Complex Task Research Articles

Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications

Abstract Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global Climate Models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here we focus on this problem by considering the two different emulation approaches introduced in the literature as perfect and imperfect, that we here refer to as Perfect Prognosis (PP) and Model Output Statistics (MOS), respectively, following the well-established downscaling terminology. In addition to standard evaluation techniques, we expand the analysis with methods from the field of eXplainable Artificial Intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differ between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM-dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs (hard transferability), due to the existence of GCM-dependent biases. This limits their applicability to build RCM ensembles. We conclude by giving prospects for future applications.

Visually navigated, ultrasound-guided, freehand percutaneous calyceal puncture – preclinical evaluation of a novel device to simplify a complex surgical task

Abstract Purpose Freehand sonographic percutaneous puncture techniques for the renal calyceal system are on the rise. Much time and practice are required to master this technique. Navigation-supported puncture aids could help make percutaneous access easier and faster. The aim of this study was to determine whether navigated puncture is feasible, and whether it is easier and faster compared to the conventional sonographic procedure. Materials & Methods We performed prospective free-hand percutaneous puncture on a porcine kidney model embedded in gelatin using the Xperius ultrasound system in combination with needle tracking with a Stimuplex Onvision hollow needle, compared to the conventional freehand ultrasound puncture technique. Punctures were performed by 25 participants using the ultrasound machine with or without needle tracking mode. Results Compared to the conventional approach, the navigated approach reduced the number of puncture procedures by 0.2 attempts(8%) in the experienced group. The time to calyx access was reduced by 15 seconds (26%). In the novice group, navigated puncture required 1.2 fewer attempts (36%) and the time to access was 70 seconds faster (61%). Conclusion Puncture using the novel device is feasible. The number of punctures and the time needed for successful access of the calyceal system was reduced by use of navigation in both groups, although the trend was significant only in the novice group. Navigation using needle tracking seems to help beginners perform sonographic percutaneous puncture at a level similar to experienced users. For a more precise analysis and validation, further studies are needed.

Harmonic Mean Optimizer (HMO) for global problems solving

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO’s applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains.

Effect of Music Listening on Neurocognition, Stress, and Performance in Neurosurgeons During Microsurgery

BackgroundStress in surgeons is well-known for its negative impact on neurocognition and surgical performance. Surgeons commonly use music for stress reduction, but its specific effects on neurosurgeons remain unclear. This study aims to investigate how listening to music during microsurgery affects neurocognitive functions, subjective anxiety, and stress levels in neurosurgeons. MethodConducted in two phases, the pilot phase involved 23 neurosurgeons completing a survey and rating compositions based on ragas of North Indian/Hindustani classical music for suitability during surgery. Five excerpts were selected for the main phase, which involved three neurosurgeons performing 16 surgeries, eight with and eight without music. Data collection included pre-surgery assessments of sleep, caffeine intake, subjective stress levels, perceived complexity of the surgery that was to be performed, and neurocognitive tasks. Post-surgery subjective stress levels, actual complexity, and surgeon experiences with music were recorded. ResultsWhile there was a trend toward reduced stress and perceived complexity with music, statistical significance was not reached. Neurocognitive tasks showed non-significant improvement with music, with a significant (at p < 0.001) speed-accuracy trade-off noted in response inhibition. ConclusionMusic appears to reduce subjective stress and task complexity, influencing neurocognition, particularly through speed-accuracy trade-offs. Further research is needed to understand its mechanisms and appropriate contexts for use before widespread recommendation.

D2 dopamine receptor expression, reactivity to rewards, and reinforcement learning in a complex value-based decision-making task.

Different dopamine subtypes have opposing dynamics at post-synaptic receptors, with the ratio of D1 to D2 receptors determining the relative sensitivity to gains and losses, respectively, during value-based learning. This effective sensitivity to different reward feedback interacts with phasic dopamine levels to determine the effectiveness of learning, particularly in dynamic feedback situations where frequency and magnitude of rewards need to be integrated over time to make optimal decisions. We modeled this effect in simulations of the underlying basal ganglia pathways and then tested the predictions in individuals with a variant of the human dopamine receptor D2 (DRD2; -141C Ins/Del and Del/Del) gene that associates with lower levels of D2 receptor expression (N=119) and compared their performance in the Iowa Gambling Task (IGT) to non-carrier controls (N=319). Ventral striatal (VS) reactivity to rewards was measured in the Cards task with fMRI. DRD2 variant carriers made less effective decisions than non-carriers, but this effect was not moderated by VS reward reactivity as is hypothesized by our model. These results suggest that the interaction between dopamine receptor subtypes and reactivity to rewards during learning may be more complex than originally thought.

Modern Trends in Development of Educational and Scientific Discipline ‘Public Health and Healthcare’: Its Methodological Role and Integrative Function

INTRODUCTION: In modern conditions of social development, there is a change of emphasis in definitions concerning not only public, but also individual health. Preserving and strengthening the health of the population is a complex task that implies the interaction of various specialists. This integration, the complexity of interaction in issues concerning preserving and strengthening the health of the population, should undoubtedly be reflected in the training of specialists, and, first of all, in higher medical education. AIM: To determine the role of the discipline ‘Public Health and Healthcare’ (PHH) in teaching population health issues and in the integration of departments of various profiles in the study of individual, group and population health in modern conditions. The article presents a discussion about modern trends in the development of the educational and scientific PHH discipline based on the opinions of students, teachers and the experience of Sechenovskiy University. Thus, students of profiles ‘General Medicine’, ‘Pediatrics’, ‘Medical and Preventive Care’ think that studying public health issues is the task of doctors of all clinical specialties (69.4%–76.7%), of hygienists ( 60.0%), of epidemiologists ( 30.0%). Integration in the teaching of PHH issues, according to teachers of PHH departments, in such types of activities as academic work, educational work, general scientific projects, joint printed works, textbooks and teaching aids, joint innovative projects, is mainly carried out with teachers of preventive educational disciplines, to a much lesser extent with teachers of clinical and fundamental disciplines. CONCLUSION: An integrative approach to definitions, terms, and concepts is needed in the study of the health of individuals, population groups and population with a significant variety of disciplines and departments teaching them. The methodological role in teaching these issues is the prerogative of the PHH discipline.

Spatial preference behavior of robo-pigeons induced by electrical stimulus targeting fear nuclei.

Numerous studies have confirmed that stimulating the mid-brain motor nuclei can regulate movement forcibly for robo-pigeons, but research on behavior modulation using non-motor nuclei is scarce. In this study, we constructed a spatial preference behavior by stimulating the stratum griseum periventriculare (SGP), a nucleus correlated with fear and escape, for robo-pigeons. The study was carried out in a square-enclosed experimental field, with a designated box serving as the 'safe' area for the robo-pigeons. If the robo-pigeon exits this area, the SGP will be stimulated. After a brief training period, the robo-pigeons will have a clear spatial preference for the box. The result from five pigeons has shown that, after simple training, the animals develop a spatial preference for the box. They can quickly return to the box in any situation when the SGP is stimulated, with a success rate exceeding 80% (89.0 ± 6.5%). Moreover, this behavior is highly stable and remains consistent, unaffected by changes in the location of the box or the interference box. The results prove that using the electrical stimulus could enable animals to accomplish more complex tasks. It may offer a novel approach to regulating pigeon behavior and further advance the study of cyborg animals.

Beyond simulation: Unlocking the frontiers of humanoid robot capability and intelligence with Pepper's open-source digital twin

This research paper presents a high-fidelity, open-source digital-twin of the Pepper robot developed within the framework of the Robot Operating System 2 (ROS 2) for better simulation realism in complex tasks of machine learning. We developed a dedicated, custom ROS 2 package with modern simulation tools, such as Gazebo Sim, MoveIt 2, Rviz2, that brings complete, realistic environments in line with the exact behaviors and interactions of robots in reality. Better accuracy of the physical movement of Pepper robot's simulation was shown on the digital twin, validated by the Choregraphe software and real robot performance, to be a strong platform of collaboration and further research by the community. This development greatly pushes the envelope of human-like humanoid robotics further by offering a scaled, flexible, and plausible training environment conducive to integrating complex algorithms of robot learning and interaction capabilities.

Crafting clarity: Leveraging large language models to decode consumer reviews

Large Language Models (LLMs) have emerged as powerful tools for understanding consumer perceptions and extracting insights from unstructured textual data. This study investigates the effectiveness of LLMs in comprehending consumer opinions, particularly in service industries. We fine-tuned four prominent LLMs—Falcon-7B, MPT-7B, GPT-2, and BERT—using 1,031,478 consumer reviews and assessed their ability to identify topics, emotions, and sentiments. Our results indicate that Falcon-7B excels in accuracy and reliability for complex tasks. This study is the first, to our knowledge, to fine-tune Large Language Models (LLMs) specifically for consumer data, showcasing the efficacy of attention mechanisms in extracting valuable insights. Our findings offer strategic decision-making insights for the service industry and underscore the transformative potential of LLMs in business intelligence from consumer feedback.

A survey of ontology-enabled processes for dependable robot autonomy

Autonomous robots are already present in a variety of domains performing complex tasks. Their deployment in open-ended environments offers endless possibilities. However, there are still risks due to unresolved issues in dependability and trust. Knowledge representation and reasoning provide tools for handling explicit information, endowing systems with a deeper understanding of the situations they face. This article explores the use of declarative knowledge for autonomous robots to represent and reason about their environment, their designs, and the complex missions they accomplish. This information can be exploited at runtime by the robots themselves to adapt their structure or re-plan their actions to finish their mission goals, even in the presence of unexpected events. The primary focus of this article is to provide an overview of popular and recent research that uses knowledge-based approaches to increase robot autonomy. Specifically, the ontologies surveyed are related to the selection and arrangement of actions, representing concepts such as autonomy, planning, or behavior. Additionally, they may be related to overcoming contingencies with concepts such as fault or adapt. A systematic exploration is carried out to analyze the use of ontologies in autonomous robots, with the objective of facilitating the development of complex missions. Special attention is dedicated to examining how ontologies are leveraged in real time to ensure the successful completion of missions while aligning with user and owner expectations. The motivation of this analysis is to examine the potential of knowledge-driven approaches as a means to improve flexibility, explainability, and efficacy in autonomous robotic systems.

Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

Sustainable manufacturing practices are crucial in job shop scheduling (JSS) to enhance the resilience of production systems against resource shortages and regulatory changes, contributing to long-term operational stability and environmental care. JSS involves rapidly changing conditions and unforeseen disruptions that can lead to inefficient resource use and increased waste. However, by addressing these uncertainties, we can promote more sustainable operations. Reinforcement learning-based job shop scheduler agents learn through trial and error by receiving scheduling decisions feedback in the form of a reward function (e.g., maximizing machines working time) from the environment, with their primary challenge being the handling of dynamic reward functions and navigating uncertain environments. Recently, Reward Machines (RMs) have been introduced to specify and expose reward function structures through a finite-state machine. With RMs, it is possible to define multiple reward functions for different states and switch between them dynamically. RMs can be extended to incorporate domain-specific prior knowledge, such as task-specific objectives. However, designing RMs becomes cumbersome as task complexity increases and agents must react to unforeseen events in dynamic and partially observable environments. Our proposed Ontology-based Adaptive Reward Machine (ONTOADAPT-REWARD) model addresses these challenges by dynamically creating and modifying RMs based on domain ontologies. This adaptability allows the model to outperform a state-of-the-art baseline algorithm in resource utilization, processed orders, average waiting time, and failed orders, highlighting its potential for sustainable manufacturing by optimizing resource usage and reducing idle times.

Enterprise Malware Detection using Digital Forensic Artifacts and Machine Learning

Malware detection is a complex task. Numerous log aggregation solutions and intrusion detection systems can help find anomalies within a host or a network and detect intrusions, but they require precise calibration, skilled analysts, and cutting-edge technology. In addition, processing host-based data is challenging, as every log, event, and configuration can be analyzed. In order to obtain trusted information about a host state, the analysis of a computer’s memory can be performed, but obtaining the data from acquisition and performing the analysis can be challenging. To address this limitation, this paper proposes to collect artifacts within a network environment. This approach involves remotely gathering memory-based and disk-based artifacts from a simulated enterprise network using Velociraptor. The data was then processed using three machine learning algorithms to detect the malware samples against regular user activity generated with a user simulation tool for added realism. With this method, Random Forest and Support Vector Machine achieved a perfect classification of 41 malware samples.

Enterprise Malware Detection using Digital Forensic Artifacts and Machine Learning

Malware detection is a complex task. Numerous log aggregation solutions and intrusion detection systems can help find anomalies within a host or a network and detect intrusions, but they require precise calibration, skilled analysts, and cutting-edge technology. In addition, processing host-based data is challenging, as every log, event, and configuration can be analyzed. In order to obtain trusted information about a host state, the analysis of a computer’s memory can be performed, but obtaining the data from acquisition and performing the analysis can be challenging. To address this limitation, this paper proposes to collect artifacts within a network environment. This approach involves remotely gathering memory-based and disk-based artifacts from a simulated enterprise network using Velociraptor. The data was then processed using three machine learning algorithms to detect the malware samples against regular user activity generated with a user simulation tool for added realism. With this method, Random Forest and Support Vector Machine achieved a perfect classification of 41 malware samples.

Automation Inspection in Metal Fabrication: Enhancing Sheet Metal Forming Processes with Automation, Machine Vision and Image Correlation for Quality Assurance

This review emphasizes the evolving need for automated inspection in metal fabrication processes due to the increasing complexity of design advancements over the years. The study explores various defect detection algorithms and evaluates their effectiveness in enhancing the accuracy and reliability of the inspection process. Machine vision plays a crucial role in this context, contributing significantly to the precision of the inspection process in metal fabrication. Its ability to handle complex tasks ensures a thorough assessment of manufactured components. The paper also explores the use of digital image correlation (DIC) as a key tool in quality assurance for metal fabricated products. This technique provides detailed insights, enabling a thorough understanding of structural integrity and defect identification. By integrating insights on automated inspection through defect detection algorithms, machine vision and DIC, this review aims to advance quality assurance methodologies in the ever-evolving field of metal fabrication.

An Effective Federated Object Detection Framework with Dynamic Differential Privacy

The proliferation of data across multiple domains necessitates the adoption of machine learning models that respect user privacy and data security, particularly in sensitive scenarios like surveillance and medical imaging. Federated learning (FL) offers a promising solution by decentralizing the learning process, allowing multiple participants to collaboratively train a model without sharing their data. However, when applied to complex tasks such as object detection, standard FL frameworks can fall short in balancing the dual demands of high accuracy and stringent privacy. This paper introduces a sophisticated federated object detection framework that incorporates advanced differential privacy mechanisms to enhance privacy protection. Our framework is designed to work effectively across heterogeneous and potentially large-scale datasets, characteristic of real-world environments. It integrates a novel adaptive differential privacy model that strategically adjusts the noise scale during the training process based on the sensitivity of the features being learned and the progression of the model’s accuracy. We present a detailed methodology that includes a privacy budget management system, which optimally allocates and tracks privacy expenditure throughout training cycles. Additionally, our approach employs a hybrid model aggregation technique that not only ensures robust privacy guarantees but also mitigates the degradation of object detection performance typically associated with DP. The effectiveness of our framework is demonstrated through extensive experiments on multiple benchmark datasets, including COCO and PASCAL VOC. Our results show that our framework not only adheres to strict DP standards but also achieves near-state-of-the-art object detection performance, underscoring its practical applicability. For example, in some settings, our method can lower the privacy success rate by 40% while maintaining high model accuracy. This study makes significant strides in advancing the field of privacy-preserving machine learning, especially in applications where user privacy cannot be compromised. The proposed framework sets a new benchmark for implementing federated learning in complex, privacy-sensitive tasks and opens avenues for future research in secure, decentralized machine learning technologies.

An Effective Federated Object Detection Framework with Dynamic Differential Privacy

The proliferation of data across multiple domains necessitates the adoption of machine learning models that respect user privacy and data security, particularly in sensitive scenarios like surveillance and medical imaging. Federated learning (FL) offers a promising solution by decentralizing the learning process, allowing multiple participants to collaboratively train a model without sharing their data. However, when applied to complex tasks such as object detection, standard FL frameworks can fall short in balancing the dual demands of high accuracy and stringent privacy. This paper introduces a sophisticated federated object detection framework that incorporates advanced differential privacy mechanisms to enhance privacy protection. Our framework is designed to work effectively across heterogeneous and potentially large-scale datasets, characteristic of real-world environments. It integrates a novel adaptive differential privacy model that strategically adjusts the noise scale during the training process based on the sensitivity of the features being learned and the progression of the model’s accuracy. We present a detailed methodology that includes a privacy budget management system, which optimally allocates and tracks privacy expenditure throughout training cycles. Additionally, our approach employs a hybrid model aggregation technique that not only ensures robust privacy guarantees but also mitigates the degradation of object detection performance typically associated with DP. The effectiveness of our framework is demonstrated through extensive experiments on multiple benchmark datasets, including COCO and PASCAL VOC. Our results show that our framework not only adheres to strict DP standards but also achieves near-state-of-the-art object detection performance, underscoring its practical applicability. For example, in some settings, our method can lower the privacy success rate by 40% while maintaining high model accuracy. This study makes significant strides in advancing the field of privacy-preserving machine learning, especially in applications where user privacy cannot be compromised. The proposed framework sets a new benchmark for implementing federated learning in complex, privacy-sensitive tasks and opens avenues for future research in secure, decentralized machine learning technologies.

ОСОБЛИВОСТІ ВИКОРИСТАННЯ СУЧАСНИХ ІНФОРМАЦІЙНОКОМУНІКАЦІЙНИХ ТЕХНОЛОГІЙ У ВИЩІЙ МАТЕМАТИЧНІЙ ОСВІТІ

The article discusses the relevance of using information and communication technologies (ICT) in higher mathematical education within the context of the contemporary educational environment characterized by rapid technological changes. It is noted that effective utilization of ICT, such as electronic journals, multimedia technologies, testing systems, and computer mathematics systems, contributes to the improvement of the teaching and learning process. Emphasis is placed on the necessity of reviewing and modifying teaching methodologies for mathematical disciplines to integrate innovative solutions. Furthermore, the use of information and communication technologies in higher mathematical education is deemed highly relevant due to the rapid development of the modern technological environment and the needs of the contemporary job market. The implementation of electronic journals, multimedia technologies, testing systems, and computer mathematics not only enhances the efficiency of teaching mathematical disciplines but also helps create a solid foundation for the future professional success of students. Electronic journals serve not only as convenient tools for administrative processes but also as platforms for active interaction among students, teachers, and administration, fostering the development of academic community and stimulating discussions and collaboration. Multimedia technologies, such as video lessons, interactive exercises, and simulations, provide students with opportunities to better grasp material and understand complex concepts, opening up new possibilities for personalized learning and adaptation to various learning styles. Testing systems, particularly those based on interactive platforms, enable instructors to effectively monitor students' knowledge and track their progress, as well as provide opportunities for personalized learning through automatic adaptation of task complexity Computer mathematics systems, including software packages for numerical and symbolic computations, become an integral part of modern mathematical education, empowering students to efficiently solve complex mathematical problems and explore new concepts, thus stimulating their creative potential and innovative approach to problem-solving. Together with educational platforms and distance learning systems, these tools contribute to the modernization and optimization of the educational process, leading to the improvement of the quality of higher mathematical education and the preparation of qualified professionals for the contemporary world.

The effects of task complexity on linguistic complexity and perception in English native speakers’ and L2 speakers’ argumentative writing

This study investigates how task complexity affects native speakers’ and second language (L2) learners’, and experts’ perceptions of task difficulty in writing tasks. It also explores how task complexity influences English native speakers’ and L2 learners’ linguistic complexity in their writing. Ninety participants performed one simple and one complex argumentative writing task manipulated by task complexity (± elements and reasoning demands) and counterbalanced. Their essays were analyzed using linguistic complexity metrics. The results showed that English native speakers, L2 learners, and experts perceived the complex task as requiring more mental effort, being more stressful, and creating more planning and writing time pressure than the simple task. The native speakers demonstrated greater overall linguistic complexity than the L2 learners in the two writing tasks. However, both English native speakers and L2 learners produced longer T-units and more diverse vocabulary in the complex task than in the simple task but also produced more syntactically similar structures in the complex task. Significant interaction effects of group (L1 and L2) and task (± elements and reasoning demands) were found for five linguistic complexity metrics. Theoretical and methodological implications are discussed in light of Robinson’s cognition hypothesis and Hayes’s writing model.

Concerto of movement: how expertise shapes the synergistic control of upper limb muscles in complex motor tasks with varying tempo and dynamics

Objective. This research aims to reveal how the synergistic control of upper limb muscles adapts to varying requirements in complex motor tasks and how expertise shapes the motor modules. Approach. We study the muscle synergies of a complex, highly skilled and flexible task—piano playing—and characterize expertise-related muscle-synergy control that permits the experts to effortlessly execute the same task at different tempo and force levels. Surface EMGs (28 muscles) were recorded from adult novice (N = 10) and expert (N = 10) pianists as they played scales and arpeggios at different tempo-force combinations. Muscle synergies were factorized from EMGs. Main results. We found that experts were able to cover both tempo and dynamic ranges using similar synergy selections and achieved better performance, while novices altered synergy selections more to adapt to the changing tempi and keystroke intensities compared with experts. Both groups relied on fine-tuning the muscle weights within specific synergies to accomplish the different task styles, while the experts could tune the muscles in a greater number of synergies, especially when changing the tempo, and switch tempo over a wider range. Significance. Our study sheds light on the control mechanism underpinning expertise-related motor flexibility in highly skilled motor tasks that require decade-long training. Our results have implications on musical and sports training, as well as motor prosthetic design.

Decision Support System for Motorcycle Credit Sales Using the Multi-Factor Evaluation Process Method at CV. Lestari Motorindo, Palembang Branch

CV. Lestari Motorindo is a company operating under PT. Astra CV. Lestari Motorindo, a subsidiary of PT. Astra Indonesia, specializes in motorcycle sales. Their credit purchase process involves gathering customer data for submission to leasing companies for verification, customer surveys, and creditworthiness assessment. Determining creditworthiness for motorcycle purchases is a complex task. Therefore, a Decision Support System (DSS) is needed to assist the marketing and admin teams in providing accurate creditworthiness recommendations.This research employs the Multi-Factor Evaluation Process (MFEP) method as the decision-making framework. MFEP is chosen due to its ability to streamline the process and enhance data validation, eliminating discrepancies between leasing companies and dealers. The MFEP-based DSS for credit applications is an accessible system for both parties, ensuring that decisions made by the DSS are valid for both leasing companies and dealers.