Improvement In Speed Research Articles

OPTIMIZING BSI MOBILE BANKING SERVICE THROUGH A THREE-DIMENSIONAL ANALYSIS : SOCIAL MEDIA, EXTERNAL AND INTERNAL

The rise of mobile banking in Indonesia underscores the strategic role of platforms like BSI Mobile in meeting customer expectations. This study examines user perceptions of BSI Mobile services using big data from X (formerly Twitter) as a real-time source of public opinion. Sentiment data from X was analyzed using the CRISP-DM framework, employing the Naive Bayes method for category and sentiment classification, which was then mapped onto the Importance-Performance Matrix (IPM). External analysis, using PESTEL, Porter's Five Forces, and competitor analysis, highlighting general, industry and competitor challenges. Internally, resource evaluations conducted through value chain analysis and the VRIO framework revealed that transitioning from BSI Mobile to the BYOND platform is a strategic step to strengthen technical and operational capabilities. The findings indicate an urgent need to enhance the app’s features and functionalities, followed by improvements in speed, reliability, security, and UI/UX. The study underscores the importance of strengthening both offline and online marketing strategies to promote BYOND’s features and functionalities, leveraging its Unique Selling Proposition (USP) as a sustained competitive advantage to drive digital adoption among both existing and new customers. Big data analytics helps identify improvements and track service performance.

Transition to GPU-based reconstruction for clinical organ-targeted PET scanner.

This article explores a new Graphics Processing Unit (GPU)-based techniques for efficient image reconstruction in organ-targeted Positron Emission Tomography (PET) scanners with planar detectors .
Approach: GPU-based reconstruction is applied to the Radialis low-dose organ-targeted PET technology, developed to overcome the issues of high exposure and limited spatial resolution inherent in traditional whole-body PET/CT (Computed Tomography) scans. The Radialis planar detector technology is based on four-side tileable sensor modules that can be seamlessly combined into a sensing area of the needed size, optimizing the axial field-of-view (AFOV) for specific organs, and maximizing geometric sensitivity. The article explores the transition from Central Processing Unit (CPU)-based Maximum Likelihood Expectation Maximization (MLEM) algorithms to a GPU-based counterpart, demonstrating a tenfold overall speedup in image reconstruction with a hundredfold improvement in iteration speed.
Main Results: Through standardized PET performance tests and clinical image analysis, this work demonstrates that GPU-based image reconstruction maintains diagnostic image quality while significantly reducing reconstruction times. The application of this technology, particularly in breast imaging using the Radialis Low-Dose Positron Emission Mammography (LD-PEM), significantly reduces exam times thus improving patient comfort and throughput in clinical settings.
Significance: This study represents an important advancement in the clinical workflow of PET imaging, providing insights into optimizing reconstruction algorithms to effectively leverage the parallel processing capabilities of GPUs.&#xD.

Improved Multi-Objective Strategy Diversity Chaotic Particle Swarm Optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior

With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns elite particle, general particle, and learning particle roles to the particles and assigns strategies for maintaining search, developing search, and learning search, respectively. In order to avoid falling into local optima, chaotic sequence perturbations are added during each iteration process avoiding premature falling into local optima. Finally, case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results show that the proposed algorithm is capable of achieving up to 23% microgrid load reduction and up to 20% improvement in convergence speed compared to other algorithms. It is superior to other algorithms in solving the problem of orderly charging and discharging of electric vehicles and has strong usability and feasibility.

Effects of Progressive Resistance Training After Hip Fracture: A Systematic Review

Hip fracture presents high morbidity, mortality, and healthcare costs. Some programs have focused on the effect of progressive strength work on post-hip fracture recovery. Therefore, the objective of this systematic review was to understand the effect of a progressive resistance training program on different variables in adults after hip fracture. This review includes randomized controlled trials that apply progressive strength programs in subjects after a hip fracture. The selected databases are PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), and Ebsco. A total of 7 studies were selected after screening. These studies were published between 2005 and 2022. Most of the research included adults over 65 years of age, showing a mean age of 77.80 years. In the majority of cases, the programs are applied between 3 and 12 months post-fracture. The most commonly applied intervention time is 3 months. The intervention time of the programs typically lasts for 3 months and includes 3–4 lower limb exercises involving, mainly hip and knee movements. All the investigations assess functional capacity and nearly all research the strength. It is shown that the intensity of strength work progresses from 60 to 80% of 1RM. Progressive strength training programs in post-hip fracture patients generally show an improvement in functional capacity, strength, balance, walking speed, flexibility, and cardiorespiratory fitness. However, the effects on independence, quality of life, self-reported physical disability, depression, and cognitive ability do not show conclusive results, and there is little research in this regard.

Rethinking density ratio estimation based hyper-parameter optimization.

Hyper-parameter optimization (HPO) aims to improve the performance of machine learning algorithms by identifying appropriate hyper-parameters. By converting the computation of expected improvement into density-ratio estimation problems, existing works use binary classifiers to estimate these ratio and determine the next point by maximizing the class posterior probabilities. However, these methods tend to treat different points equally and ignore some important regions, because binary classifiers are unable to capture more information about search spaces and highlight important regions. In this work, we propose a hyper-parameter optimization method by estimating ratios and selecting the next point using multi-class classifiers. First, we divide all samples into multiple classes and train multi-class classifiers. The decision boundaries of the trained classifiers allow for a finer partitioning of search spaces, offering richer insights into the distribution of hyper-parameters within search spaces. We then define an acquisition function as a weighted sum of multi-class classifiers' outputs, with these weights determined by samples in each class. By assigning different weights to each class posterior probability in our acquisition function, points within search spaces are no longer treated equally. Experimental results on three representative tasks demonstrate that our method achieves a significant improvement in immediate regrets and convergence speed.

Exploring Second-Order Nonlinear Optical Properties of 10-(3, 5-dinitrophenyl)-9-(4-nitrophenyl)-8, 9-dihydro-1H-pyreno [4, 5-d] imidazole Chromophore: Insights from a DFT Study

Finding effective nonlinear optical (NLO) materials for potential use in electro-optic applications is crucial. Among the limitations in high-speed data transmission, efficient signal transmission is a key problem. Organic materials furnish tunable nonlinear properties, ease of fabrication, and the ability to exhibit high second-order nonlinearities, making it crucial to address improvements in efficiency, speed, and reliability by introducing new organic materials to the evolving demand for cutting-edge technologies in photonics. Here we report a new novel D-π-A type chromophore 10-(3, 5-dinitrophenyl)-9-(4-nitrophenyl)-8, 9-dihydro-1H-pyreno [4, 5-d] imidazole chromophore and investigation of its second order nonlinear optical properties using density functional theory. The main target is to analyse nonlinear optical properties of the molecule and estimate NLO properties by ωB97XD/6311++G (d, p) method. Hyperpolarizability (β) is reported as 368.02 × 10−31 esu which is 98 times higher than standard urea β value (3.728 × 10–31 e.s.u) also dipole moment (μ), linear polarizability (α). UV Visible spectra shows optical behaviour of the materials, while HOMO-LUMO and MEP gives insight into intermolecular charge transfer. In addition, natural bond orbital analysis was carried out to confirm the interactions and charge transfer within the molecule. These findings emphasize utilization of the organic molecule for potential applications in photonics.

The Impact of Physiotherapy on Motor Function in a Child Recovering from Human Coronavirus NL63: A Case Report

Backgrounds: This case report aims to investigate potential neurological complications in children following Human Coronavirus NL63 (HCoV-NL63) infection and evaluate the effectiveness of 12-week physiotherapy intervention in improving motor function in this child. Methods: This case study examines a four-year-old male patient with multiple health dysfunctions following an HCoV-NL63 infection. A 12-week rehabilitation program including whole body vibration, lower extremities strengthening exercise, stairs climbing, balance exercise, and obstacles course was performed. The evaluation included the Pediatric Balance Scale (PBS), the Denver Developmental Screening Test (DDST), and the 10-Meter Walking Test (10MWT) to assess motor abilities and walking speed. Results: The participant demonstrated clinically meaningful improvement in motor function. The PBS score increased from 33 at baseline to 49 at 12 weeks. Walking speed increased from 0.56 meters/second at baseline to 1.43 meters/second. The DDST revealed improvement in fine motor skills such as copying a circle and drawing a person with three body parts Conclusions: This case report demonstrates that a 12-week physiotherapy program can lead to substantial improvements in motor function, including balance and walking speed, in a child with developmental delays following a severe HCoV-NL63 infection. However, the study also highlights the broader neurological and developmental populations. While respiratory symptoms are typically the focus of HCoV-NL63 treatment, this case report suggests that clinician should also consider potential long-term neurological effects, especially in severe cases. The child’s history of seizures and global developmental delay points to the need for more research into the virus’s impact on brain function and development.

IoT-Based a Control System for Household Waste Management Machines at Waste Disposal Sites using Human Machine Interface Method

To manage waste efficiently and sustainably, Integrating Algoritma Bellman-Ford in IoT-Based Control Systems for Household Waste Management Machines, the use of automation technologies based on the Internet of Things (IoT) is becoming increasingly relevant. The integration of HMI allows operators to manage and control the machine with precision and convenience. The synergy between IoT and HMI promises significant improvements in waste processing speed, accuracy, and safety. Developing a control system using the HMI method is not just a solution but an innovative approach that aims to find an effective solution in managing or destroying waste that requires less labor, so there is no need to increase assistance. The method that will be used in this research is the descriptive statistical method, namely, assessing the technical data needed to develop a control system that complies with the standard. This innovative approach is one of the solutions to the problem of labor shortages in landfills that simplify work and speed up the process of operating machines.

High-Accuracy Prediction of Mechanical Properties of Ni-Cr-Fe Alloys Using Machine Learning

Artificial intelligence-driven prediction models have emerged as powerful tools for estimating material properties with high accuracy, yet the preparation of training datasets often demands labor-intensive and time-consuming experimental procedures. Leveraging the Computational Materials Science (CMS) approach, this study utilizes phase transformation calculations and thermodynamic data to simulate the mechanical properties of Ni-Cr-Fe alloys. Using JMatPro software, mechanical properties (0.2% Proof Stress, Fracture Stress, and Young's Modulus) of 50 Ni-Cr-Fe alloy compositions were simulated across a temperature range of 540–920°C, generating a dataset of 1000 rows. This dataset was used to train an Artificial Neural Network (ANN) model, with 80% allocated for training and 20% for validation and testing. The trained AI model demonstrated robust predictive capabilities, achieving a 96,61% accuracy rate in forecasting material compositions with the desired thermo-physical properties at specific temperatures. To validate the model's reliability, predicted alloy compositions were re-simulated under identical conditions in JMatPro, confirming the high fidelity of the model's predictions. The results underscore the efficacy of Computational Materials Science (CMS)-generated datasets as a scalable and reliable source for training AI models in materials science. This study highlights the potential of integrating Computational Materials Science (CMS) and Machine Learning approaches to accelerate material design and development processes, delivering significant improvements in prediction speed and accuracy.

TinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference

Objective.In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself.Approach.This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed to address these challenges by converting Python-based AI models into platform-independent hardware description language code accelerators. Specifically designed for convolutional neural networks, tinyHLS seamlessly integrates into the AI developer's workflow in Python TensorFlow Keras. Our methodology leverages a template-based hardware compiler that ensures flexibility, efficiency, and ease of use. In this work, tinyHLS is first-published featuring templates for several layers of neural networks, such as dense, convolution, max and global average pooling. In the first version, rectified linear unit is supported as activation. It targets one-dimensional data, with a particular focus on time series data.Main results.The generated accelerators are validated in detecting atrial fibrillation on ECG data, demonstrating significant improvements in processing speed (62-fold) and energy efficiency (4.5-fold). Quality of code and synthesizability are ensured by validating the outputs with commercial ASIC design tools.Significance.Importantly, tinyHLS is open-source and does not rely on commercial tools, making it a versatile solution for both academic and commercial applications. The paper also discusses the integration with an open-source RISC-V and potential for future enhancements of tinyHLS, including its application in edge servers and cloud computing. The source code is available on GitHub:https://github.com/Fraunhofer-IMS/tinyHLS.

Adaptive Kalman filter-based time synchronization algorithm for civil aviation communication

Abstract The traditional synchronization algorithms often struggle to meet the stringent requirements of civil aviation communication. This paper proposes an adaptive time synchronization algorithm based on Kalman filtering (KF) to enhance the synchronization performance of civil aviation communication systems. Firstly, a dynamic clock model is established considering the characteristics of civil aviation communication, with clock offset and drift as state variables. Moreover, a step-by-step detection strategy is employed to adaptively adjust the detection range based on the statistical characteristics of synchronization errors, thereby improving the algorithm's robustness and convergence speed. To evaluate the algorithm's performance, a civil aviation communication system-level simulation platform is built based on ICAO standards, and various typical scenarios, such as take-off, landing, and cruising, are designed. Simulation results demonstrate that the proposed algorithm achieves significant improvements in synchronization accuracy, convergence speed, and robustness compared to classical synchronization algorithms based on phase-locked loops (PLL) and early late gates (ELD).

Efficient Tensor Robust Principal Analysis via Right-Invertible Matrix-Based Tensor Products

In this paper, we extend the definition of tensor products from using an invertible matrix to utilising right-invertible matrices, exploring the algebraic properties of these new tensor products. Based on this novel definition, we define the concepts of tensor rank and tensor nuclear norm, ensuring consistency with their matrix counterparts, and derive a singular value thresholding (*L,R SVT) formula to approximately solve the subproblems in the alternating direction method of multipliers (ADMM), which is integral to our proposed tensor robust principal component analysis (*LR TRPCA) algorithm. The computational complexity of the *LR TRPCA algorithm is O(k·(n1n2n3+p·min(n12n2,n1n22))) for k iterations. According to this complexity analysis, by using a right-invertible matrix that selects p rows from the n3 rows of the invertible matrix used in the tensor product with an invertible matrix, the computational load is approximately reduced to p/n3 of what it would be with an invertible matrix, highlighting the efficiency gain in terms of computational resources. We apply this efficient algorithm to grayscale video denoising and motion detection problems, where it demonstrates significant improvements in processing speed while maintaining comparable quality levels to existing methods, thereby providing a promising approach for handling multi-linear data and offering valuable insights for advanced data analysis tasks.

Advancing healthcare through laboratory on a chip technology: Transforming microorganism identification and diagnostics

In a recent case report in the World Journal of Clinical Cases , emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes. Laboratory-on-a-chip (LOC) technology has emerged as a transformative tool in health care, offering rapid, sensitive, and specific identification of microorganisms. This editorial provides a comprehensive overview of LOC technology, highlighting its principles, advantages, applications, challenges, and future directions. Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics, epidemiology, and food safety. Comparative studies have underscored the superiority of LOC technology over traditional methods, showcasing improvements in speed, accuracy, and portability. The future integration of LOC with biosensors, artificial intelligence, and data analytics promises further innovation and expansion. This call to action emphasizes the importance of continued research, investment, and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.

Accelerated dynamic light sheet microscopy: Unifying time-varying patterned illumination and low-rank and sparsity constrained reconstruction

Abstract Light Sheet Fluorescence Microscopy (LSFM) enables rapid and gentle 3D fluorescence imaging of dynamic processes over extended periods in translucent samples at the mesoscopic scale. However, its temporal resolution is constrained by the sequential acquisition of individual two-dimensional planes at varying depths, making it challenging to capture rapid dynamics such as the beating of a zebrafish heart. To address this limitation, we recently developed spatially modulated Selective Volume Illumination Microscopy (sm-SVIM), which utilizes a compressed sensing approach to reconstruct the entire imaging volume from measurements where multiple planes are illuminated simultaneously using spatially modulated light. Building on this advancement, we now introduce a novel spatio-temporal patterned illumination strategy and volume reconstruction method that incorporates low-rank and sparsity constraints, effectively leveraging the temporal and spatial redundancy present in sequential volumetric acquisitions. This method was applied to the volumetric imaging of embryonic zebrafish hearts, achieving an improvement in imaging speed of 4-fold compared to standard LSFM and a 2-fold improvement compared to traditional compressed sensing approaches, while preserving reconstruction accuracy and enabling the visualization of fast dynamic events with a resolution of a few tens of milliseconds. Our approach represents a step forward in enhancing the temporal resolution of LSFM for studying fast biological dynamics.

A recurrent YOLOv8-based framework for event-based object detection

Object detection plays a crucial role in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relying on conventional frame-based RGB sensors. However, these sensors face challenges such as motion blur and poor performance under extreme lighting conditions. Novel event-based cameras, inspired by biological vision systems, offer a promising solution with superior performance in fast-motion and challenging lighting environments while consuming less power. This work explores the integration of event-based cameras with advanced object detection frameworks, introducing Recurrent YOLOv8 (ReYOLOV8), a refined object detection framework that enhances a leading frame-based YOLO detection system with spatiotemporal modeling capabilities by adding recurrency. ReYOLOv8 incorporates a low-latency, memory-efficient method for encoding event data called Volume of Ternary Event Images (VTEI) and introduces a novel data augmentation technique based on Random Polarity Suppression (RPS) optimized for event-based sensors and tailored to leverage the unique attributes of event data. The framework was evaluated using two comprehensive event-based datasets Prophesee's Generation 1 (GEN1) and Person Detection for Robotics (PEDRo). On the GEN1 dataset, ReYOLOv8 achieved mAP improvements of 5%, 2.8%, and 2.5% across nano, small, and medium scales, respectively, while reducing trainable parameters by 4.43% on average and maintaining real-time processing speeds between 9.2 ms and 15.5 ms. For the PEDRo dataset, ReYOLOv8 demonstrated mAP improvements ranging from 9% to 18%, with models reduced in size by factors of 14.5 × and 3.8 × and an average speed improvement of 1.67 × . The results demonstrate the significant potential of bio-inspired event-based vision sensors when combined with advanced object detection frameworks. In particular, the ReYOLOv8 system effectively bridges the gap between biological principles of vision and artificial intelligence, enabling robust and efficient visual processing in dynamic and complex environments. The codes are available on GitHub at the following link https://github.com/silvada95/ReYOLOv8.

Advanced Quantitative Phase Microscopy Achieved with Spatial Multiplexing and a Metasurface.

Quantitative optical phase information provides an alternative method to observe biomedical properties, where conventional phase imaging fails. Phase retrieval typically requires multiple intensity measurements and iterative computations to ensure uniqueness and robustness against detection noise. To increase the measurement speed, we propose a single-shot quantitative phase imaging method with metasurface optics that can be conveniently integrated into conventional imaging systems with minimal modification. The improvement of the measurement speed is simultaneously made possible by combining deep learning with the transport-of-intensity equation. As a proof-of-concept, we demonstrate phase retrieval on both calibrated phase objects and biological specimens by using an imaging system integrated with our metasurface. When combined with the matched neural network, the system yields result with errors as low as 5% and increased space-bandwidth-product. A multitude of commercial applications can benefit from the compactness and rapid implementation of our proposed method.

SKL-YOLOv8s: an object detection method for coal gangue flow

ABSTRACT Coal gangue sorting is a critical process in the clean production of coal. To address the issues of high computational complexity and deployment challenges in coal gangue object detection tasks, this paper proposes the SML-Neck structure, which incorporates the KernelWarehouse, LSK module, and MPDIoU to enhance YOLOv8s, resulting in the lightweight SKL-YOLOv8s model. Experiments were conducted using a self-constructed dataset comprising 14,790 images of coal gangue. The experimental results demonstrate that the proposed method surpasses YOLOv5s, YOLOv5×, YOLOv7, YOLOv7×, SSD, Faster-RCNN, RT-DETR-l, RT-DETR-x, and YOLOv8s in both accuracy and speed. Compared to the original model, it achieves a 60.9% reduction in computational cost, a 49.5% improvement in inference speed, and a decrease in information transmission loss from 2.6 to 0.6. The GradGAM visualization algorithm reveals that the improved model concentrates its attention more effectively on the entire target, enhancing the network’s ability to distinguish between objects and background. This method contributes to advancing clean coal development and provides a significant reference for coal gangue sorting based on object detection.

A Comprehensive Review of the Diagnostics for Pediatric Tuberculosis Based on Assay Time, Ease of Operation, and Performance.

Pediatric tuberculosis (TB) is still challenged by several diagnostic bottlenecks, imposing a high TB burden in low- and middle-income countries (LMICs). Diagnostic turnaround time (TAT) and ease of operation to suit resource-limited settings are critical aspects that determine early treatment and influence morbidity and mortality. Based on TAT and ease of operation, this article reviews the evolving landscape of TB diagnostics, from traditional methods like microscopy and culture to cutting-edge molecular techniques and biomarker-based approaches. We examined the benefits of efficient rapid results against potential trade-offs in accuracy and clinical utility. The review highlights emerging molecular methods and artificial intelligence-based detection methods, which offer promising improvements in both speed and sensitivity. The review also addresses the challenges of implementing these technologies in resource-limited settings, where most pediatric TB cases occur. Gaps in the existing diagnostic methods, algorithms, and operational costs were also reviewed. Developing optimal diagnostic strategies that balance speed, performance, cost, and feasibility in diverse healthcare settings can provide valuable insights for clinicians, researchers, and policymakers.

A Narrative Review: How Does ABC Running Affect Speed Improvement in Young Athletes?

The purpose of this study is to analyze the effect of ABC Running on increasing speed in young athletes. This type of research uses a narrative review method. Literature search through databases such as PubMed, Scopus, WOS, Semantic Scholar and Google Scholar. Keywords such as ABC running OR speed enhancement OR athletic performance OR athletic basic coordination OR youth or young athletes. The criteria for articles are open access, scientific journals, in English or Indonesian, and published from 2019 to 2024. From the literature search, 6 articles were found that met the criteria for analysis. The results of this study are that ABC running is effective in increasing running speed in young athletes. In addition, a combination of exercises such as Running Mechanics and Ladder Drills, as well as visualization exercises were found to help improve athletes' technical skills. In conclusion, ABC running exercises are very useful in building a strong physical foundation, preventing injuries, and improving technical understanding for young athletes. This shows the importance of integrating ABC running training into young athletes' training programs to support success at higher levels. Therefore, it is recommended to continue and develop research and practice of ABC running training with various training models. Then a deeper analysis of the methodology such as developing training models, adding control variables, details on training programs and a large number of samples.

Leveraging descriptor learning and functional map-based shape matching for automated anatomical Landmarking in mouse mandibles.

Geometric morphometrics is used in the biological sciences to quantify morphological traits. However, the need for manual landmark placement hampers scalability, which is both time-consuming, labor-intensive, and open to human error. The selected landmarks embody a specific hypothesis regarding the critical geometry relevant to the biological question. Any adjustment to this hypothesis necessitates acquiring a new set of landmarks or revising them significantly, which can be impractical for large datasets. There is a pressing need for more efficient and flexible methods for landmark placement that can adapt to different hypotheses without requiring extensive human effort. This study investigates the precision and accuracy of landmarks derived from functional correspondences obtained through the functional map framework of geometry processing. We utilize a deep functional map network to learn shape descriptors, which enable us to achieve functional map-based and point-to-point correspondences between specimens in our dataset. Our methodology involves automating the landmarking process by interrogating these maps to identify corresponding landmarks, using manually placed landmarks from the entire dataset as a reference. We apply our method to a dataset of rodent mandibles and compare its performance to MALPACA's, a standard tool for automatic landmark placement. Our model demonstrates a speed improvement compared to MALPACA while maintaining a competitive level of accuracy. Although MALPACA typically shows the lowest RMSE, our models perform comparably well, particularly with smaller training datasets, indicating strong generalizability. Visual assessments confirm the precision of our automated landmark placements, with deviations consistently falling within an acceptable range for MALPACA estimates. Our results underscore the potential of unsupervised learning models in anatomical landmark placement, presenting a practical and efficient alternative to traditional methods. Our approach saves significant time and effort and provides the flexibility to adapt to different hypotheses about critical geometrical features without the need for manual re-acquisition of landmarks. This advancement can significantly enhance the scalability and applicability of geometric morphometrics, making it more feasible for large datasets and diverse biological studies.