Personal, National and Global security. The main approach of this paper is to tell the importance of Biometric systems. Different types of biometric systems have been used these days. These biometric systems are iris recognition, palm vein biometrics, voice recognition, face recognition. The main threat to all these Biometric systems is Spoofing. Facial recognition biometric system is being widely used when compared to other biometric systems. Different types of facial spoofing include mask attack; photo attack & video attack and Different methods used to detect Face spoofing are discussed in this paper. These face spoofing detection techniques include the use of LBP, DMD, SVM, PReLU, IDA, LFHOG and CNN. The Other Techniques Include a Combination of LBP, CNN, and IDA. However, the results strongly prove that the Implementation of the above techniques will detect Face spoofing.