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Machine learning techniques for differentiating psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes
(Springer Science and Business Media Deutschland GmbH, 2025) Vardar-Yel, Nurcan
Psychrophilic enzymes represent a category of macromolecules that have acquired specific properties that enable these enzymes to perform their catalytic activity at low temperatures with high efficiency. One of the factors contributing to their adaptation is increased active site flexibility. Psychrophilic enzymes are of significant industrial interest due to their applications in food production, environmental remediation, pharmaceuticals, textiles, and detergents. Despite growing interest, the molecular mechanisms underlying the adaptation of psychrophilic enzymes to low temperatures remain largely unexplored. This study aims to investigate the differences between psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes. 464 psychrophilic and 562 non-psychrophilic α/β hydrolase enzymes were retrieved from the UniProt database. Further classification of these enzymes based on amino acid composition was performed using a set of machine learning algorithms such as Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Ten variables, including the contents of Ala, Gly, Ser, Thr, charged, aliphatic, aromatic, and hydrophobic amino acids, as well as the aliphatic index and the grand average of hydropathy (GRAVY), were analyzed. The Random Forest algorithm achieved the highest classification rate with an accuracy of 77%. Further analyses showed that the amino acid threonine and serine played the most important role in determining psychrophilic traits. This suggests that these amino acids play a significant role in enhancing the enzyme's hydrogen-bonding capacity, thereby contributing to its structural flexibility and stability under cold conditions. This study confirms that some amino acids, especially serine and threonine, are generally involved in the cold adaptation of psychrophilic α/β hydrolase enzymes and may provide an interesting platform from a biotechnological point of view.
Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment
(Sakarya University, 2025) Aloamiri, Adel; Abdu Ibrahim, Abdullahi
The present paper has been devoted to the study conducted with the purpose of examining the possibility of applying Machine Learning techniques in classifying leadership based on structured survey data. The objective was to create a predictive model that would allow classifying leadership into three groups – Low, Medium, and High – based on behavior scores. The model was expected to offer a reliable tool for improving leadership development programs and recruitment processes by providing a precise and scalable leadership classification, The study illustrates the potential of advanced ML techniques for rethinking the traditional approaches to the assessment of leadership. Due to the use of advanced ensemble modeling, it was possible to ensure the high accuracy of 93.3% in leadership predicting. Such outcomes can generate considerable advantages for organizational development strategies. The use of ensemble machine learning in the domain of organizational behavior studies can be considered as a valuable academic contribution as it has demonstrated the capacity of determining the application of ensemble techniques for enhancing leadership studies. at the same time, it offers a useful instrument to develop more sophisticated and data-driven practices for leadership development.
Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study
(2025) Tanyıldızı-Kökkülünk, Handan
Objectives: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols. Methods: A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances. Results: The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses. Conclusions: The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.
Enhancing Radiotherapy Tolerance With Papaya Seed-Derived Nanoemulsions
(Malden, 2025) Siddiqui, Muhammad Tariq; Ölçeroğlu, Bilge; Gümüş, Zinar Pınar; Şenışık, Ahmet Murat; Barlas, Fırat Barış
Flavonoid-rich plant materials have gained attention for their potential to reduce radiotherapy side effects. Carica papaya (CP) seeds, known for high flavonoid content, hold promise for therapeutic applications. This study explored the extraction and evaluation of two oils-sunflower oil-based papaya oil (SPO) and pure papaya oil (PPO)-and their nano emulsions (SPOE and PPOE), derived from CP seeds, for radioprotective effects. Chemical analysis using QTOF-MS revealed antioxidants and phytochemicals in the oils and emulsions. Size analysis and zeta potential measurements using dynamic light scattering (DLS) showed particle sizes of 140 ± 26.06 nm for PPOE and 293.7 ± 49.42 nm for SPOE. Post-radiation, both SPOE and PPOE significantly enhanced cell viability, with values of 72.24 ± 3.92 (p ≤ 0.001) and 75.85 ± 2.62 (p ≤ 0.001), respectively. These nanoemulsions show potential as topical agents for reducing radiation-induced tissue damage in radiotherapy. Despite the promising in vitro findings, further in vivo studies are needed to confirm the clinical relevance of these nanoemulsions. Additionally, their incorporation into sunscreen formulations could provide further protection against radiation-induced skin damage, broadening their potential applications.
Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye
(Springer Science and Business Media B.V., 2025) Khalaf, Oras Fadhil; Uçan, Osman Nuri; Alsamarai, Naseem Adnan
This research highlights the importance of integrating machine learning algorithms with Geographical Information Systems (GIS) applications in the field of renewable energy by finding a suitable site for wind farms due to their importance in preserving the environment to achieve efficiency and cost-effectiveness and reduce the environmental impact of fossil fuel energy sources. Using GIS various factors affecting wind energy localization were processed and analyzed including natural, socio-economic and environmental criteria. Ensemble learning of four supervised machine learning algorithms (Random Forest, K-Nearest Neighbor, Support Vector Machines, Naive Bayes) was used to classify suitable and unsuitable data representing geo-referenced points on the ground with three criteria for each site (wind speed, elevation and slope). The results of the algorithms varied in terms of accuracy and variance, then the results were collected, and the intersection between them was found so that the location classification would be agreed upon in the results of the algorithms used. The aim of using this technique is to reduce the error, increase the accuracy and avoid the bias or variance present in individual models. Accuracy of the algorithms result was respectively (K-Nearest Neighbor, Random Forest, Support Vector Machines, Naive Bayes) (93.022%, 93.018%, 95.095%, 89.553%). The final result is a map using GIS showing the suitable and unsuitable sites of wind farms in the study area (Türkiye) has been chosen as a study area in the research due to several factors that make it suitable for wind energy projects, including its geographical location, which gives it great climatic and terrain diversity, as it is surrounded by seas (Black Sea, Aegean Sea, and Mediterranean Sea), which leads to the activity of seasonal and continuous winds, which contributes to the activity of seasonal and permanent winds. Its drive to develop investment in renewable energy due to economic and population growth has increased the demand for energy and consequently the development of renewable and sustainable energy sources. This research contributes to supporting the global transition to sustainable energy by providing a new methodology for integrating multiple technologies to support a sustainable energy future.
A multi-objective supply chain optimization model for reliable remanufacturing problems with M/M/m/k queues
(Elsevier B.V., 2025) Hajipour, Vahid; Kaveh, Shermineh Hadad; Yiğit, Fatih; Gharaei, Ali
Product recovery is critical in reducing costs, enhancing profitability, and improving supply chain responsiveness to customer demands. Remanufacturing returned products, as part of the circular economy, is a central strategy in achieving these goals. This study presents a model that optimizes the remanufacturing process using in-house workstations and outsourcing to maximize supply chain profitability, reduce queue lengths, and ensure machine reliability. The remanufacturing system is modeled as an M/M/m/k queuing system, considering real-world supply chain constraints such as budget limitations, station capacity, and machine reliability. Supply chain optimization is achieved by maintaining efficiency while examining different remanufacturing policies and pricing strategies. The results show that expanding remanufacturing capacity enhances supply chain profitability, even with moderate increases in queue length. We provide valuable insights for supply chain managers aiming to optimize their remanufacturing processes and balance cost, efficiency, and reliability.
Advanced Deep Learning Models for Improved IoT Network Monitoring Using Hybrid Optimization and MCDM Techniques
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Qasim Jebur Al-Zaidawi, Mays; Çevik, Mesut
This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential for real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm Optimization (HGWOPSO) and Hybrid World Cup Optimization with Harris Hawks Optimization (HWCOAHHO)—designed to symmetrically balance global exploration and local exploitation, thereby enhancing model training and adaptation in IoT environments. These methods leverage complementary search behaviors, where symmetry between global and local search processes enhances convergence speed and detection accuracy. The proposed approaches are validated using real-world IoT datasets, demonstrating significant improvements in anomaly detection accuracy, scalability, and adaptability compared to state-of-the-art techniques. Specifically, HGWOPSO combines the symmetrical hierarchy-driven leadership of Grey Wolves with the velocity updates of Particle Swarm Optimization, while HWCOAHHO synergizes the dynamic exploration strategies of Harris Hawks with the competition-driven optimization of the World Cup algorithm, ensuring balanced search and decision-making processes. Performance evaluation using benchmark functions and real-world IoT network data highlights superior accuracy, precision, recall, and F1 score compared to traditional methods. To further enhance decision-making, a Multi-Criteria Decision-Making (MCDM) framework incorporating the Analytic Hierarchy Process (AHP) and TOPSIS is employed to symmetrically evaluate and rank the proposed methods. Results indicate that HWCOAHHO achieves the most optimal balance between accuracy and precision, followed closely by HGWOPSO, while traditional methods like FFNNs and MLPs show lower effectiveness in real-time anomaly detection. The symmetry-driven approach of these hybrid algorithms ensures robust, adaptive, and scalable monitoring solutions for IoT networks characterized by dynamic traffic patterns and evolving anomalies, thus ensuring real-time network stability and data integrity. The findings have substantial implications for smart cities, industrial automation, and healthcare IoT applications, where symmetrical optimization between detection performance and computational efficiency is crucial for ensuring optimal and reliable network monitoring. This work lays the groundwork for further research on hybrid optimization techniques and deep learning, emphasizing the role of symmetry in enhancing the efficiency and resilience of IoT network monitoring systems.
A Hybrid Tree Convolutional Neural Network with Leader-Guided Spiral Optimization for Detecting Symmetric Patterns in Network Anomalies
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Al-Dulaimi, Reem Talal Abdulhameed; Türkben, Ayça Kurnaz
In the realm of cybersecurity, detecting Distributed Denial of Service (DDoS) attacks with high accuracy is a critical task. Traditional machine learning models often fall short in handling the complexity and high dimensionality of network traffic data. This study proposes a hybrid framework leveraging symmetry in feature distribution, network behavior, and model optimization for anomaly detection. A Tree Convolutional Neural Network (Tree-CNN) captures hierarchical symmetrical dependencies, while a deep autoencoder preserves latent symmetrical structures, reducing noise for better classification. A Leader-Guided Velocity-Based Spiral Optimization Algorithm is proposed to optimize the parameters of the system and achieve better performance. A Leader-Guided Velocity-Based Spiral Optimization Algorithm is introduced to maintain a symmetrical balance between exploration and exploitation, optimizing the autoencoder, Tree-CNN, and classification thresholds. Validation using three datasets—UNSW-NB15, CIC-IDS 2017, and CIC-IDS 2018—demonstrates the framework’s superiority. The model achieves 96.02% accuracy on UNSW-NB15, 99.99% on CIC-IDS 2017, and 99.96% on CIC-IDS 2018, with near-perfect precision and recall. Despite a slightly higher computational cost, the symmetrically optimized framework ensures high efficiency and superior detection, making it ideal for real-time complex networks. These findings emphasize the critical role of symmetrical network patterns and feature selection strategies for enhancing intrusion detection performance.
Enhancing Cold Cases Forensic Identification with DCGAN-based Personal Image Reconstruction
(University of Baghdad, 2025) AL-Muttairi, Hasan Sabah K.; Kurnaz, Sefer; Aljuboori, Abbas Fadhil
With the improvement of artificial intelligence and deep learning techniques, especially deep convolutional generative adversarial network (DCGAN), there has been a significant development in personal identity and generating images through facial reconstruction systems. This study focuses on proposing a model of personal image reconstruction from forensic sketches using DCGAN. The model comprises two networks: a generator to convert sketch images into real images and a feature network to determine the similarity of the generated images to real ones. Forensic sketches provided by relevant authorities are used as inputs to the proposed model. These sketches include details and information on the perpetrators or missing persons obtained from witnesses or the missing person parents. Prominent facial features extracted from the reconstructed images aid in the process of personal image reconstruction. The proposed model shows good results, achieving up to 99% accuracy in the generated images. The error ratio is reported to be as low as 0.92% based on the evaluation using the CUHKFaces dataset. This study presents a new approach to reconstructing human face images from forensic sketches using DCGAN.
Maturity Model for Corporate Sector Based on Zero Trust Adoption
(Institute of Electrical and Electronics Engineers Inc., 2025) Ilyas, Muhammad; Akal, Mustafa; Althebyan, Qutaibah
The rapid evolution of cybersecurity threats necessitates the adoption of robust security frameworks. One such approach gaining significant attention is the concept of zero trust, which emphasizes continuous and strong verification, strict access controls and identity management, and well-planned strategy and assessment to mitigate risks. However, organizations often face challenges in effectively implementing and assessing their progress in zero trust adoption. We propose a maturity model for zero trust adoption, designed to assist organizations in evaluating their current security posture, identifying gaps, and developing a roadmap for achieving higher levels of zero trust maturity. The model encompasses various dimensions, including network segmentation, access controls, data protection, and incident response. Additionally, a comprehensive set of self-assessment queries is provided to enable organizations to gauge their progress and identify areas for improvement. Through the utilization of this maturity model and self-assessment framework, organizations can enhance their understanding of zero trust principles, align their security strategies, and prioritize necessary investments to strengthen their overall security posture.
Integrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection
(Coll Science Women, 2025) Sami, Fadia; Koyuncu, Hakan
Molecular similarity, governed by the principle that “similar molecules exhibit similar properties,” is a pervasive concept in chemistry with profound implications, notably in pharmaceutical research where it informs structure-activity relationships. This study focuses on the pivotal role of molecular similarity techniques in identifying sample molecules akin to a target molecule while differing in key features. Within the realm of artificial intelligence, this paper introduces a novel hybrid system merging Swarm Intelligence (SI) behaviors (Aquila and Termites) with Neural Networks. Unlike previous applications where Aquila or Termites were used individually, this amalgamation represents a pioneering approach. The objective is to determine the most similar sample molecule in a dataset to a specific target molecule. Accuracy assessments reveal a manual evaluation accuracy of 70.58%, surging to 90% with the incorporation of Neural Networks. Additionally, a three-dimensional grid elucidates the Quantitative Structure-Activity Relationship (QSAR). The Euclidean and Manhattan Distance metrics quantify differences between molecules. This study contributes to molecular similarity assessment by presenting a hybrid approach that enhances accuracy in identifying similar molecules within complex datasets.
Correction to : The Psychometric Properties of Autism Mental Status Examination (AMSE) in Turkish Sample
(2025) Meral, Yavuz; Bıkmazer, Alperen; Örengül, Abdurrahman Cahid; Çakıroğlu, Süleyman; Altınbilek, Esra; Bakır, Fulya; Bıkmazer, Bilgihan; Saleh, Ayman; Görmez, Vahdet
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Identification of common genes associated with development of resistance against tamoxifen and doxorubicin in MCF7 cells
(Springer Nature, 2025) Karabay, Arzu Zeynep; Koç, Aslı; Hekmatshoar, Yalda
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Probabilistic Risk Framework for Nuclear- and Fossil-Powered Vessels: Analyzing Casualty Event Severity and Sub-Causes
(MDPI, 2025) Tanyıldızı-Kökkülünk, Handan; Kökkülünk, Görkem; Settles, John
Maritime activities pose significant safety risks, particularly with the growing presence of nuclear-powered vessels (NPVs) alongside traditional fossil-powered vessels (FPVs). This study employs a probabilistic risk assessment (PRA) approach to evaluate and compare accident hazards involving NPVs and FPVs. By analyzing historical data from 1960 to 2024, this study identifies risk patterns, accident frequency (probability), and severity levels. The methodology focuses on incidents such as marine incidents, marine casualties, and very serious cases with sub-causes. Key findings reveal that Russia exhibits the highest risk for very serious incidents involving both NPVs and FPVs, with a significant 100% risk for NPVs. China has the highest FPV risk, while France and the USA show above-average risks, particularly for marine casualties and very serious incidents. Moreover, collision is the most significant global risk, with a 26% risk for NPVs and 34% for FPVs, followed by fire hazards, which also pose a major concern, with a 17% risk for NPVs and 16% for FPVs, highlighting the need for enhanced safety and fire-prevention measures. In conclusion, comparative analysis highlights the need for enhanced stability improvements, fire prevention, and maintenance practices, particularly in the UK, France, Russia, and China. This study underscores the importance of targeted safety measures to mitigate risks, improve ship design, and promote safer maritime operations for both nuclear- and fossil-fueled vessels.
A machine learning assisted designing and chemical space generation of benzophenone based organic semiconductors with low lying LUMO energies
(Elsevier Ltd, 2025) Güleryüz, Cihat; Hassan, Abrar U.; Güleryüz, Hasan; Kyhoiesh, Hussein A.K.; Mahmoud, Mohamed H.H.
Current study presents a machine learning (ML) approach to design benzophenone-based organic chromophore with their lowest possible LUMO energy (ELUMO). A dataset of their 1142 donors is collected from literature and their molecular descriptors are designed by using RDKit. Among various models, the Random Forest regression model produces accurate results to predict their ELUMO values. Based on these predictions, their 5000 new donors are designed with their Synthetic Accessibility Likelihood Index (SALI) scores. Their SHAP value analysis reveals that their electro topological state indices are the most critical descriptors to lowering ELUMOs. The top- performing donor are further extended with acceptors and their photovoltaic (PV) properties by density functional theory (DFT). Their results show their maximum open-circuit voltage (Voc) of 2.30 V, a short-circuit current (Jsc) of 47.19 mA/cm2, and a light-harvesting efficiency (LHE) of 93 %. This study demonstrates the potential of ML assisted design to design new organic chromophores.
OPTIMIZING ROAD SAFETY: THE ROLE OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) IN TRAFFIC ACCIDENT ANALYSIS AND PREDICTION
(Faculty of Engineering, University of Kragujevac, 2025) Alfaras, Mohammed Shukur; Karan, Oğuz; Kurnaz, Sefer
This study investigates the application of Geographic Information Systems (GIS) in traffic accident analysis and prediction. By integrating GIS with deep learning techniques, the research highlights how spatial data management and analysis can enhance road safety. Key objectives include identifying accident hotspots, optimizing traffic control systems, and improving emergency response. The methodology involves a comprehensive review of existing literature, emphasizing GIS's role in data integration, spatial analysis, and predictive modeling. Findings demonstrate that GIS significantly contributes to understanding traffic patterns, predicting accidents, and formulating targeted safety interventions. Challenges such as data complexity, real-time processing, and model interpretability are addressed, offering future directions for leveraging GIS in road safety management. The study concludes that GIS, combined with advanced analytics, presents a powerful tool for reducing traffic accidents and enhancing overall traffic safety.
A graph neural network assisted reverse polymers engineering to design low bandgap benzothiophene polymers for light harvesting applications
(Elsevier Ltd, 2025) Hassan, Abrar U.; Güleryüz, Cihat; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
In this study, we present a novel approach to reverse polymer engineering utilizing a Graph Neural Network (GNN) framework to design low bandgap benzothiophene (BT) polymers for light harvesting applications. We have curated an extensive dataset comprising 57,556 structure-property pairs of BT-based compounds, leveraging expert knowledge to enhance the quality and relevance of the data. Our Transformer-Assisted Oriented pretrained model for on-demand polymer generation (TAO) demonstrates exceptional performance, achieving a chemical validity rate of 99.27 % in top-1 generation mode across a test set of 6000 generated polymers, marking the highest success rate reported among polymer generative models to date. Throughout the training process, the loss steadily decreased with each epoch, indicating that the model was learning effectively from the data. The model predictive accuracy is further validated by an impressive average R2 value of 0.96 for 15 defined properties, highlighting the TAO with its robust capabilities in polymer design. The newly designed polymers exhibit a bandgap range of 1.5–3.40 eV, making them promising candidates for light harvesting applications. Additionally, their highest Synthetic Accessibility Likelihood Index (SALI) scores reach up to 17 and also indicates that the majority of these polymers are amenable to synthesis. This work not only advances the field of polymer design but also provides a powerful tool for the targeted development of materials with specific electronic properties.
A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials
(Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Elnaggar, Ashraf Y.; Noreen, Sadaf
The selenium-based compounds are gaining significance for their surface-enhanced properties. In order to accelerate their discovery, a machine learning (ML) approach has been employed to predict their structural correlations. For this a dataset of 618 compounds is collected from literature and is trained by using Support Vector Machine (SVM) with its Linear Kernal. Among ten ML evaluated models, three top-performing models are selected to make predictions for their stability energy. A Convex Hull Distribution (CHD) is constructed to elucidate the relationship for their stability and structural correlations. The main finding of this study reveals its strong correlation between stability and its related structural descriptors, particularly Bertz Branching Index" corrected for the number of Terminal atoms (BertzCT), Partial Equalization of Orbital Electronegativities-Van der Waals Surface Area with 14 bins (PEOE_VSA14), and First-Order Connectivity Index (chi 1). The analysis demon strates that the current ML models can effectively predict the stability of such materials to enable their rapid screening. Their calculations can provide a framework to understand their complex relationships between their material properties, structure, and stability.
Online simulation versus traditional classroom learnings in clinical pharmacy education: effect on students' knowledge, satisfaction and self-confidence
(BioMed Central, 2025) Selçuk, Aysu; Öztürk, Nur; Önal, Nurbanu; Bozkır, Asuman; Aksoy, Nilay
Background: Over the course of the past few years, the area of medical education has experienced a substantial movement towards the establishment of online learning platforms and resources. This study aimed to to evaluate the efficacy of an online simulation learning intervention, MyDispense®, compared to traditional classroom learning in terms of enhancing knowledge, satisfaction, and self-confidence among participants. Methods: A multicentre randomized controlled study was conducted among pharmacy students who were assigned either intervention MyDispense® or control traditional classroom learning groups. They were eligible if they previously had experience with online simulation learning. A previously validated questioner were used to measure the outcome of knowledge, satisfaction and self-confidence. Results: Both the intervention and control groups revealed significant improvement in knowledge, the P value for pre-post knowledge scores for each group was < 0,001. Despite these internal improvements, this study's findings showed no statistically significant differences (p > 0.05) between the intervention and control groups on knowledge gain, satisfaction, or self-confidence. This represents comparable outcomes irrespective of the group's exposure to intervention. Conclusion: The study evaluated the efficacy of online simulation learning intervention MyDispense® in comparison to traditional classroom learning. While both strategies effectively improved knowledge, satisfaction, and self-confidence, the findings demonstrated that the online simulation yielded equivalent learning benefits. MyDispense® could be an alternative to traditional education in situations where face to face learning is not feasible, with comparable learning outcomes. Clinical trial number: not applicable.
3D modelling and x-ray depth analysis map of the pulp with computer software via digital periapical radiography and cone beam computed tomography
(2025) Felek, Turgut; Şatır, Samed; Özel, Şelale
Objective: Periapical radiographs (PAR) offer information about the pulp and periodontal health of teeth. However, intraoral radiographs are insufficient for diagnosing buccolingual anomalies and variations such as bifid canals due to their two-dimensional nature. Cone beam computed tomography (CBCT) is the gold standard for 3D imaging in the clinic but requires additional radiation. The aim of this study was to create a software (XPAR) which obtains x-ray depth analysis and 3D modelling of the pulps of single-rooted teeth by converting the grey values in the original radiographs into numerical data. Materials and methods: Two single-rooted teeth were included in the experimental part of the study. Chicken fibula bone was preferred for alveolar bone simulation because it could simulate cortical and trabecular structures due to similarity. A total of four images (60kVp & 70kVp; single alveolar bone & double alveolar bone) were obtained. The aim of this experimental part is to test the repeatability and realism of the algorithm to be created for pulp modelling. Retrospectively, 31 single-rooted teeth with both periapical radiography and cone-beam computed tomography imaging were included in the retrospective part of the study. According to XPAR, depth increase areas were interpreted as root resorption and accessory canal. Depth decrease areas were evaluated as the transformation of the pulp from an elliptical to an oval form, pulp stone, bifid canal formation and the presence of thick alveolar bone. The diagnostic accuracy of XPAR application on pathological and morphological changes was evaluated by comparing the obtained results with CBCT. Results: 80% of the analyses diagnosed as bifurcation by XPAR application were supported by CBCT. This rate decreased to 27% in the diagnosis of transitions from elliptical to oval form. A total of 5 and 19 linear formations observed in the form of depth decrease and increase, respectively, were accepted as image errors in XPAR. Conclusion: Buccolingual bifid canal formations and pulp obliterations can be diagnosed with a rate of nearly 50% with the depth decrease finding obtained in XPAR application. Imaging errors caused by deformed detectors are typically observed as linear formations.

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