Multidisciplinary Research in Computing Information Systems
Multidisciplinary Research in Computing Information Systems (MRCIS) is a peer-reviewed academic journal that publishes high-quality research on a wide range of topics within the fields of computing, information systems, and their interdisciplinary applications. The journal aims to provide a platform for the dissemination of innovative research, new methodologies, and advancements in computing technologies and their integration into various sectors. It covers a diverse array of topics including but not limited to: Software engineering Artificial intelligence and machine learning Data science and big data analytics Cloud computing and virtualization Information security and cryptography Human-computer interaction Network and communication systems Internet of Things (IoT) Computational models and simulations Business information systems Healthcare and e-health informatics Smart technologies and systems integration The journal encourages submissions from a variety of disciplines that explore the intersection of computing and information systems, fostering interdisciplinary research that drives technological advancements. It provides a comprehensive resource for researchers, practitioners, and academics interested in cutting-edge developments in the field of computing and its practical applications across different industries.
All articles published in the Multidisciplinary Research in Computing Information Systems are licensed under an open-access model. Authors retain full copyright and grant the journal the right of first publication. The content can be freely accessed, distributed, and reused for non-commercial purposes, provided proper citation is given to the original work.
Slice Consistent Three Dimensional Modeling for CT Reconstruction
Most CT denoising methods operate on individual slices and do not explicitly exploit inter-slice continuity. Based on hybrid convolution–attention designs exemplified by CTLformer, this paper proposes a slice-consistent reconstruction approach that extends convolutional and self-attention operations to three-dimensional contexts. Local convolutions capture intra-slice texture, while attention mechanisms aggregate information across adjacent slices. The model is evaluated on volumetric CT datasets containing 7,200 3D scans and approximately 180,000 slices. Compared with 2D CNNs, 3D CNNs, and transformer-based volumetric models, the proposed method improves volumetric PSNR by 1.0–1.6 dB and reduces inter-slice intensity discontinuities
Copyright (c) 2026 Michael Chen, Daniel Rodriguez, Emily Johnson (Author) https://creativecommons.org/licenses/by/4.0
Dynamic Driver Compartmentalization Using On-Demand Memory Domains
Device drivers contribute disproportionately to kernel vulnerabilities due to their large attack surface. This work proposes an on-demand driver compartmentalization mechanism that assigns temporary memory domains to active driver routines. Using lightweight memory remapping and PKS-controlled regions, the approach confines driver faults without interrupting global kernel execution. Evaluated across 14 commodity drivers, the system prevents privilege-escalation exploits in 11 CVEs and contains the impact of remaining attacks. Performance overhead stays within 4.1% on I/O-intensive workloads. This dynamic model demonstrates an effective balance between strong isolation and operational flexibility.
Copyright (c) 2026 Jonas Müller, Hannah Weiss, Leon Schneider (Author) https://creativecommons.org/licenses/by/4.0
ThreatSense: Neuro-Symbolic Reasoning for Autonomous Threat Assessment in Unmanned Systems
Autonomous unmanned systems increasingly operate in adversarial environments where real-time threat recognition and response are safety-critical imperatives. This paper presents ThreatSense, a novel neuro-symbolic AI framework that integrates deep neural perception with symbolic logic reasoning for autonomous threat assessment in unmanned aerial vehicle (UAV) networks. Unlike conventional intrusion detection approaches that rely solely on either pattern-based learning or handcrafted rule systems, ThreatSense unifies both paradigms through a layered inference architecture: a neural perception module for multi-modal sensor feature extraction, a symbolic knowledge engine encoding domain-specific threat ontologies in first-order logic, and a joint inference layer mediating probabilistic symbol grounding. ThreatSense is evaluated against four representative threat classes including GPS spoofing, denial-of-service, adversarial sensor perturbation, and black hole routing attacks. Experimental results demonstrate a detection accuracy of 97.3%, a false positive rate (FPR) of 1.8%, and an average inference latency of 38 ms, outperforming deep learning baselines by 4.8 percentage points in accuracy while maintaining full symbolic interpretability. The framework advances explainable and computationally efficient threat intelligence for safety-critical autonomous platforms.
Copyright (c) 2026 Tobias Krüger , Elena Petrova (Author) https://creativecommons.org/licenses/by/4.0
A Study on the Relationship between On-site Management Personnel Turnover and Accident Rate in Construction Enterprises
The stability of on-site management personnel has a significant impact on safe production, but its effect has long lacked systematic quantitative research. This study conducts an empirical analysis on the relationship between on-site management personnel turnover and accident rate. The study uses project data from a construction enterprise over 36 consecutivemonths, covering 92 construction projects, 1,740 on-site management personnel, and 286 accident records. Personnel turnover is measured by the number of management personnel changes within the project cycle, and safety performance is represented by accident rate and severity grading indicators. A negative binomial regression model is used to analyze the impact of personnel turnover on accident frequency. The results show that projects with frequent management personnel changes have significantly higher accident rates than projects with stable personnel. This study provides a quantitative basis for construction enterprises to strengthen the stability of on-site management personnel.
Negative binomial regressionNegative binomial regression
Copyright (c) 2026 Hiroshi Tanaka , Yuki Nakamura , Kenji Sato (Author) https://creativecommons.org/licenses/by/4.0
An Event Sequence Study on the Organizational Capability Changes of Manufacturing Engineering and Technical Teams
During equipment upgrades and process adjustments, changes in the organizational capability of manufacturing engineering and technical teams directly impact production efficiency. This study analyzes the temporal relationship between key organizational events and production performance, focusing on the process of organizational capability change. The study uses 30 months of organizational adjustment and project execution data from an equipment manufacturing company, with a sample containing 4,960 organizational event records and corresponding production cycle and rework rate indicators. Event sequence analysis is used to identify organizational adjustment stages and compare the differences in production performance before and after the adjustment. The results show that within 3 to 6 months after the organizational restructuring, the average production cycle of key processes shortens by 7.4%, and the rework rate decreases by approximately 9%. The study indicates that organizational capability changes have quantifiable stage characteristics.
Copyright (c) 2026 Wei Ming Tan , Jun Kai Lim , Siti Nur Aisyah Rahman (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Competency Structure and Employee Behavioral Characteristics in Hotel Frontline Positions
Improving the competency level of frontline positions is an important way for hotels to improve service quality and operational efficiency. This study systematically analyzes the ability differences implied in employee behavioral data, focusing on the structural characteristics of jobcompetencies. The study selected multi-source operational data from over 6,500 employees of a chain hotel, covering attendance, scheduling, performance appraisal, customer complaints, and work order processing, totaling over 19 million records. A behavioral indicator system was constructed, and cluster analysis was used to identify the characteristic differences among different performance groups. The results show that high-performing employees significantly outperformed other groups in peak-hour response speed, complaint handling efficiency, and cross job collaboration. Based on the analysis results, job competency profiles were created and used in recruitment and probationary period evaluations, resulting in a significant increase in the probationary period pass rate for new employees. The research indicates that using behavioral data to characterize competency structure helps optimize hotel human resource decisions.
Copyright (c) 2026 Emily R. Thompson , Daniel K. Rodriguez , Michael J. Anderson (Author) https://creativecommons.org/licenses/by/4.0
Uncertainty-Aware Pedestrian Re-Identification in Autonomous Driving Perception Pipelines
Pedestrian re-identification models trained on one city often degrade when deployed in another due to changes in camera placement, lighting, and pedestrian appearance patterns. Building on CLIP-based uncertainty modal modeling, this work proposes an uncertainty-weighted adaptation strategy that transfers vision–language embeddings across cities while down weighting unreliable pseudo-labels. The method combines (i) entropy-based sample filtering, (ii) uncertainty-aware class prototype refinement, and (iii) consistency regularization between image and text embeddings. Experiments are conducted on cross-city splits constructed from autonomous driving data, totaling 320,000 images and 38,000 identities across three urban domains. Compared with OSNet, TransReID, and CLIP-derived ReID baselines under standard unsupervised domain adaptation settings, the proposed method improves target-domain mAP by 4.0%–6.3% and rank-1 accuracy by 3.2%–5.1%, with the largest gains observed in nighttime and rain subsets.
Copyright (c) 2026 Marco Bianchi, Alessandro Rossi , Giulia Conti , Lorenzo De Santis (Author) https://creativecommons.org/licenses/by/4.0
Beyond Deterministic Alignment: Probabilistic VisionLanguage Representations for Pedestrian Re-Identification
Uncertainty estimation plays an important role in perception tasks for autonomous systems. Motivated by CLIP-based uncertainty modal modeling, this study formulates pedestrian representations as probabilistic embeddings rather than fixed feature vectors. A variational learning strategy is employed to model both data-dependent and model-related uncertainty across visual and textual modalities. The approach is evaluated on three pedestrian re-identification benchmarks containing over 95,000 identities and 780,000 images. Performance is compared with deterministic baselines, including ResNet-based ReID, Transformer-based ReID, and CLIP derived embedding models. Results show average gains of 3.1% in rank-1 accuracy and 3.7% in mAP, along with a reduction of expected calibration error by 18%–24% in occlusion-heavy scenarios.
Copyright (c) 2026 Hiroshi Tanaka , Yuki Nakamura , Kenji Sato , Ayumi Kobayashi (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Fusion of Traces and Logs via Graph Attention for Microservice Anomaly Localization
Microservice architectures have become increasingly prevalent in modern cloud-native applications due to their flexibility and scalability benefits. However, the distributed nature of microservices introduces significant challenges in anomaly detection and root cause localization, as failures can propagate across multiple services and manifest differently in various monitoring data sources. Traditional approaches that rely on single-modal data such as metrics, logs, or traces often fail to capture the complete picture of system behavior, leading to high false positive rates and missed anomalies. This paper proposes an adaptive fusion framework that integrates distributed traces and application logs through Graph Attention Networks (GAT) to achieve accurate microservice anomaly localization. The proposed approach constructs a unified graph representation that captures both the structural dependencies between services from traces and the semantic information from logs. By leveraging the attention mechanism, our method adaptively assigns importance weights to different data modalities and service interactions, enabling the model to focus on critical anomaly patterns while suppressing noise. Experimental evaluation on benchmark microservice systems demonstrates that the proposed framework achieves superior performance in both anomaly detection and root cause localization compared to existing single modal and naive multi-modal approaches, with detection accuracy exceeding 92% and localization precision reaching 87%.
Copyright (c) 2026 Yuchen Tang , Zhiyu Qian , Aleksandar Petrović (Author) https://creativecommons.org/licenses/by/4.0
Deep Sequence Models For Real-Time Journal Entry Anomaly Detection In Financial Reporting
Financial reporting integrity remains a critical concern for auditors, regulatory bodies, and stakeholders as the complexity and volume of accounting transactions continue to escalate. Traditional rule-based audit sampling techniques face increasing challenges in identifying fraudulent activities and accounting errors concealed within massive general ledger datasets. This research explores the application of deep sequence models, particularly Long Short-Term Memory (LSTM) networks and their variants, for real-time anomaly detection in journal entry data. By treating journal entries as temporal sequences and leveraging the memory capabilities ofrecurrent architectures, our approach captures intricate patterns and dependencies that characterize normal accounting behavior. The proposed framework addresses key challenges including variable-length transaction structures, class imbalance in anomaly datasets, and the need for unsupervised or semi-supervised learning paradigms where labeled fraudulent instances remain scarce. We demonstrate how LSTM's sophisticated gating mechanisms enable selective information retention across extended temporal horizons, while stacked architectures learnhierarchical representations of transaction patterns. The methodology integrates preprocessing techniques to handle categorical and numerical features, followed by deep learning architectures that model both short-term transactional patterns and long-range dependencies across accounting periods. Experimental validation using synthetic and real-world financial datasets shows that sequence-based deep learning models achieve superior performance metrics compared to conventional statistical methods and shallow machine learning techniques, with particular improvements in separating anomalous transactions based on reconstruction error distributions. The reconstruction-based detection paradigm demonstrates clear discrimination between normal entries exhibiting low reconstruction errors and fraudulent entries with significantly elevated error magnitudes. This work contributes to the growing intersection of artificial intelligence and forensic accounting by providing a scalable, adaptable solution for automated anomaly detection in enterprise financial systems.
Copyright (c) 2026 Zirui Tang, Haonan Qiu , Lukas Steiner (Author) https://creativecommons.org/licenses/by/4.0
A cross-domain OTA service quality governance framework based on policy layering and service level objectives (SLOs)
OTA updates used in aviation, rail and wind-power systems must meet different delay and availability limits, which makes a single update rule hard to apply. This study proposes an SLO-based method that gives security patches, feature upgrades and configuration changes their own timing targets and applies slowdown or pause steps when these targets are at risk. The method was tested for 90 days on three independent systems, with 3,462 update jobs collected. Under the proposed rules, the overall SLO failure rate fell from 7.4% to 1.2%, and peak CPU and memory use fell by 22.9%. These results show that simple SLO checks can coordinate update timing across several critical sectors while reducing resource peaks. The study used simulated avionics and rail setups and one OTA design, so further tests with real fleets and longer time windows are needed.
Copyright (c) 2026 Eva de Vries, Tom Bakker, Mark Jansen (Author) https://creativecommons.org/licenses/by/4.0
OTA fault injection and rollback verification framework for safety-critical systems
Safety-critical systems in aviation and nuclear control must keep OTA software updates safe and stable to avoid faults during remote deployment. This study built a Monte Carlo–based test and rollback system to check OTA reliability under different fault types. Eight common failures—such a network loss, image damage, and signature errors—were tested in 1,000 OTA update runs. The system reached a 96.2% automatic recovery rate, an average recovery time of 13.2 seconds, and 100% log accuracy. These results show that the setup can find, control, and recover from multiple faults effectively. The design follows DO-178C and ARINC 653 safety rules and provides clear data support for system validation. The framework gives a simple and practical way to test OTA safety in aviation, nuclear, and other control systems and can be expanded to field networks for large-scale reliability studies.
Copyright (c) 2026 Lina M. Kovacs, Hiroshi Tanaka, Sofia G. Alvarez (Author) https://creativecommons.org/licenses/by/4.0
Long-Range Dependency Modeling in Decentralized Finance Markets Through Structured State Space Architectures
The decentralized finance market exhibits extreme volatility and complex nonlinear dynamics that pose significant challenges for accurate price prediction and risk management. Traditional time series models, including Long Short-Term Memory networks and Transformer architectures, struggle with either computational inefficiency in capturing long-rangedependencies or inadequate context retention across extended sequences. This research investigates the application of Structured State Space Models, particularly the Mamba architecture with selective state spaces, for modeling temporal dependencies in DeFi markets. The proposed framework addresses the limitations of conventional approaches by leveraging SSMs' linear-time complexity while maintaining superior long-sequence modeling capabilities through context-aware selective mechanisms. Our methodology integrates SSM architectures with DeFispecific features including on-chain transaction volumes, liquidity metrics, and market microstructure indicators. Experimental validation across multiple cryptocurrency pairs demonstrates that SSM-based models achieve competitive performance compared to attentionbaseTransformers while offering substantial computational advantages. The results indicate that selective state space mechanisms enable effective capture of both short-term volatility patterns and long-horizon price trends in decentralized markets. This work contributes to the emerginintersection of advanced sequence modeling techniques and blockchain-based financial systems, providing insights for algorithmic trading strategies and risk assessment frameworks in the rapidly evolving DeFi ecosystem.
Copyright (c) 2026 Chengyuan Xu (Author) https://creativecommons.org/licenses/by/4.0
Progressive Training Strategies for Routability Prediction in Modern Floorplanning
Modern integrated circuit design faces increasing challenges in achieving design closure due to complex routing congestion patterns that emerge during physical implementation. This paper presents a progressive training framework for routability prediction that decomposes the learning task into hierarchical stages aligned with the natural abstraction levels of physical design. The proposed methodology employs a three-stage architecture that progressively refines predictions from coarse-grained global congestion estimation through intermediate pin accessibility analysis to fine-grained design rule violation detection. By leveraging convolutional neural networks for spatial feature extraction and graph neural networks for topological modeling, our approach achieves superior prediction accuracy compared to conventional single stage methods. Experimental validation on industry-standard benchmarks demonstrates an area under the curve exceeding 0.92, representing a 7% improvement over baseline approaches, while maintaining inference latency below 100 milliseconds suitable for interactive placement optimization workflows.
Copyright (c) 2026 Shengyu Lin, Kangwei Xu , Matthias Vogel (Author) https://creativecommons.org/licenses/by/4.0
INTELLIGENT INCIDENT TRIAGE IN HIGH-VOLUME PAYMENT PROCESSING SYSTEMS VIA HIERARCHICAL GRAPH DIFFUSION NETWORKS
The consolidation behavior of soft soil foundations subjected to cyclic loading presents a complex multiphysics problem critical to the stability of transportation infrastructure and offshore structures. Traditional continuum mechanics approaches often struggle to capture the granular rearrangements and micro-structural evolution inherent in soft clays and silts under repeated stress applications. This study employs the Discrete Element Method (DEM) coupled with a coarse-grid fluid dynamics scheme to simulate the hydro-mechanical response of soft soil assemblies. By modeling the soil skeleton as a collection of bonded particles and the pore fluid as a continuum interacting through drag forces, we investigate the generation and dissipation of excess pore water pressure and the resulting cumulative settlement. The simulation results reveal that cyclic loading frequency and amplitude significantly influence the breakdown of inter-particle bonds and the reorganization of the force chain network, leading to non-linear consolidation rates that deviate from classical Terzaghi theory. The study further elucidates the mechanism of strain accumulation, showing that particle reorientation acts as the primary driver for permanent deformation after the initial breakdown of soil structure. These findings provide micro-scale insights that enhance the predictive capability for long-term foundation settlement.
Copyright (c) 2026 Shengyu Lin , Kangwei Xu , Matthias Vogel (Author) https://creativecommons.org/licenses/by/4.0
Causal Fault Localization for Cross-Border Payment Gateways Using Multi-Relational Service Dependency Graphs
Cross-border payment gateways have become critical infrastructure for global e commerce, processing trillions of dollars in international transactions annually. However, their complex distributed architecture, involving multiple microservices, currency conversion systems, compliance modules, and international banking networks, creates significant challenges for fault diagnosis when service degradations occur. Traditional fault localization approaches struggle with the intricate causal relationships and multi-relational dependencies inherent in these systems.This research proposes a novel causal fault localization framework based on Multi-Relational Service Dependency Graphs (MRSDG) specifically designed for cross-border payment gateways. Our approach constructs a comprehensive dependency graph capturing both synchronous servicecalls and asynchronous message flows while incorporating temporal causality constraints derived from transaction traces. We introduce a weighted PageRank algorithm enhanced with causal inference mechanisms to identify root causes by analyzing fault propagation patterns across heterogeneous service relationships. The framework integrates Graph Neural Network architectures with structural causal models to distinguish correlation from genuine causal relationships, thereby reducing false positives in fault diagnosis. Experimental validation using production traces from a multinational payment gateway demonstrates that our MRSDG-based approach achieves superior accuracy in localizing root causes compared to conventional dependency graph methods, with mean time to detection reduced by approximately forty-three percent and false positive rates decreased by thirty-seven percent. The causal inference component successfully filters spurious correlations arising from shared payment peaks and regulatory events. These results indicate that incorporating multi-relational dependencies and causal reasoning significantly enhances fault localization effectiveness in complex cross-border payment infrastructures.
Copyright (c) 2026 Stefan Müller , Isabel Torres (Author) https://creativecommons.org/licenses/by/4.0
Hierarchical Attention Mechanisms for Interpretable Competency Modeling in Computer-Adaptive Testing Environments
Computer-adaptive testing (CAT) systems have revolutionized educational assessment by providing personalized evaluation experiences. However, traditional CAT models often lack interpretability in understanding student competency development. This paper proposes a hierarchical attention mechanism framework for interpretable competency modeling in CAT environments. The proposed approach leverages multi-level attention layers to capture both fine grained skill mastery patterns and high-level competency representations. By integrating attention weights as interpretability indicators, educators can understand which knowledge components contribute most significantly to student performance predictions. Experimental results demonstrate that our hierarchical attention model achieves superior prediction accuracy while maintaining interpretability through attention visualization. The framework provides actionable insights for adaptive item selection and personalized learning pathway recommendations. This research bridges the gap between model performance and educational interpretability in intelligent tutoring systems.
Copyright (c) 2026 Lucía Fernández , Thomas Keller (Author) https://creativecommons.org/licenses/by/4.0
Bridging Cognitive Load Theory and Deep Q-Networks for Real-Time Difficulty Calibration in Automated Tutoring Systems
This paper presents a novel approach integrating Cognitive Load Theory with Deep Q Network reinforcement learning to achieve real-time difficulty calibration in intelligent tutoring systems. While conventional adaptive learning systems rely on static rule-based approaches or retrospective performance analysis, our framework leverages DQN algorithms to dynamically optimize instructional difficulty by continuously evaluating learner cognitive load states. The proposed architecture employs a dual-stream neural network that simultaneously estimates learner competency levels and predicts optimal task complexity, enabling moment-to-momentadjustments that maintain learners within their zone of proximal development. Experimental validation demonstrates significant improvements in learning efficiency and knowledge retention compared to traditional adaptive systems, with the DQN-based approach reducing cognitive overload incidents by 43% while maintaining engagement levels above 85%. This work contributes to the intersection of educational psychology and artificial intelligence by demonstrating how modern deep reinforcement learning techniques can operationalize theoretical frameworks from cognitive science to create more effective personalized learning environments.
Copyright (c) 2026 Yifan Guo (Author) https://creativecommons.org/licenses/by/4.0
Restoration of Intestinal Barrier Function via Dietary Amino Acid Supplementation: A Systems Biology Modeling Approach
The maintenance of intestinal barrier integrity is fundamental to host health, serving as the primary defense against pathogen invasion and systemic inflammation. Disruption of this barrier, often termed leaky gut, is a hallmark of various gastrointestinal disorders, including inflammatory bowel disease and irritable bowel syndrome. While specific amino acids such as glutamine, arginine, and tryptophan have shown promise in promoting mucosal healing, the complex interplay between their metabolic pathways and the structural components of the tight junction assembly remains incompletely understood. This paper presents a comprehensive systems biology approach to model the restorative dynamics of the intestinal epithelium under dietary amino acid supplementation. By integrating metabolic flux analysis with signal transduction network modeling, we developed a computational framework that simulates the dose-dependent and synergistic effects of amino acids on tight junction protein synthesis and assembly. Our simulations reveal that while single-nutrient supplementation provides linear restorative benefits, combinatorial approaches yield emergent, non-linear improvements in barrier function through cross-talk between the mTOR and NF-kB signaling pathways. These findings suggest that a holistic, systems-level perspective is required to optimize nutritional interventions for gut barrier restoration.
Copyright (c) 2026 Thomas H. Miller , Sarah E. Johnson (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning-Based Automated Detection of Lesions in Medical Imaging using Convolutional Neural Networks and Transfer Learning
The exponential growth of medical imaging data has created a significant demand for automated diagnostic tools capable of assisting radiologists in the detection and classification of pathological lesions. Traditional manual interpretation is labor-intensive, subject to interobserver variability, and prone to diagnostic errors due to fatigue. This paper presents a comprehensive study on the application of Deep Learning, specifically Convolutional Neural Networks, for the automated detection of lesions in medical imaging. Recognizing the inherent challenge of data scarcity in the medical domain, we employ Transfer Learning techniques to leverage the feature extraction capabilities of models pre-trained on large-scale natural image datasets. Our methodology involves the adaptation of established architectures, such as ResNet and VGG, which are fine-tuned on a curated dataset of annotated medical scans. We investigate the impact of various preprocessing strategies, data augmentation techniques, and hyperparameter configurations on the model performance. The experimental results demonstrate that the proposed approach achieves high sensitivity and specificity, significantly outperforming traditional computer-aided diagnosis systems based on hand-crafted features. The findings suggest that deep learning models, when effectively optimized via transfer learning, offer a robust and scalable solution for clinical decision support systems.
Copyright (c) 2026 Robert Thompson , Sarah Miller (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Identification of Novel Circulating Biomarkers for Early Cancer Detection using High-Dimensional Feature Selection Algorithms
The early detection of malignant neoplasms remains one of the most significant challenges in modern oncology, directly influencing patient survival rates and therapeutic efficacy. Liquid biopsies, utilizing circulating biomarkers such as cell-free DNA, microRNAs, and circulating tumor cells, offer a minimally invasive alternative to traditional tissue biopsies. However, the high dimensionality of omics data derived from these sources introduces the curse of dimensionality, where the number of features vastly outnumbers the sample size, leading to model overfitting and reduced generalizability. This paper proposes a novel computational framework utilizing high-dimensional feature selection algorithms to identify robust circulating biomarkers for early cancer detection. We employ a hybrid approach combining filter-based ranking with wrapper-based optimization to isolate subsets of features that maximize classification accuracy while minimizing redundancy. Our methodology is validated on a multi-cancer dataset, demonstrating superior performance metrics compared to conventional selection techniques. The study identifies a panel of novel biomarkers with high sensitivity and specificity, elucidating their potential role in early-stage diagnosis. These findings underscore the critical importance of advanced machine learning techniques in translating complex biological data into actionable clinical tools.
Copyright (c) 2026 Takumi Saito , Kaito Sasaki (Author) https://creativecommons.org/licenses/by/4.0
Evaluating the Learning Outcomes of Metaverse-Based Educational Platforms using Learning Analytics and Educational Data Mining
The advent of the Metaverse represents a significant paradigm shift in educational technology, transitioning from two-dimensional Learning Management Systems to immersive, three-dimensional virtual environments. While the pedagogical potential of these platforms is theoretically high, empirical evaluation of learning outcomes remains a complex challenge. This paper proposes a comprehensive framework for evaluating student performance and cognitive development within Metaverse-based educational platforms by leveraging Learning Analytics and Educational Data Mining. We investigate the correlation between immersive behavioral metrics—such as spatial trajectory, object interaction frequency, and gaze latency—and traditional learning outcomes. By applying clustering algorithms and predictive modeling to multimodal data streams generated within virtual environments, we identify distinct learner archetypes and engagement patterns. Our methodology addresses the limitations of self-reported surveys by introducing objective, data-driven indicators of engagement and knowledge acquisition. The findings suggest that while high immersion correlates with increased motivation, it introduces unique cognitive load challenges that must be managed through adaptive instructional design. This study provides a foundational rubric for educators and developers to assess the efficacy of immersive learning systems systematically.
Copyright (c) 2026 Sarah Johnson , Michael Richardson (Author) https://creativecommons.org/licenses/by/4.0
Analyzing Consumer Purchase Patterns using Large-Scale Behavioral Data and Hidden Markov Models
The proliferation of e-commerce platforms has generated unprecedented volumes of granular behavioral data, offering researchers and practitioners the opportunity to analyze consumer decision-making processes with exceptional precision. Traditional static models, such as regression analysis or cluster-based segmentation, often fail to capture the temporal dynamics and sequential nature of consumer purchase journeys. This paper proposes a robust methodological framework utilizing Hidden Markov Models to decode the latent intent states driving observable online behaviors. By leveraging a large-scale dataset comprising clickstream logs from a major online retailer, we construct a probabilistic model that identifies distinct phases of the purchase funnel, ranging from initial exploration to focused evaluation and final transaction. Our approach treats consumer intent as a latent variable that evolves stochastically over time, manifesting through observable actions such as product views, cart additions, and search queries. The results demonstrate that the proposed model significantly outperforms baseline static classifiers in predicting purchase conversion and user churn. Furthermore, the analysis reveals complex transition patterns between latent states, providing actionable insights for dynamic personalization andinventory management. This study contributes to the literature on customer relationship management by bridging the gap between behavioral big data and stochastic modeling techniques, offering a scalable solution for understanding the temporal evolution of consumer demand.
Copyright (c) 2026 Ji-Hoon Lee, Emily Robertson (Author) https://creativecommons.org/licenses/by/4.0
A Shapley Value-Based Explainable AI Framework using Monte Carlo Sampling for High-Stakes Decision Making
The deployment of complex machine learning models in high-stakes domains, such as healthcare diagnostics, financial credit scoring, and criminal justice, has precipitated an urgent demand for interpretability. While deep learning and ensemble methods offer superior predictive performance, their opaque nature presents significant barriers to trust, accountability, and regulatory compliance. This paper introduces a robust Explainable AI framework grounded in cooperative game theory, specifically leveraging the Shapley value to attribute feature importance. To address the NP-hard computational complexity associated with exact Shapley value calculations in high-dimensional datasets, we propose and evaluate a Monte Carlo sampling approximation method. This approach balances computational efficiency with the rigorous axiomatic properties of the Shapley value, providing local and global explanations that are theoretically sound. We demonstrate the efficacy of this framework through extensive experimentation on financial risk assessment datasets. The results indicate that our Monte CarloThe based estimator converges rapidly to true feature contributions, offering stable and actionable insights for decision-makers operating under strict liability constraints. By quantifying the uncertainty of explanations, this research bridges the gap between black-box efficacy and the transparency required for critical societal infrastructure.
Copyright (c) 2026 Riku Kato , Sota Yoshida (Author) https://creativecommons.org/licenses/by/4.0
Latency-Aware Edge-Cloud Collaboration for Distributed AI Inference: A Convex Optimization Perspective
The proliferation of Internet of Things devices and the subsequent demand for intelligent applications have catalyzed the shift from centralized cloud computing to distributed edge computing paradigms. This paper addresses the critical challenge of minimizing inference latency in distributed Artificial Intelligence systems through a collaborative edge-cloud architecture. While deep neural networks provide high accuracy for tasks such as image recognition and natural language processing, their computational demands often exceed the capabilities of resource-constrained edge devices. Conversely, offloading all tasks to a centralized cloud introduces significant wide-area network transmission delays. To navigate this trade-off, we propose a mathematically rigorous framework based on convex optimization to determine optimal task offloading ratios and resource allocation strategies. We formulate the latency minimization problem as a joint optimization of computation resources, transmit power, and offloading decisions, subject to strict power and bandwidthconstraints. By relaxing the integer constraints inherent in binary offloading decisions, we transform the problem into a convex form solvable via standard optimization techniques. Our theoretical analysis and numericalsimulations demonstrate that the proposed method significantly reduces system-wide latency compared to local-only, cloud-only, and heuristic-based approaches. This work provides a scalable foundation for real-time AI inference in next-generation networks.
Copyright (c) 2026 Arthur P. Jenkins (Author) https://creativecommons.org/licenses/by/4.0
Distributed Multi-Agent Coordination using Game Theoretic Consensus Protocols for Complex Problem Solving
The coordination of distributed multi-agent systems represents a fundamental challenge in modern systems engineering and artificial intelligence. As the scale and complexity of networked systems increase, traditional centralized control architectures become untenable due to communication bottlenecks, latency issues, and susceptibility to single points of failure. This paper explores the application of game theoretic frameworks to the problem of distributed consensus, where independent agents must agree on a state or decision variable through local interactions alone. By modeling the consensus problem as a non-cooperative game, we establish a protocol where the Nash equilibrium corresponds to the desired global consensus state. We investigate the dynamics of agent interactions under this game-theoretic formulation, analyzing how utility maximization at the local level translates to global coherence. The research delves into the robustness of these protocols against topological variations and information delays, comparing them against standard linear consensus algorithms. Our findings suggest that game theoretic approaches offer superior flexibility and resilience in dynamic environments, particularly where agents possess heterogeneous objectives or resource constraints. The proposed methodology provides a scalable pathway for solving complex problems in domains ranging from sensor network fusion to autonomous robotic swarms.
Copyright (c) 2026 Min-Jun Kim (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Grating Couplers for Vertical Emission Lasers using Adjoint Method and Topology Optimization
The efficient coupling of light from planar photonic integrated circuits to external optical fibers or free-space optics remains a significant bottleneck in the deployment of silicon photonics and vertical emission laser systems. Traditional uniform grating couplers suffer from limited coupling efficiency and restricted bandwidth due to mode mismatch and significant back reflections. This paper presents a comprehensive study on the design and optimization of highefficiency vertical emission grating couplers utilizing the adjoint method combined with topology optimization. Unlike conventional parameter sweeping approaches that are limited to predefined geometric shapes, topology optimization allows for the free distribution of dielectric material within a design domain, enabling non-intuitive organic geometries that manipulate the optical field with high precision. We establish a computational framework that integrates finite-difference time-domain simulations with gradient-based optimization algorithms to maximize the figure of merit, defined as the vertical coupling efficiency into a Gaussian mode. The study rigorously addresses fabrication constraints, such as minimum feature sizes, to ensure themanufacturability of the generated designs. Our results demonstrate that topology-optimized couplers can achieve coupling efficiencies exceeding those of apodized gratings by a significant margin, while maintaining robust operation across a wide spectral bandwidth. These findings suggest that inverse design methodologies are essential for the next generation of high-performance vertical emission devices.
Copyright (c) 2026 Juliette Durand (Author) https://creativecommons.org/licenses/by/4.0
Numerical Simulation of Consolidation Behavior of Soft Soil Foundations under Cyclic Loading using Discrete Element Method
The consolidation behavior of soft soil foundations subjected to cyclic loading presents a complex Multiphysics problem critical to the stability of transportation infrastructure and offshore structures. Traditionacontinuum mechanics approaches often struggle to capture the granular rearrangements and micro-structural evolution inherent in soft clays and silts under repeated stress applications. This study employs the Discrete Element Method (DEM) coupled with a coarse-grid fluid dynamics scheme to simulate the hydro-mechanical response of soft soil assemblies. By modeling the soil skeleton as a collection of bonded particles and the pore fluid as a continuum interacting through drag forces, we investigate the generation and dissipation of excess pore water pressure and the resulting cumulative settlement. The simulation results reveal that cyclic loading frequency and amplitude significantly influence the breakdown of inter-particle bonds and the reorganization of the force chain network, leading to non-linear consolidation rates that deviate from classical Terzaghi theory. The study further elucidates the mechanism of strain accumulation, showing that particle reorientation acts as the primary driver for permanent deformation after the initial breakdown of soil structure. These findings provide micro-scale insights that enhance the predictive capability for long-term foundation settlement.
Copyright (c) 2026 Alexander Smith (Author) https://creativecommons.org/licenses/by/4.0
Active Learning for Human-in-the-Loop AML Detection: Optimizing Label Efficiency and Analyst Workload
This work explores active learning strategies for improving anti-money laundering (AML) model performance while managing the cost of human annotation. Four query strategies—uncertainty sampling, margin sampling, diversity sampling, and a hybrid policy—were evaluated using 9.4 million transactions and 84,000 alerts from a real-world financial dataset. Across five active-learning cycles, each strategy requested labels for 1% of unlabeled alerts per round. The hybrid policy achieved the highest gains, increasing the model AUC from 0.86 to 0.93 and reducing false-positive counts by 34.2% at 80% recall. Analyst workload decreased by 41.5% due to a higher concentration of informative borderline samples. The results show that effective sampling strategies can significantly improve AML detection without increasing annotation budgets.
Copyright (c) 2026 Noa Levi, Eitan Shapiro, David Ben-Ami (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning-Based Optimization Techniques For Large-Scale Data Processing In Cloud Environments
The exponential growth of large-scale datasets in modern industries—ranging from social media and e-commerce to bioinformatics and IoT—has increased the demand for efficient cloud-based data processing platforms. Deep learning-powered optimization techniques offer powerful solutions for handling the computational complexity, latency challenges, and resource allocation demands of distributed cloud systems. This article provides an in-depth exploration of deep learning models used for optimizing data processing tasks, including neural scheduling algorithms, reinforcement learning-based resource allocation, auto-scaling prediction models, and deep neural network–driven workload balancing. Two graphs demonstrate the efficiency improvements and reduction in processing latency offered by deep learning integration. The article concludes with key challenges such as energy consumption, model transparency, and data security, while outlining future research directions involving federated optimization, edge–cloud collaboration, and quantum-assisted deep learning.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0