Identification Procedures Research Articles

A procedure for the experimental identification of the strain gradient characteristic length

A procedure for the experimental identification of the strain gradient characteristic length

The Law and Science of Eyewitness Evidence

Eyewitness testimony is heavily reliant on their memory and visual perception, both of which are highly prone to error. Eyewitness memory's propensity for error has long been shown in instances of wrongful conviction. Currently, decades of scientific studies on memory and visual perception have shown important error sources and strategies for enhancing eyewitness accuracy. Similar to a few other legal fields, eyewitness testimony that involves experimentation in the identification process lends itself to scientific study. Consequently, eyewitness testimony has evolved into a test bed for applying science to legal interpretation. This article looks at the responses given to this corpus of research by legal players, including state and federal courts, state legislators, and law enforcement organisations. Although decisions from the Supreme Court have established a constitutional floor, we discover that this largely does not inform first-hand evidence-based law. As we document in a comprehensive fifty-state survey of rulings, state courts have increasingly used eyewitness memory science to reduce misidentifications. In our second examination, we look at what more state legislators have accomplished by examining the twenty-four state statutes that govern the process of eyewitness identification. Third, law enforcement organisations have embraced updated identification procedures, notably through the adoption of a model policy by twenty-nine states.

Correlation of Visible Reflectance Spectrometry and Portable Raman Data for Red Pigment Identification

The accurate identification of pigments is of principal relevance in the field of cultural heritage conservation and restoration practices. In this preliminary study, a first attempt to set up a procedure for accurate red pigment identification, based on the assessment of the correlations existing between visible reflectance spectrometry (vis-RS) and Raman data, is presented. The proposed approach involved the realization of a library consisting of data acquired on a set of 35 pure red historical pigments supplied by ©Kremer Pigmente. In particular, vis-RS data, collected through a Konica Minolta CM-2600d spectrophotometer, were registered, together with the position of the Extrema Points (E.P.s) encompassing both the maximum and minimum points of the first derivative of the % spectral reflectance factor (SRF%) curves. Portable Raman spectroscopy measurements were collected by a B&W Tek Inc. portable Raman spectrometer equipped with a 785 nm laser. For each tested pigment, the positions and relative intensities of the characteristic Raman peaks were considered. The library was then tested for the characterization of the red/reddish painted areas of the medieval wall painting located within The Norman Castle of Aci Castello (Catania, Italy), and was shown to be essential for the unambiguous identification of the pigment used. It is worth noting that this study represents the first novel attempt to establish a reliable and efficient methodology for pigment identification, offering promising prospects in reducing uncertainties and ambiguities arising from the application of a single stand-alone approach.

Distributed robust data-enabled predictive control based voltage control for networked microgrid system

The networked microgrid system (NMG) has become an effective alternative for integrating various types of distributed energy resources (DERs). However, because of the increasing share of DERs and high R/X ratio in the system, the uncertainty and variability of DERs' power generation may cause significant voltage violations in the system. To address these issues, this paper introduces a novel distributed data-driven voltage control strategy based on data-enabled predictive control (DeePC) to address voltage violations problems in the NMG. The DeePC algorithm uses only historical system trajectories, eliminating the need for parameter identification procedures of power system. Based on the system data model, the proposed controller coordinates MGs in the NMG in a distributed manner by employing a consensus version of the fast alternating direction method of the multiplier algorithm (FADMM), which achieves a better privacy and scalability. Simulation studies in a modified two-MG NMG demonstrate the effectiveness and superiority of the proposed algorithm.

There's Something in What We Eat: An Overview on the Extraction Techniques and Chromatographic Analysis for PFAS Identification in Agri-Food Products.

Per- and polyfluorinated alkyl substances (PFASs) are a group of anthropogenic chemicals used in a range of industrial processes and consumer products. Recently, their ubiquitous presence in the environment as well as their toxicological effects in humans have gained relevant attention. Although the occurrence of PFASs is widely investigated in scientific community, the standardization of analytical method for all matrices still remains an important issue. In this review, we discussed extraction and detection methods in depth to evaluate the best procedures of PFAS identification in terms of analytical parameters (e.g., limits of detection (LODs), limits of quantification (LOQs), recoveries). Extraction approaches based on liquid-liquid extraction (LLE), alkaline digestion, and solid phase extraction (SPE), followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) analysis are the main analytical methods applied in the literature. The results showed detectable recoveries of PFOA and PFOS in meat, milk, vegetables, eggs products (90.6-101.2% and of 89.2-98.4%), and fish (96-108%). Furthermore, the low LOD and LOQ values obtained for meat (0.00592-0.01907 ng g-1; 0.050 ng g-1), milk (0.003-0.009 ng g-1; 0.010-0.027 ng g-1), fruit (0.002-0.009 ng g-1; 0.006-0.024 ng g-1), and fish (0.00369-0.017.33 ng g-1; 0.05 ng g-1) also confirmed the effectiveness of the recent quick, easy, cheap, effective, rugged, and safe method (QuEChERS) for simple, speedy, and sensitive ultra-trace PFAS analysis.

Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybrid and Federated Cloud Environment

The concept of several clouds has greatly extended the use of cloud computing and gained popularity in academic and business circles. The use of multi-cloud techniques has increased as businesses use cloud computing more and more to meet their computational demands. A thorough analysis of cloud architectures intended for distributed multi-cloud computing is presented in this study, with an emphasis on federated and hybrid cloud systems. The study looks at the opportunities and difficulties of adopting and overseeing a variety of cloud resources from several providers. The review starts out by going over the basic ideas and reasons for using multi-cloud strategies, emphasizing how important flexibility, scalability, and resilience are in contemporary computing settings. The study then explores the nuances of hybrid cloud architectures, with a focus on how private and public cloud resources can be seamlessly combined. In the context of hybrid cloud installations, important factors including data sovereignty, security, and workload orchestration are covered. In addition, the research delves into federated cloud architectures, clarifying how enterprises can coordinate and oversee workloads across several cloud providers. An examination of resource identification, policy enforcement, and interoperability procedures sheds light on the intricacies of federated cloud computing. The review delves into new developments in standards, best practices, and technology that help multi-cloud ecosystems mature. The study analyses the state of research and industry practices now, pointing out gaps and possible directions for future development. The intention is to provide decision-makers, researchers, and practitioners with a comprehensive grasp of the changing cloud architectural scene so they can plan and execute distributed multi-cloud solutions with knowledge. In conclusion, this article provides a thorough overview of hybrid and federated cloud architectures by combining information from many sources. Through a comprehensive analysis of the difficulties and possibilities associated with multi-cloud computing, the study hopes to add to the current conversation on cloud environment design and optimization in the rapidly changing technological landscape.

Legal actors' and laypersons' utility judgments of eyewitness lineup procedures and outcomes.

Recent attempts to model the relative performances of eyewitness lineup procedures necessarily include theoretical assumptions about the various costs/benefits, or utilities, of different identification outcomes. We collected data to incorporate empirically derived utilities into such modeling as well as data on various stakeholders' views of lineup procedures as tertiary objectives. This research was exploratory; therefore, we did not have a priori hypotheses. We surveyed judges' (n = 70), prosecutors' (n = 28), police officers' (n = 82), and laypersons' (n = 191) opinions about eyewitness identification procedures and the utilities of outcomes of eyewitness identification procedures. We incorporated the utility judgments into models comparing the desirability of various lineup reforms and compared policy preferences between our samples. All samples frequently mentioned estimator and system variables in open-ended evaluations of lineup procedures, but legal samples mentioned system variables more often than did laypersons. Reflector variables (e.g., confidence) were mentioned less often across the board, as was the scientific basis/standardization of identification policy (especially among laypersons). Utility judgments of various identification outcomes indicated that judges adopt values more closely aligned with normative legal ethics (i.e., the Blackstone ratio), whereas other stakeholders (especially laypersons) depart significantly from those standards. Utility models indicated general agreement among samples in lineup procedure preferences, which varied as a function of culprit-presence base rates. Although legal stakeholders vary in how they value eyewitness identification outcomes, their values imply relatively consistent policy preferences that sometimes depart from scientific recommendations. Nonetheless, all samples expressed support for using scientific research to inform legal policy regarding eyewitness evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development and Validation of High-performance Thin layer Chromatography Method for Simultaneous Determination of Gallic Acid and Quercetin from Annona squamosa L. leaf Extract

The objective of this research was to develop a validated high performance thin layer chromatography (HPTLC) procedure for identification, estimation and standardization of two biomarker compound Quercetin and gallic acid from Annona squamosa L. Leaf hydro alcoholic Extract. The chromatographic quantification were conducted on normal HPTLC (20 cm x 10 cm glass backed silica gel 60 F 254) with Toluene: Ethyl acetate: Formic Acid (5.4:3.8:0.8 v/v/v) used as a mobile phase . The detection and evaluation were performed by densitometric scanning at 254nm. The system was found to give a well resolved, compact and intense peaks for quercetin (Rf 0.45±0.05) and gallic acid ( Rf 0.28±0.05) at 360nm and 280nm respectively from other constituents present in the extract of Annona squamosa L .The proposed analytical method for both the biomarker compound was found to be handy, simple, precise, linear (%RSD =0.673808 and 0.896642 for GA and Quercetin respectively), accurate, reliable and sensitive for analysis of both the biomarkers. The LOD/ LOQ (ng) for Gallic acid and Quercetin were recorded as 15/45 and 12/36, respectively in the linearity range of 200-1200ng per spot. The obtained result showed quantities of Gallic acid and Quercetin in leaf hydroalcoholic extract as 0.023% w/w and 0.048 w/w, respectively. The developed HPTLC was found to be suitable for the routine analysis of both of these biomarkers in the hydroalcoholic extract of Annona squamosa L. and can be further employed in the process quality control of herbal formulations containing the mentioned biomarkers.

Applications of Legal Psychology in Japan: Current Practice, Research Knowledge, and Future Directions

Legal psychology is field of research which seeks to bring evidence-based practice to the vital work of the criminal justice system. This research is increasingly being conducted and its findings applied around the world. However, worldwide legal systems and their processes can vary greatly. In the current paper, we summarise discussions between legal psychology academics and criminal justice practitioners from Japan. Together, we examine how practices in the Japanese criminal justice system interact with the international evidence base for psychologically-informed ‘best practice’ approaches. Our discussion targets areas of popular study in legal psychology, focusing on concealed crime knowledge detection, line up identification procedures, and investigative interviewing of witnesses, suspects, and victims. Each section features a description of current Japanese practice, followed by a review of the current state of the relevant academic legal psychological literature. We then connect this practice and research synergy to a reflection with suggestions for future research. Taken together, our paper acts as a conduit to incentivise more research and practice collaboration for Japanese and non-Japanese audiences and presents opportunities for collective international legal psychology.

Application of fuzzy failure mode and effect analysis to investigate lean wastes in the sewing section

PurposeThis study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.Design/methodology/approachWe employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.FindingsThese techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.Research limitations/implicationsThe research focuses on a case company and the result could not be generalized for the whole industry.Practical implicationsThe study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.Originality/valueThe methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

Rapid screening of the novel bioactive peptides with notable α-glucosidase inhibitory activity by UF-LC-MS/MS combined with three-AI-tool from black beans

This work aimed to propose a rapid method to screen the bioactive peptides with anti-α-glucosidase activity instead of traditional multiple laborious purification and identification procedures. 242 peptides binding to α-glycosidase were quickly screened and identified by bio-affinity ultrafiltration combined with LC-MS/MS from the double enzymatic hydrolysate of black beans. Top three peptides with notable anti-α-glucosidase activity, NNNPFKF, RADLPGVK and FLKEAFGV were further rapidly screened and ranked by the three artificial intelligence tools (three-AI-tool) BIOPEP database, PeptideRanker and molecular docking from the 242 peptides. Their IC50 values were in order as 4.20 ± 0.11 mg/mL, 2.83 ± 0.03 mg/mL, 1.32 ± 0.09 mg/mL, which was opposite to AI ranking, for the hydrophobicity index of the peptides was not included in the screening criteria. According to the kinetics, FT-IR, CD and ITC analyses, the binding of the three peptides to α-glucosidase is a spontaneous and irreversible endothermic reaction that results from hydrogen bonds and hydrophobic interactions, which mainly changes the α-helix structure of α-glucosidase. The peptide-activity can be evaluated vividly by AFM in vitro. In vivo, the screened FLKEAFGV and RADLPGVK can lower blood sugar levels as effectively as acarbose, they are expected to be an alternative to synthetic drugs for the treatment of Type 2 diabetes.

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Abstract Background Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

Sequence of Simple Digital Technologies for Detection of Platelets in Medical Images

Platelets play an important role in the diagnosis and detection of various diseases, the course of the disease in the future, and the possibility of justifying treatment methods. In this aspect, platelet counting is of key importance. For these purposes, it is important to correctly identify such objects. This served as the basis for the development and generalization of an appropriate medical image analysis procedure. The purpose of this study is to construct a generalized procedure for platelet identification in medical digital images. The work examined at least 30 images containing objects such as platelets. These images are approximately the same type, but with different intensity of the presence of the main objects in the blood smear. Similar but noisy images are also considered. These images were noisy with different types of noise. Thus, a total of at least 120 images were examined. In general, this allows us to evaluate the effectiveness of the proposed procedure for identifying platelets in medical images. This procedure includes simple methods of image analysis such as: binarization, morphological analysis, taking into account the influence of the sizes of different objects and comparative analysis of images at intermediate stages of the study. To summarize the results, estimates such as the percentage of false detection of platelets and the percentage of missed platelets were considered. The platelet identification results that were obtained for non-noisy images are as follows: false platelet isolation was less than 0.1%, missed platelets were within 2-2.5%. The worst result for noisy images is false platelet isolation – within 10% (for images with multiplicative noise), missed platelets – within 7.5-8% (for images with multiplicative noise). It should also be noted that noisy images are characterized by identification of platelets with distortion of their sizes: these sizes are reduced or increased. The percentage of such distortions does not exceed 1.3% (for images with Poisson noise).The problematic aspects of platelet identification in digital medical images are considered. Particular attention is paid to simple methods of digital image processing. Among the problematic aspects of the proposed approach there is a need to clearly take into account the geometric dimensions of platelets. The results obtained are acceptable and can be used as the basis for an automated blood smear analysis system.

A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques

The early and precise identification of the different phenological stages of the bean (Phaseolus vulgaris L.) allows for the determination of critical and timely moments for the implementation of certain agricultural activities that contribute in a significant manner to the output and quality of the harvest, as well as the necessary actions to prevent and control possible damage caused by plagues and diseases. Overall, the standard procedure for phenological identification is conducted by the farmer. This can lead to the possibility of overlooking important findings during the phenological development of the plant, which could result in the appearance of plagues and diseases. In recent years, deep learning (DL) methods have been used to analyze crop behavior and minimize risk in agricultural decision making. One of the most used DL methods in image processing is the convolutional neural network (CNN) due to its high capacity for learning relevant features and recognizing objects in images. In this article, a transfer learning approach and a data augmentation method were applied. A station equipped with RGB cameras was used to gather data from images during the complete phenological cycle of the bean. The information gathered was used to create a set of data to evaluate the performance of each of the four proposed network models: AlexNet, VGG19, SqueezeNet, and GoogleNet. The metrics used were accuracy, precision, sensitivity, specificity, and F1-Score. The results of the best architecture obtained in the validation were those of GoogleNet, which obtained 96.71% accuracy, 96.81% precision, 95.77% sensitivity, 98.73% specificity, and 96.25% F1-Score.

Design and analysis of shadow detection and removal scheme in real-time still images

This research’s main objective is to study and evaluate the detection and removal of undesired shadows from still images since these shadows might mask important information caused by light sources and other obstructions. A variety of methods for detecting and eliminating shadows as well as object tracking approaches based on movement estimation and identification are investigated. This includes shadow removal methods like background subtraction, which are intended to improve obstacle recognition of the source item and increase the accuracy of shadow removal from objects. When new items enter the frame, they are first distinguished from the background using a reference frame. The tracking procedure is made more difficult by the merging of the shadow with the foreground object. The approach highlights the difficulties in object detection owing to frequent occurrences of obstacles by using morphological procedures for shadow identification and removal. The proposed approach uses feature extraction is also discussed, highlighting its importance in image processing research and the use of suggested methods to get over obstacles in image sequences. The proposed method for shadow identification and removal offers a novel approach to improve image processing when dealing with still images. The purpose of this technique is to better detect and remove shadows from images, which will increase the precision of object tracking and detection. Depending on the type of images being processed, the process begins with initializing a background model, which is based on a static image background.

Method for identifying the main factors controlling landslides based on a particle coupling algorithm simulating the ultimate state of a gas pipeline

To determine the ultimate load of buried gas pipelines under different working conditions under landslide action and provide a quick decision-making tool for landslide risk management, a pipe–soil coupling model is established based on smoothed particle hydrodynamics and the finite element method. The double elastic slope criterion and fault tree analysis method are used to perform limit and failure analyses on the pipelines. The research results indicate that pipeline stress and strain during oblique crossing are positively correlated with the crossing angle. The upper section of the pipeline exhibits tensile bending, whereas the lower section exhibits compressive bending. Local stress is concentrated at the elbows at both ends of the pipeline during longitudinal crossing, which is similar to the force applied during oblique crossing. The ultimate pipeline bearing capacity is negatively correlated with its burial depth and pressure. There is a positive correlation between the distance from the pipeline to the rear edge of the landslide and the width of the landslide. There is also an optimal value for the moisture content of landslides. Fault tree analysis reveals that, to prevent pipeline failure under the action of landslides, priority should be given to determining a reasonable pipeline laying method, reducing the moisture content of the sliding body, controlling the thickness and width of the landslide body, controlling the displacement and inclination angle of the sliding body, timely eliminating soil and soil pressure, and avoiding the failure of drainage systems and support facilities. Based on the research results, we propose a method that can preliminarily identify the main control factors affecting pipeline integrity. This method provides a reference for studying and developing engineering mitigation measures, identification procedures, and operation guidance documents for geological disaster risks for pipelines crossing landslide sections.

First Responder's Standard Operating Procedure for Employing Drones to Expedite Insurance Claims

Drones offer cost-effective real-time data collection, payload delivery, and have accelerated various commercial, industrial, and recreational applications. They are commonly used for research, disease transmission monitoring, disaster site assessment, and biohazard monitoring. However, disaster victim insurance claims are hampered by insufficient cooperation between first responders, typically the Civil Defence Department, and the insurance industry. This paper proposes integrating drones into standard operating procedures for disaster victim identification by first responders. Improved collaboration between first responders and insurers can expedite insurance claims, benefiting both parties and disaster victims. Insights are drawn from feedback provided by Civil Defence Department Malaysia's first responders and experienced forensic experts in search and rescue operations

Intelligent detection of tetracycline by a rare earth multicolor fluorescent probe based on guanosine-5′-monophosphate

Long-term discharge of tetracycline (TC) in the environment exerts selective pressure on the bacteria, increasing resistance in sensitive bacteria and posing a serious threat to public health. Therefore, it is crucial to develop real-time detection of tetracycline. In this study, we reported a novel rare-earth fluorescent nanosensor FITC-GMP-Eu, based on the formation of guanosine-5′-monophosphate disodium salt (GMP) and fluorescein isothiocyanate (FITC), which enabled the real-time identification of tetracycline in the environment. When there was no TC in the environmental sample, the FITC-GMP-Eu nanosensor exhibited the green fluorescence of FITC due to the fluorescence quenching effect of water molecules on Eu3+. When the environmental samples contained TC, the red fluorescence of Eu3+ could be sensitized due to the diketone structure in the tetracycline molecule, which enabled the naked-eye visualized identification of TC. The nanosensor had high sensitivity (detection limit of 19 nM) and a wide detection range (0–30 μM) for TC. At the same time, the sensor was characterized by high visualization and anti-interference ability due to the strong fluorescence of both FITC and Eu complexes. In order to improve the practicality of the detection, this study also prepared a paper-based and sodium alginate gelated fluorescence detection platform with low background fluorescence. Combined with the common color analysis and RGB extraction procedures in mobile phones, real-time and on-site identification of TC on the surface of various fruit samples can be achieved.

Bicistronic expression of nuclear transgenes in Chlamydomonas reinhardtii.

In eukaryotic organisms, proteins are typically translated from monocistronic messenger RNAs containing a single coding sequence (CDS). However, recent long transcript sequencing identified 87 nuclear polycistronic mRNAs in Chlamydomonas reinhardtii natively carrying multiple co-expressed CDSs. In this study, we investigated the dynamics of 22 short intergenic sequences derived from these native polycistronic loci by their application in genetic constructs for synthetic transgene expression. A promising candidate sequence was identified based on the quantification of transformation efficiency and expression strength of a fluorescence reporter protein. Subsequently, the expression of independent proteins from one mRNA was verified by cDNA amplification and protein molecular mass characterization. We demonstrated engineered bicistronic expression in vivo to drive successful co-expression of several terpene synthases with the selection marker aphVIII. Bicistronic transgene design resulted in significantly increased (E)-α-bisabolene production of 7.95 mg L-1 from a single open reading frame, 18.1× fold higher than previous reports. Use of this strategy simplifies screening procedures for identification of high-level expressing transformants, does not require the application of additional fluorescence reporters, and reduces the nucleotide footprint compared to classical monocistronic expression cassettes. Although clear advantages for bicistronic transgene expression were observed, this strategy was found to be limited to the aphVIII marker, and further studies are necessary to gain insights into the underlying mechanism that uniquely permits this co-expression from the algal nuclear genome.

Integrating artificial intelligence and adult dental age estimation in forensic identification: A literature review

Age estimation is crucial in various forensic fields, including forensic medicine, anthropology, and demographic studies. Adult dental age estimation is affected by multiple factors, resulting in discrepancies between dental age and chronological age. The development of artificial intelligence (AI) technology has led to extensive investigations in forensic sciences, encompassing several areas such as facial recognition, age, sex identification, and DNA analysis. Adult age estimation methods commonly used include the pulp-tooth ratio approach, the Harris & Nortje method, and the Van Heerden method. AI approaches such as Fuzzy Logic (FL), Evolutionary Computing (EC), and Machine Learning (ML) are being extensively applied. These techniques use algorithms to imitate human thinking and behavior. Deep learning techniques, explicitly using deep convolutional neural networks (DCNN), enable age estimation by segmenting images and making measurements, replicating the cognitive processes of radiologists when computing indices such as the third molar maturity (I3M) index. Also, DCNNs automatically optimize teeth segmentation in dental X-ray images, improving image refining and analysis efficiency. AI integration in forensic dentistry improves the precision and effectiveness of dental data processing while significantly accelerating individual identification procedures. Incorporating this technology shows potential for enhancing the caliber and dependability of evidence in forensic investigations.