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The frustrated total internal reflection (FTIR) optical fingerprint scanning method is widely used due to its cost-effectiveness. However, fingerprint image quality is highly dependent on fingertip surface conditions, with moisture generally considered a degrading factor. Interestingly, a prior study reported that excessive moisture may improve image quality, though their findings were based on qualitative observations, necessitating further quantitative analysis. Additionally, since the FTIR method relies on optical principles, image quality is also influenced by the wavelength of the light source. In this study, we conducted a preliminary clinical experiment to quantitatively analyze the impact of moisture levels on fingertips (wet, dry, and control) and light wavelengths (red, green, and blue) on FTIR fingerprint image quality. A total of 20 male and female participants with no physical impairments were involved. The results suggest that FTIR fingerprint image quality may improve under wet conditions and when illuminated with green and blue light sources compared to dry conditions and red light. Statistical evidence supports this consistent trend. However, given the limited sample size, the statistical validity and generalizability of these findings should be interpreted with caution. These insights provide a basis for optimizing fingerprint imaging conditions, potentially enhancing the reliability and accuracy of automatic fingerprint identification systems (AFIS) by reducing variations in individual fingerprint quality. [ABSTRACT FROM AUTHOR]

Owing to its capability to offer remote services, the Internet of Things (IoT) has immersed itself in all areas of our daily lives. However, this big use of IoT networks makes the user's data change insecurely in open channels vulnerable to malicious use. As a result, the security of the user's data in an IoT environment becomes a critical issue. Given that authentication is a mechanism that may prevent hackers from retrieving and exploiting data communicated between IoT devices, researchers have proposed many lightweight IoT authentication schemes in the last decades. However, most of these schemes are based on two authentication factors and are unable to ensure unlink ability, key secrecy, perfect forward secrecy, and resistance to node capture, denial of service (DoS) attacks, stolen verifiers, denning-SSACO attacks, and GWN bypassing. In this paper, we present an anonymous three-factor authentication scheme based on elliptic curve cryptography (ECC), which can provide all security services and resist well-known attacks. Then, based on informal security analysis and the formal security proof using ProVerif we show that our provided scheme is secure and can resist known attacks. Finally, we show the comparison result among our protocol and other protocols in terms of computation overheads, communication overheads, and security features. [ABSTRACT FROM AUTHOR]

This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system's performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications. [ABSTRACT FROM AUTHOR]

Background: Student wellness is of increasing concern in medical education. Increased rates of burnout, sleep disturbances, and psychological concerns in medical students are well documented. These concerns lead to impacts on current educational goals and may set students on a path for long-term health consequences. Methods: Undergraduate medical students were recruited to participate in a novel longitudinal wellness tracking project. This project utilized validated wellness surveys to assess emotional health, sleep health, and burnout at multiple timepoints. Biometric information was collected from participant Fitbit devices that tracked longitudinal sleep patterns. Results: Eighty-one students from three cohorts were assessed during the first semester of their M1 preclinical curriculum. Biometric data showed that nearly 30% of the students had frequent short sleep episodes (<6 hours of sleep for at least 30% of recorded days), and nearly 68% of students had at least one episode of three or more consecutive days of short sleep. Students that had consecutive short sleep episodes had higher rates of stress (8.3%) and depression (5.4%) symptoms and decreased academic efficiency (1.72%). Conclusions: Biometric data were shown to significantly predict psychological health and academic experiences in medical students. Biometrically assessed sleep is poor in medical students, and consecutive days of short sleep duration are particularly impactful as it relates to other measures of wellness. Longitudinal, biometric data tracking is feasible and can provide students the ability to self-monitor health behaviors and allow for low-intensity health interventions. [ABSTRACT FROM AUTHOR]

Biometric systems use the unique behavioral or physical characteristics of a user to verify their claimed identity. Due to the high probability of forgery or theft of traditional passwords and keys, there is a decreasing tendency to use them in security systems. By using biometric indicators, it becomes impossible to forge or steal them. Electroencephalogram (EEG) signals meet the basic requirements of biometric indicators, making them suitable for use in authentication and crypto-biometric systems. In this paper, the first step involves extracting features from recorded EEG signals using transformer-based models within an identification system. In the second step, the extracted features are imported into a key generation system. The proposed method maps the features of each user to different segments. The distributions of the segment indexes are then used to generate repeatable keys from EEG features in future sessions. The Transformer-based identification system achieved a mean accuracy of 99.8%, and the key generation system achieved a 0.1% mean Half Total Error Rate (HTER) using five different categories of visual stimulus. The high accuracy of the proposed identification system and the low error rate of the proposed key generation system indicate that features extracted by the Transformers are a good choice for visual stimulus EEG-based biometric systems. [ABSTRACT FROM AUTHOR]

The representatives of Prunus cerasifera are the most numerous and diverse in vegetative and reproductive indicators, although valued by the local population in the sub-mountainous and mountainous regions Minkov and Mihova, 2021; Todorova and Marinova, 2024). Despite their wide distribution, this species is poorly studied. Expeditions were conducted during the period 2020-2023, in the region of the Central Stara Planina, with the aim of studying and selecting valuable plant genetic resources of cherry plum (Prunus cerasifera Ehrh) without pit and with a detachable stone. Two red-fruited genotypes of the Karlovska afozka type, 1 red-leaved, and 2 yellow large-fruited genotypes with valuable economic qualities, distinguished by high fertility and simultaneous ripening of the fruits, were selected, marked, and studied. The years of study are characterized by abundant precipitation in spring and drought during the period (July-October). The genotype Yellow 46 Troyanska stands out with the highest weight (46.74±0.35g), followed by Red Troyan Leaf (34.16g±0.48g). Regarding the biochemical composition, the highest amount of dry matter was recorded in Red Karlovska - 17.05%, and the highest value of polyphenols was registered in Red Troyan Leaf (143.27mg/g). The fruits of Red Troyan Leaf and Yellow 46 Troyanska have the most attractive appearance. [ABSTRACT FROM AUTHOR]

Iris is one of the most widely used biometric modalities because of its uniqueness, high matching performance, and inherently secure nature. Iris segmentation is an essential preliminary step for iris-based biometric authentication. The authentication accuracy is directly connected with the iris segmentation accuracy. In the last few years, deep-learning-based iris segmentation methodologies have increasingly been adopted because of their ability to handle challenging segmentation tasks and their advantages over traditional segmentation techniques. However, the biggest challenge to the biometric community is the scarcity of open-source resources for adoption for application and reproducibility. This review provides a comprehensive examination of available open-source iris segmentation resources, including datasets, algorithms, and tools. In the process, we designed three U-Net and U-Net++ architecture-influenced segmentation algorithms as standard benchmarks, trained them on a large composite dataset (>45K samples), and created 1K manually segmented ground truth masks. Overall, eleven state-of-the-art algorithms were benchmarked against five datasets encompassing multiple sensors, environmental conditions, demography, and illumination. This assessment highlights the strengths, limitations, and practical implications of each method and identifies gaps that future studies should address to improve segmentation accuracy and robustness. To foster future research, all resources developed during this work would be made publicly available. [ABSTRACT FROM AUTHOR]

This paper investigates the use of continuous morse wavelet transform (CWT) coefficients as inputs to convolutional neural networks (CNNs) for electrocardiogram (ECG) biometric authentication. We evaluate the performance and generalization of pre-trained SqueezeNet architecture using the ECG-ID Database. Our approach involves extracting 10 scalograms from each subject's ECG signals and employing gradient descent optimization during training. The models demonstrate high accuracy, achieving over 90% on both training and validation datasets, indicating robust performance and minimal overfitting. Further analysis using the F1 confidence curve and ROC curve reveals a balanced trade-off between precision and recall, with an optimal F1 score of 0.84 and an AUC of 0.84, respectively. Additionally, we explore the impact of different CWT parameter settings, including Voice per Octave (VPO), symmetry parameter (gamma), and time-bandwidth product (P2). The optimal VPO of 41 yields an AUC of 0.87 and an F1 score of 0.84. The best performance is achieved with gamma values greater than 2 and time-bandwidth products between 45 and 80, enhancing time localization and frequency resolution. In this study, the significance of fine-tuning wavelet parameters to improve the effectiveness of ECG biometric systems is demonstrated, demonstrating the potential of combining CWT and CNNs for reliable biometric authentication. [ABSTRACT FROM AUTHOR]

Next-generation identity verification using biometric features is nearly foolproof with the right classifier. However, selecting the correct classifier poses a key challenge, particularly in the recognition of biometric patterns. High-potential projects may face delays due to a lack of the right recognition mechanism or the malfunction of the selected classifier. This could also result from not choosing the appropriate classifier that aligns with the project's patterns. This study aims to evaluate various classifiers with potential in biometric research and the capabilities of different machine learning algorithms. Several classifiers were experimentally evaluated in combination with dynamic algorithms. The ultimate objective was to identify a standard classifier suitable for general biometric pattern recognition. Using well-known biometric pattern datasets, multivariate algorithms, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were applied. These methods were combined with different classifiers, including SVM-L, MLP, KNN, etc. After analyzing the results obtained, the combination of LDA with MLP outperformed other approaches in terms of accuracy. [ABSTRACT FROM AUTHOR]

Research on multi‐biometric authentication systems using multiple biometric modalities to defend against adversarial attacks is actively being pursued. These systems authenticate users by combining two or more biometric modalities using score or feature‐level fusion. However, research on adversarial attacks and defences against each biometric modality within these authentication systems has not been actively conducted. In this study, we constructed a multi‐biometric authentication system using fingerprint, palmprint, and iris information from CASIA‐BIT by employing score and feature‐level fusion. We verified the system's vulnerability by deploying adversarial attacks on single and multiple biometric modalities based on the FGSM, with epsilon values ranging from 0 to 0.5. The experimental results show that when the epsilon value is 0.5, the accuracy of the multi‐biometric authentication system against adversarial attacks on the palmprint and iris information decreases from 0.995 to 0.018 and 0.003, respectively, and the f1‐score decreases from 0.995 to 0.007 and 0.000, respectively, demonstrating susceptibility to adversarial attacks. In the case of fingerprint data, however, the accuracy and f1‐score decreased from 0.995 to 0.731 and from 0.995 to 0.741, respectively, indicating resilience against adversarial attacks. [ABSTRACT FROM AUTHOR]

With the rapid growth of the Internet of Things (IoT), the Internet of Medical Things (IoMT) has emerged as a critical sector that enhances convenience and plays a vital role in saving lives. IoMT devices facilitate remote access and control of various medical tools, significantly improving accessibility in the healthcare field. However, the connectivity of these devices to the internet makes them vulnerable to adversarial attacks. Safeguarding medical data becomes a paramount concern, particularly when precise biometric readings are required without compromising patient safety. This paper proposes a fast encryption mechanism to protect the color information in medical videos utilized within the IoMT environment. Our approach involves scrambling medical video frames using a rapid block-splitting method combined with simple operations. Subsequently, the scrambled frames are encrypted using different keys generated from the logistic map. To ensure the practicality of our proposed method in the IoMT setting, we implement the encryption mechanism on a cost-effective Raspberry Pi platform. To evaluate the effectiveness of our proposed mechanism, we conduct comprehensive simulations and security analyses. Notably, we investigate medical test videos during the evaluation process, further validating the applicability of our method. The results confirm our proposed mechanism's robustness by hiding patterns in original videos, achieving high entropy to increase randomness in encrypted videos, reducing the correlation between adjacent pixels in encrypted videos, and resisting various attacks. [ABSTRACT FROM AUTHOR]

A Brain–Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset. [ABSTRACT FROM AUTHOR]

This study aimed to critically explore the loopholes in Branchless Banking in Pakistan. This study was qualitative, and a total of 16 cyber investigators were interviewed face-to-face from the district Faisalabad. Collected data were analyzed using the content analysis method. Results of this study are presented under different key themes, i.e. (i) serious violations of Know Your Customer (KYC) & Customer Due Diligence (CDD), (ii) BVS (Biometric Verification System) Failure followed by some sub-themes. This study unveiled that the security of internet-based financial services is a great concern. Whereas violations of KYC and CDD reflect various vulnerabilities that augment financial crimes. Ineffective use of biometric verification systems, poorly maintained retailer records, and the operation of unauthorized individuals aggravate the risks of financial crimes. There is a need for a realtime monitoring system of transactions, a strict BVS, and an awareness campaign for public on safe use of branchless banking. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Institute of Science & Technology / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi is the property of Igdir University, Institute of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the profession of policing, the accumulation of stressful incidents over the course of a career can lead to a host of adverse health outcomes: increased incidence of injury and illness, diminished cognitive performance, mental health impacts (including anxiety, depression, addiction and elevated risk of suicide), increased risk of cardiovascular disease and early mortality. The toxic climate of dysfunctional agency culture, local community resistance and distrust, and the national political discourse around policing all serve to increase the stress that first responders bear, contributing to erosion of police-community relationships. Beyond Us & Them partnered with California State University San Marcos to offer the Peace Officer Wellness, Empathy & Resilience (POWER) training to university police officers. POWER is a nationally certified 12-week training program, which teaches skills and practices that promote well-being, mindfulness and relationality, and improve police-community relations. Based on survey data from prior cohorts, we realized the potential benefit of adding biometric measurements to look for improvement in autonomic health. Other studies have demonstrated an inverse correlation between heart rate variability (HRV) and cardiovascular disease, cognitive decline and risk of allcause mortality. Of the 17 participants, 15 completed preand post-intervention surveys, and HRV was obtained from 13 of these participants: findings demonstrated improved autonomic health, as well as statistically significant changes in empathy, mindfulness and social connection. Additionally, we noted increased HRV coherence, which may be a physiologic marker of enhanced social connection. Future studies offer the possibility of utilizing HRV coherence as a marker of group connection and performance. [ABSTRACT FROM AUTHOR]

Background: To compare intraocular pressure (IOP) and anterior segment parameters between eyes with unilateral primary angle closure glaucoma (PACG) and their fellow eyes with primary angle closure (PAC) or primary angle closure suspect (PACS). Methods: Subjects underwent anterior segment imaging using 360‐degree swept‐source optical coherence tomography (SS‐OCT, CASIA Tomey, Nagoya, Japan) and ocular investigations including gonioscopy and IOP measurement. Each SS‐OCT scan (divided into 8 frames, 22.5 degrees apart) was analysed and an average was obtained for the following anterior segment parameters: iridotrabecular contact (ITC), angle opening distance (AOD750), iris thickness and curvature, anterior chamber width, depth and area (ACW, ACD and ACA) and lens vault (LV). Results: Among 132 unilateral PACG subjects (mean age: 62.91 ± 7.2 years; 59.1% male), eyes with PACG had significantly higher presenting IOP (24.81 ± 0.94 vs. 18.43 ± 0.57 mmHg, p < 0.001), smaller gonioscopic Shaffer grade (2.07 ± 0.07 vs. 2.31 ± 0.07, p < 0.001) and a greater extent of peripheral anterior synechiae (PAS, 1.21 ± 0.21 vs. 0.54 ± 0.16 clock hours, p = 0.001). PACG eyes also exhibited increased ITC, ITC area, greater LV and smaller AOD750, ACD and ACA (all p < 0.05). Using the forward stepwise regression model, an increase in 1 mmHg in presenting IOP before laser peripheral iridotomy (LPI) increases the odds of having PACG by 9% (95% confidence interval 5%–14%). Conclusions: PACG eyes have higher presenting IOP, smaller anterior segment parameters, greater extent of PAS, and larger LV compared to their fellow eyes with angle closure. Narrower anterior chamber dimensions and higher presenting IOP before LPI may increase risk of chronic elevated IOP and glaucomatous optic neuropathy after LPI. [ABSTRACT FROM AUTHOR]

A system that recognizes the iris is susceptible to presentation attacks (PAs), in which a malicious party shows artefacts such as printed eyeballs, patterned contact lenses, or cosmetics to obscure their personal identity or manipulate someone else's identity. In this study, we suggest the dual channel DenseNet presentation attack detection (DC-DenseNetPAD), an iris PA detector based on convolutional neural network architecture that is dependable and effective and is known as DenseNet. It displays generalizability across PA datasets, sensors, and artifacts. The efficiency of the suggested iris PA detection technique has been supported by tests performed on a popular dataset which is openly accessible (LivDet-2017 and LivDet-2015). The proposed technique outperforms state-of-the-art techniques with a true detection rate of 99.16% on LivDet-2017 and 98.40% on LivDet-2015. It is an improvement over the existing techniques using the LivDet-2017 dataset. We employ Grad-CAM as well as t-SNE plots to visualize intermediate feature distributions and fixat. [ABSTRACT FROM AUTHOR]

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people's security and promote their well-being. [ABSTRACT FROM AUTHOR]

Fingerprint and iris traits are used in sensitive applications and so, spoofing them can impose a serious security threat as well as financial damages. Spoofing is a process of breaking biometric security using artificial biometric traits. This spoofing can be avoided by detecting the liveness of the biometric traits. Hence, liveness detection techniques have become an active research area. However, liveness detection techniques are also prone to attack because of advanced spoofing materials. Hence, they are subjected to further development to face futuristic spoofing and compromising real biometric traits. To aid the development, this paper technically and informatically reviews the state-of-the-art liveness detection techniques in the last decade. Firstly, the paper reviews the processing strata, adopted features and detectors in the existing liveness detection techniques. Secondly, the paper presents the benchmark datasets, their characteristics, availability and accessibility, along with the potential spoofing materials that have been reported in the literature under study. Thirdly, the survey reports the performance of the techniques on the benchmark datasets. Eventually, this paper summarizes the findings, gaps and limitations to facilitate strengthening of liveness detection techniques. This paper further reports that the Fingerprint Liveness Detection (FLD) techniques such as Slim-ResCNN, JLW and Jung CNN have achieved a better accuracy of 94.30%, 98.61% and 97.99%, respectively on LivDet19 datasets. It has been observed that CNN-based architectures have outperformed in significant number of FLD datasets. In contrast, Support Vector Machine (SVM) with appropriate shallow and deep features has achieved equivalent performance against deep classifiers on detecting iris spoofs from Iris Liveness Detection (ILD) datasets. [ABSTRACT FROM AUTHOR]

The blockchain has continued to showcase myriads of promises as a breakthrough tech and highly adapted in manifold sectors. Its adoption is accompanied by a range of issues that make implementation complicated. To aid successful implementation, a variety of frameworks have been developed. But, selecting the appropriate framework based on the conformity of its features with the financial sector is a challenge for decision-makers. This study seeks to provide solution to the challenge of synchronizing entire blockchain in a minimum possible time, and create a Blockchain Technology Framework for pricing and Evaluating Financial Markets and also simulate the cryptocurrency market data using a neural network based application to generate the optimal prediction and predicting the future of investment by fusing the facial recognition system with the framework. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively. Design/methodology/approach: A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed. Findings: The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities. Research limitations/implications: Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved. Originality/value: To the best of the author's knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction. [ABSTRACT FROM AUTHOR]

The palmprint recognition model has been attracting researchers in the last few years. Among the mage concentrations, most of the researchers focused on local feature extraction model and recognition model, or a few focused on global feature extraction model for classification purposes. In this model, the main focus was on fusion of both the local and global features of palmprint image. Various traditional methods were used to extract both the global and local features of palmprint images. The feature selection algorithm selects the optimal features from a list to include in fusion. Hausdorff distance method is used for matching purpose. For this model, two different datasets (IITD and Tongji palmprint) were used. The accuracy of the dataset was 98.91% and 98.25%, respectively. Both datasets are contactless. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) is the property of Dinasti Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Different kinds of occlusion have proven to disrupt the accuracy of face recognition systems, one of them being masks. The problem of masked faces has become even more apparent with the widespread of the COVID-19 virus, with most people wearing masks in public. This brings up the issue of existing face recognition systems been able to accurately recognize people even when part of their face and the major identifiers (such as the nose and mouth) are covered by a facemask. In addition, most of the databases that have been curated in different organizations, countries are majorly of non-masked faces, and masked databases are rarely stored or universally accepted compared with conventional face datasets. Therefore, this paper aim at the development of a Masked Face Recognition System using facial anthropometrics technique (FAT). FAT is the science of calculating the measurements, proportion and dimension of human face and their features. A dataset of faces with individual wearing medical face mask was curated. Using the Facial anthropometry based technique a Masked Face Recognition System developed. This system was implemented using Local Binary Patterns Histogram algorithms for recognition. On testing the developed system trained with unmasked dataset, show a high recognition performance of 94% and 96.8% for masked and non-masked face recognition respectively because of the Facial anthropometry based technique adapted. On deployment, users were been recognized when they are wearing a mask with part of their face covered in real-time. [ABSTRACT FROM AUTHOR]

In recent years, face biometrics recognition systems are a wide space of a computer usage which is mostly employed for security purpose. The main purpose of the face biometrics recognition system is to authenticate a user from a given database. Due to the widespread expansion of the surveillance cameras and facial recognition technology, a robust face recognition system required. The recognition system needs to store a large number of training samples in any storage unit, that time hackers can access and control that data. So, Protecting and managing sensitive data is essential object. This requires a technique that preserve the privacy of individuals, maintain data integrity, and prevent information leakage. The storage of biometric templates on centralized servers has been associated with potential privacy risks. To address this issue, we have developed and implemented a proof-of-concept facial biometric identification system that uses a private Blockchain platform and smart contract technology. So, the proposed approach is presented a secure and tamper-proof from data breaches as well as hacks with data availability, by using the Blockchain platform to store face images. This paper aims to utilize Blockchain technology to identify individuals based on their biometric traits, specifically facial recognition system makes it tamper-proof (immutable) ensuring security. The system consists of enrolment and authentication phases. Blockchain technology uses peer-to-peer communication, cryptography, consensus processes, and smart contracts to ensure the security. The proposed approach was tested on two popular datasets: CelebFaces Attributes (CelebA) and large-scale face UTKFace datasets. The experimental results indicate that the system yields highly performance outcomes, as evidenced by the Equal Error Rate (EER) values of 0.05% and 0.07% obtained for the CelebA and UTKFace datasets, respectively. The system was compared to three baseline methods and scored the lowest Equal Error Rate. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency. [ABSTRACT FROM AUTHOR]

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment. [ABSTRACT FROM AUTHOR]

Gait recognition is one of the most promising video-based biometric technologies. The edge of silhouettes and motion are the most informative feature and previous studies have explored them separately and achieved notable results. However, due to occlusions and variations in viewing angles, their gait recognition performance is often affected by the predefined spatial segmentation strategy. Moreover, traditional temporal pooling usually neglects distinctive temporal information in gait. To address the aforementioned issues, we propose a novel gait recognition framework, denoted as GaitASMS, which can effectively extract the adaptive structured spatial representations and naturally aggregate the multi-scale temporal information. The Adaptive Structured Representation Extraction Module (ASRE) separates the edge of silhouettes by using the adaptive edge mask and maximizes the representation in semantic latent space. Moreover, the Multi-Scale Temporal Aggregation Module (MSTA) achieves effective modeling of long-short-range temporal information by temporally aggregated structure. Furthermore, we propose a new data augmentation, denoted random mask, to enrich the sample space of long-term occlusion and enhance the generalization of the model. Extensive experiments conducted on two datasets demonstrate the competitive advantage of proposed method, especially in complex scenes, i.e., BG and CL. On the CASIA-B dataset, GaitASMS achieves the average accuracy of 93.5% and outperforms the baseline on rank-1 accuracies by 3.4% and 6.3%, respectively, in BG and CL. The ablation experiments demonstrate the effectiveness of ASRE and MSTA. The source code is available at https://github.com/YanSun-github/GaitASMS. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims at developing an intelligent face recognition students' attendance system to enhance school attendance tracking. The system is made up of four phases-database of students' details, face detection, face recognition and attendance report. The database stores all the students' details and images of the students' face captured while face detection and recognition is carried out with convolutional neural network algorithms from the face recognition and opencv library. As faces are detected and recognized from live streaming video of the classroom, attendance are recorded into an excel file and then sent to a real time database. The system also contains a mobile interface through which the course instructors can access information at all time. The methodology adopted for this work is object-oriented analysis and design methodology (OOADM) while programming language used is Python. This work has helped immensely to eliminate the issue of proxy attendance as well as reduce the time wasted in manual attendance system. It is very beneficial to schools and other institutions where attendance is required. [ABSTRACT FROM AUTHOR]

This research paper illustrates about how to manage attendance using a new method that combines face detection techniques together with Python programming as well as machine learning methods among others; which are CNN models. The paper gives details on data collection methods as well as pre-processing methods other than explaining the architecture of CNN. According to the results obtained from its experiments it could be concluded that this system is capable of accurately identifying individuals and recording their presence or absence thus may contribute to increasing productivity in schools, businesses, or other organisations. [ABSTRACT FROM AUTHOR]

The article discusses the importance of rehabilitation therapy in treating somatosensory disorders (SD) that can arise from conditions such as strokes, spinal cord injuries, and diabetic neuropathy. It highlights various research studies exploring rehabilitative interventions, prognostic forecasting, and innovative methodologies for assessing and treating SD. The findings emphasize the need for interdisciplinary collaboration and the development of precise, objective assessments and treatments. Another article explores the relationship between somatosensory impairment and motor recovery in stroke patients, suggesting that while somatosensory impairment may impact motor recovery, it is not a strict requirement for improvement. This research provides valuable insights into the rehabilitation process for stroke patients. [Extracted from the article]

In recent years, security systems based on biometric features have become a promising solution to identify humans, and it is preferred over traditional methods working based on what we know. With the rapid growth of such identification methods, ECG authentication approaches as an emerging biometric recognition scheme is developed. It can be efficiently used to identify individuals, specifically for continuous authentication to allow particular access privileges for users. In comparison with other biometric features even in abnormal conditions, it gives more valid and better results. Although there are several works that have offered some techniques in order to overcome the various issues affecting the ECG authentication schemes' outputs, there are still many concerns to be considered. How can we see, there are not many studies that deal with all aspects of ECG authentication techniques? The objective this paper is to evaluate some surveys related to ECG authentication domain since 2010. We have done a comprehensive taxonomy including existing methods and techniques in ECG based authentication domain. With the aim of providing a classical taxonomy form, this study presents a Systematic Literature Review (SLR) on ECG-based authentication schemes to introduce the state-of-the-art approaches in this domain. We have done the selection of journals and conference proceedings using the standard systematic literature review methodology in order to find and assess the studies related to ECG authentication. Finally, the paper is concluded with a summary of the content of the paper, and open issues and future research challenges are discussed. [ABSTRACT FROM AUTHOR]

The impact of air quality inside residential buildings and constructions on the health of residents and workers has been studied very little in recent years. In the school environment, students are constantly exposed to mixtures of airborne substances from a wide variety of sources both in classrooms and in the school environment. Exposure to such factors of students influences, in the long term, physiological development, dynamics, quality of life, life expectancy, without being aware of the dangers in the air and being able to make decisions on reducing exposure to the risk factor. In the short term, poor air quality in classrooms leads to a decrease in attention and concentration, both for the student and the teaching staff. The research is motivated by the following: a) The lack of data and studies carried out in Romania, and their relationship with the change in student behavior during the school program; b) Laboratories and practice workshops in high schools are totally different from classrooms from the point of view of air quality and internal factors that contribute to pollution due to the equipment and specific, different activities carried out in these spaces. The novelty of this study is brought by the fact that, at the same time as the monitoring and data acquisition of air quality (inside and outside), there will be a monitoring and data acquisition of real-time biometric measurements of the subjects directly exposed to this environment, via a bracelet attached to the subject's arm. This bracelet is designed to perform biometric measurements without disturbing the subjects in the activities in which they are involved. These measurements are made with 9 different sensors for: pulse, blood oxygen (SPO2), air flow (respiration), body temperature, electrocardiogram (ECG), galvanic skin response (GSR - sweat), blood pressure (sphygmomanometer), patient position/movement, and muscle sensor/electromyography (EMG). The data thus obtained will be centralized on a PC and analyzed later. At the end, an ecological equipment capable of ensuring the ventilation of the space and a constant air quality, by monitoring the values of the compounds in the air, will be created experimentally. [ABSTRACT FROM AUTHOR]

In this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task (M3CV$$ {M}^3 CV $$) dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications. [ABSTRACT FROM AUTHOR]

The mangrove oyster (Crassostrea) is one of the most common bivalves found on the Brazilian coast, widely used in artisanal fisheries. Oysters are dioecious with external reproduction, typically found in estuarine environments. The present study aimed to identify the population aspects of Crassostrea in different fishing locations in northeastern Brazil. The study area was the Acaú-Goiana Extractive Reserve, a protected area focused on the development of artisanal fisheries. Oysters were collected during low tide at three sampling points, where their removal from mangrove roots was carried out, during May, July, September, November 2021, and January and March 2022. In the laboratory, biometric measurements were conducted, and for the months of May and November 2021 histological preparations to study the reproductive cells. Biometric measurements were significantly higher in the dry season at all sampling points. The sex ratio was M = 0.38, F = 0.62, for May, and M = 0.30, F = 0.69, for November. The biometric and reproductive data obtained can support the development of management strategies, including a rotation plan between collection sites and the establishment of a minimum size for fishing. Here we recommend a minimum size of 20 mm shell length (SL) or larger for fishing. [ABSTRACT FROM AUTHOR]

The conception of biometric systems as a means of securing sensitive information and enhancing service delivery remains under-researched. To address this knowledge gap, we explore the case of a public-sector social security and pension organisation in Ghana using a qualitative interpretative study approach and the information security model of confidentiality-integrity-availability as an analytical lens. The study's findings indicate that integrating and using biometric identification and authentication as part of delivering social security and pension services can protect availability, confidentiality, and integrity of information. The findings further show that the use of a biometric system for social security and pension information security can contribute to reducing service turnaround time and vulnerability to fraudulent manipulation of benefits payments. The study provides implications for research, practice, and policy. For research, the paper opens up biometric systems' study from the perspective of information security and service improvement. For practice and policy, the study demonstrates the importance of aligning biometric systems' deployment and use with domain application requirements. [ABSTRACT FROM AUTHOR]

Finger vein recognition (FVR) is a biometric trait that can authenticate the person in real-time applications. However, finger vein (FV) images are generally poor due to various unfavorable factors. Therefore, these images are prone to low contrast, insufficient brightness, and noise problems, significantly impacting the FVR system performance. Hence, image quality assessment and enhancement play a vital role in finger vein recognition. To advance the FVR system performance, we propose two FVR algorithms based on FV image quality estimation and an enhancement algorithm. At first, good quality feature such as contrast, entropy and information capacity were extracted from finger vein images. Then, the image quality is evaluated by the KNN with the r-smote technique to classify the FV image into two classes, High Quality (HQ) and Low Quality (LQ) images. Second, a novel enhancement method called guided filter and bilateral filter (GFBF) are presented to enhance the low-quality FV images. Afterward, we estimate the enhancement algorithm by using SSIM and PSNR. Finally, we evaluate and test the proposed system strength using two parameters—namely accuracy and equal error rate (EER), for Classifier and recognition performance, respectively, on a dataset of 1052 FV images. The completed experiment determined that the proposed image assessment and enhancement method outperformed other enhancement and assessment schemes by achieving a low identification error rate of 0.0335. Further results conclude that the proposed art would be a perfect pre-processing tool for finger vein feature-based algorithms. [ABSTRACT FROM AUTHOR]

Contactless acquisition enables palm identification device to be more easily accepted by people who worry about hygiene problems. The uncertainty of the position between the palm and the device leads to the horizontal rotation, translation, scaling of palm image. This paper proposes a novel feature extraction method which can reflect the geometric features of palmprint and palm vein without being affected by the scaling, rotation and translation. Firstly, the inscribed circle of palm is obtained, and then several radiation segments are made between the circle center and circumference. The wiring direction from the center to the root points of both middle finger and ring finger is as the direction of the first radiation segment. Secondly, the gradient value of pixels in the inscribed circle is calculated by creating template. The feature vector space is established by the relative radius of radiation segment's centroids. Finally, the palm image database is established based on the application. The feature stability in different size of sub-template and recognition performance in different number of feature line segments are analyzed. In the size of sub-template is equal to three and the number of feature line segments is 60, the equal error rate is less than 0.4% and feature extraction time is 0.0019 s. The experimental results show that the method can extract out stable characteristics whenever the hand image is scaled, rotated or translated. [ABSTRACT FROM AUTHOR]

The morphometric and meristic characteristics, length-length relationships (LLRs), length-weight relationships (LWRs) and relative condition factor of forty-one individuals of red cornetfish Fistularia petimba were studied for the first time from the North-West coast of India during February to September 2022. The maximum coefficient of variation was observed for inter-orbital length (25.85%) followed by lower jaw length (18.14%) and pectoral-fin length (16.96%). The morphometric index value was found <10% (genetically-controlled characters) for all the morphometric characters except total length with caudal filament length indicating restricted distribution. LLRs revealed isometric growth and high values of correlation coefficient (0.6109–0.9992). The b value of LWRs showed significant departure from 3 (p < 0.05) and hence negative allometric growth was recorded. The high R2 value of the LWRs indicates absolute fitness of the growth model and superior health status of the studied species. The mean condition factor value was calculated as 0.99 ± 0.04 indicating that the F. petimba population is in good health. Further, F. petimba is a reef-associated species forming a part of low-value bycatch which is mainly utilized for the preparation of fishmeal. Thus the provided morphometric and biometric information of the species will be useful for further biological, stock assessment and nutritional research in the region. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of an Android application for online attendance management, utilizing face recognition and GPS location technologies. The app automates the attendance process while ensuring the authenticity of records. Advanced face detection and recognition algorithms are employed to verify the identity of individuals, eliminating the possibility of proxy attendance. GPS location tracking further enhances reliability by confirming physical presence at the designated location. The user-friendly interface caters to both administrators and attendees, allowing for easy management of classes, attendance parameters, and generation of comprehensive reports. The app eliminates manual attendance-taking, reduces administrative burden, and saves time. It offers highly accurate attendance verification, minimizing errors and manipulation. With its transparency and features, the app revolutionizes attendance tracking for educational institutions, businesses, and event organizers. This implementation provides a reliable, efficient, and user-friendly solution for streamlined attendance management. It is a cost efficient system because there is no need of installing any extra device at the location to mark the attendance, users can mark the attendance using their personal phones [ABSTRACT FROM AUTHOR]

The study's research objectives included examining the effects of advanced surveillance technologies on law enforcement by NPS in Nairobi County and evaluating the ethical implications of using these technologies by the NPS. The social contract theory was used to understand the ethical implications of using advanced surveillance technologies in law enforcement. The research population includes the 4.7 residents of Nairobi County affected by the implementation of advanced surveillance technologies both overtly and covertly. The data was collected from a sample of 50 randomly sampled participants. The respondents were engaged using a qualitative questionnaire. Therefore, both qualitative and quantitative data were collected in the study. The study was analyzed using Statistical Package for Social Science (SPSS) and the thematic analysis method. The study concluded that the use of advanced surveillance technologies such as CCTV systems, facial recognition, license plate recognition, drones, forensic tools, and biometric systems in law enforcement and public safety are widely acknowledged. The ethical considerations regarding these technologies include data security, privacy, freedom of expression, transparency, and accountability. These, however, do not negate the fact that the technologies are necessary for enhancing public safety and emergency response. It is, therefore, imperative to establish clear guidelines and regulations for using advanced surveillance technologies in law enforcement. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Basrah Researches (Sciences) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Smart gloves are gloves with sensing devices that perceive the movements and touch of the hand through artificial intelligence technology and enable interaction with other devices. By wearing the device, it can autonomously recognize the user's movement intention and assist user in completing actions such as clenching fists and lifting objects. With the rapid development of internet technology, flexible wearable electronic devices have a wide range of applications in health supervision, motion detection, and other fields. To improve the power source performance of wearable electronic devices, this study proposed a flexible wearable biometric smart glove based on a friction nanogenerator. The study innovatively prepared a flexible wearable biometric smart device based on two different structures including traditional fabric and porous fiber and achieved multifunctional applications such as harvesting and signal sensing of external energy through friction nanogenerators. The results showed that there was a maximum average power at an external load resistance of 1 when the power density was about 3.98 mW/m². The devices short-circuit current was continuously stabilized at about 40 nA when the pressure magnitude was 10 N in the cycling experiment. When the frequency was varied in the interval from 1 to 4 Hz, the open circuit voltage magnitude was stable at around 57.8 V. The current in a short circuit had the highest value of about 0.4 μA when the frequency was 4 Hz. The classification accuracy rate of the smart glove for biometric recognition was as high as 98.3%. The results indicated that the smart glove designed in the study had good, stabilized performance and could operate for a long time, while having a high classification accuracy rate for biometric identification. The results provided new and improved ideas for the development and application of friction nanogenerators, and contributed to the creation of bendable wearable electronics. [ABSTRACT FROM AUTHOR]

The demand for a non-contact biometric approach for candidate identification has grown over the past ten years. Based on the most important biometric application, human gait analysis is a significant research topic in computer vision. Researchers have paid a lot of attention to gait recognition, specifically the identification of people based on their walking patterns, due to its potential to correctly identify people far away. Gait recognition systems have been used in a variety of applications, including security, medical examinations, identity management, and access control. These systems require a complex combination of technical, operational, and definitional considerations. The employment of gait recognition techniques and technologies has produced a number of beneficial and wellliked applications. Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences. This framework'smain challenge is improving the accuracy of accuracy gait classification under varying conditions, such as carrying a bag and changing clothes. The proposed method's first step is selecting two pretrained deep learning models and training from scratch using deep transfer learning. Next, deep models have been trained using static hyperparameters; however, the learning rate is calculated using the particle swarmoptimization (PSO) algorithm. Then, the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm. This algorithm chooses the best features, combined in a novel correlationbased fusion technique. Finally, the fused best features are categorized using medium, bi-layer, and tri-layered neural networks. On the publicly accessible dataset known as the CASIA-B dataset, the experimental process of the suggested technique was carried out, and an improved accuracy of 94.14% was achieved. The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work. [ABSTRACT FROM AUTHOR]

Background: The global community has set an ambitious goal to end HIV/AIDS as a public health threat by 2030. Significant progress has been achieved in pursuing these objectives; however, concerns remain regarding the lack of disaggregated routine data for key populations (KPs) for a targeted HIV response. KPs include female sex workers, transgender populations, gay men and other men who have sex with men, people who are incarcerated, and people who use drugs. From an epidemiological perspective, KPs play a fundamental role in shaping the dynamics of HIV transmission due to specific behaviors. In South Africa, routine health information management systems (RHIMS) do not include a unique identifier code (UIC) for KPs. The purpose of this protocol is to develop the framework for improved HIV monitoring and programming through piloting the inclusion of KPs UIC in the South African RHIMS. Objective: This paper aims to describe the protocol for a multiphased study to pilot the inclusion of KPs UIC in RHIMS. Methods: We will conduct a multiphased study to pilot the framework for the inclusion of KPs UIC in the RHIMS. The study has attained the University of Johannesburg Research Ethics Committee approval (REC-2518-2023). This study has four objectives, including a systematic review, according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (objective 1). Second, policy document review and in-depth stakeholder interviews using semistructured questionnaires (objective 2). Third, exploratory data analysis of deidentified HIV data sets (objective 3), and finally, piloting the framework to assess the feasibility of incorporating KPs UIC in RHIMS using findings from objectives 1, 2, and 3 (objective 4). Qualitative and quantitative data will be analyzed using ATLAS.ti (version 6; ATLAS.ti Scientific Software Development GmbH) and Python (version 3.8; Python Software Foundation) programming language, respectively. Results: The results will encompass a systematic review of literature, qualitative interviews, and document reviews, along with exploratory analysis of deidentified routine program data and findings from the pilot study. The systematic review has been registered in PROSPERO (International Prospective Register of Systematic Reviews; CRD42023440656). Data collection is planned to commence in September 2024 and expected results for all objectives will be published by December 2025. Conclusions: The study will produce a framework to be recommended for the inclusion of the KP UIC national rollout. The study results will contribute to the knowledge base around the inclusion of KPs UIC in RHIMS data. Trial Registration: PROSPERO CRD42023440656; https://tinyurl.com/msnppany International Registered Report Identifier (IRRID): PRR1-10.2196/55092 [ABSTRACT FROM AUTHOR]