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        <title>International Journal of Applied Mathematics and Computer Science Feed</title>
        <link>https://reference-global.com/journal/AMCS</link>
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            <title>International Journal of Applied Mathematics and Computer Science Feed</title>
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            <link>https://reference-global.com/journal/AMCS</link>
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        <copyright>All rights reserved 2026, University of Zielona Góra</copyright>
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        <item>
            <title><![CDATA[Combined No–Reference Metrics for Quality Assessment of Reconstructed Satellite Lidar Data]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0027</link>
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            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[In this paper, experimental results concerning the application of no-reference image quality metrics for automatic quality assessment of reconstructed satellite light detection and ranging (LiDAR) data are discussed. Since satellite LiDARs provide much sparser data compared to airborne systems and their sending to Earth requires additional compression, the received data should be reconstructed. For this purpose, generative neural networks can be applied, including generative diffusion models; however, they require the choice of appropriate parameters, and the reconstruction results should be reliably assessed. Although the physical representation of data is related to the number of received photons, considering the novel data representation based on hyper-height data cubes, it is possible to analyze individual percentiles, representing canopy height models or digital terrain models, as grayscale images. Therefore, their quality may be evaluated using image quality assessment metrics. Due to the lack of reference data, in the application considered, only no-reference metrics may be efficiently applied, even though they have lower correlation with subjective quality perception compared to full-reference metrics. Therefore, the idea of designing combined no-reference metrics optimized for the highest correlation with subjective quality scores is discussed, along with the presentation of the obtained correlation results.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Alike Parts: A Feature–Informed approach to Local and Global Prototype Explanations]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0026</link>
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            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Prototype-based explanations offer an intuitive, example-based approach to support the interpretability of machine learning black box classifiers but often lack feature-level granularity. We introduce a framework that integrates feature importance at two levels to address this gap. First, for local explanations, we propose alike parts—a method that uses feature importance scores to highlight the most relevant, shared feature subsets between a classified instance and its nearest prototype, guiding user attention. Second, we augment the global prototype selection objective function with a feature importance term to actively promote diversity in the feature attributions of the selected prototypes. Experiments on six benchmark datasets show that this augmented selection process maintains or, in some cases, increases the prediction fidelity of the surrogate model, suggesting that feature diversity does not compromise model fidelity.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Contact Models in Robotic Grasp Learning]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0029</link>
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            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Autonomous robotic manipulation faces persistent challenges in dynamic, unstructured environments, where traditional physics-based models struggle with incomplete or inaccurate object and contact data. Machine learning has enabled adaptive manipulation skills, but purely end-to-end approaches often make contact-level reasoning implicit and rely on extensive training data. Structured methods that incorporate segmentation, depth, and planning offer complementary, more interpretable alternatives. Contact modelling plays a central role in predicting object motion and enabling realistic simulations, especially at close-range interactions. Early forward models demonstrated generalization to unseen objects, but struggled with action generation and exemplar dependence. Later advances introduced generative contact models that infer grasps directly from depth images, enabling one-shot learning and robust manipulation without prior object knowledge. This paper reviews and compares such contact modelling approaches, highlighting their role in grasp synthesis, generalization, and integration into dynamic and perception-driven manipulation pipelines.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Parameter Identification of Bearing Stiffness for a Centrifugal Compressor Using an Artificial Neural Network]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0033</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0033</guid>
            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The design of modern high-tech equipment (pumps, compressors, turbines) is inevitably related to the need to ensure vibration reliability, especially at high shaft rotation speeds, which is particularly relevant for flexible rotors. Therefore, the research aims to provide vibration reliability of energy-intensive rotary machines by developing a hybrid parametric identification approach that leverages analytical dependencies in rotor dynamics, numerical simulations to study rotor behavior, and artificial intelligence systems. While implementing the proposed solution, the Monte Carlo method and Moore–Penrose pseudoinversion are also applied. As a result, modified linear and nonlinear parameter identification approaches are developed. They are based on an improved finite element model that accounts for the analytical dependence of bearing stiffness on rotor speed, with the parameters evaluated numerically. The corresponding algorithms were implemented in the PTC MathCAD 14 computer algebra system and the Python 3.13 programming language, using the authors’ program code and an artificial neural network. The developed methods and algorithms were proven in the case study of a natural gas compressor station’s multistage centrifugal compressor, S325 GC2-650/6-56M12. As a result, stiffness parameters of bearing supports were identified with a 0.2–0.7% accuracy for the first two critical frequencies compared with the available data from the accelerating-balancing stand “Schenck” with a vacuum chamber. The proposed parameter identification approach can also be extended to evaluate stiffness parameters for high-precision bearing supports with speed-dependent stiffness in multistage centrifugal pumps, turbochargers, turbines, and turbopump units.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Efficient Anomaly Detection System in IoT Edge Using Chi–Square and Improved Particle Swarm Optimization Feature Selection with Ensemble Classifiers]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0031</link>
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            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Anomaly detection using machine learning (ML) has become a critical research focus point in the modern digital era. Although recent ML-based models demonstrate strong detection capabilities, their performance is often limited by the vast volume and high dimensionality of data, leading to reduced accuracy, lower detection rates, and increased learning complexity. To address these challenges, this study proposes a novel ensemble learning framework that combines random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), category boosting (CatBoost), and a light gradient boosting machine (LGBM) with a hybrid feature selection strategy. An improved particle swarm optimization (IPSO) algorithm enhanced with elimination and opposition-based learning is developed and integrated with the chi-square method (chi-IPSO) to optimize feature selection. Additionally, the synthetic minority oversampling technique (SMOTE) is employed to handle class imbalance in the datasets. The proposed framework is evaluated using two benchmark datasets, UNSW-NB15 and CICIDS 2017. Experimental results reveal that the RF algorithm with chi-IPSO achieves superior performance, attaining an accuracy of 94.58% on UNSW-NB15 and 99.70% on CICIDS 2017. These results illustrate the effectiveness of the proposed model, showing notable betterment over existing state-of-the-art anomaly detection approaches.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Bridging Stochastic, Robust, and Neutrosophic Paradigms for Hurricane Evacuation Under Compound Uncertainty: A Six–Layer Methodological Synthesis]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0034</link>
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            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Hurricane evacuation planning under uncertainty requires integrating decisions across multiple spatial and temporal scales, yet the literature addresses demand stochasticity, temporal robustness, and computational tractability largely in isolation. This paper develops a unified optimization framework organized around six progressive layers: meteorological scenario generation, deterministic and stochastic network flow planning, vehicle scheduling with origin-based decomposition, classical robust temporal protection, neutrosophic adaptive robust optimization with scenario-anchored uncertainty sets, and computational complexity characterization. An integrated critical review of 337 Scopus-indexed publications (2022–2026) identifies persistent methodological gaps that motivate each architectural layer. The framework couples two-stage stochastic programming with budget-constrained robust optimization, affine decision rules, and neutrosophic indeterminacy calibration within a single operational pipeline. Validation on a georeferenced Caribbean network demonstrates a 94% reduction in binary variables through origin-based decomposition and end-to-end execution in under four minutes, with a sensitivity analysis showing that neutrosophic budget calibration reduces the price of robustness from 24.8% to 2.8%.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Improved Tabu Search Algorithm for Parallel Task Scheduling on Identical Machines]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0030</link>
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            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Aiming at the parallel task scheduling problem (P |sizej|Cmax), which requires multiple machines to work collaboratively, this paper proposes an improved tabu search algorithm to overcome the shortcoming of traditional methods being prone to falling into local optima. Firstly, a hybrid neighborhood structure combining swap and insertion operations is designed to enhance search diversity and local optimization capabilities. Secondly, a dynamic tabu length adjustment mechanism is introduced to adaptively change the cycle of tabu objects according to the search process, thereby achieving a balance between breadth and depth of search. Finally, a simplified aspiration criterion is proposed to avoid the loss of high-quality solutions caused by excessive tabu. Extensive computational experiments on randomly generated test instances show that the designed algorithm achieves highly competitive performance compared with the original tabu search algorithm and the OR-Tools solver, while significantly improving computational efficiency. In particular, the proposed method reduces the average Cmax by approximately 10.6% on extra-large-scale instances, demonstrating superior solution quality and robustness.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Fixed–Time Positional Path–Following Control System for Unicycle–Like Mobile Robots with Constrained Inputs: Design and Robustness]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0028</link>
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            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents a fixed-time solution to the positional path-following control task for unicycle-like mobile robots with constraints imposed on the control inputs. The proposed control system, designed for a guidance point located out of the robot wheels’ axle, guarantees local convergence of a positional error for a guidance point in finite time which is upper bounded by a constant (independent of an initial robot configuration). Apart from control derivation and a stability proof conducted for closed-loop dynamics, the paper includes also a robustness analysis which reveals how the fixed-time control function improves robustness of the closed-loop system to external perturbations affecting the control input channels of a robot. Numerical simulations and experimental results validate the concept, and also highlight some computational and implementation issues important in practical applications.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Extended Time Petri Nets]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0024</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0024</guid>
            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[In many complex systems that can be modeled using Petri nets, time can be a very important factor which should be taken into account during creation and analysis of the model. Time data can describe the starting moments of some actions or their duration before their immediate effects start to influence some other areas of the modeled system. Places in a Petri net often portray static components of the system, but they can also describe states. Such a state can have time restrictions, for example, indicating how long it can influence other elements in the model. Time values describing some systems may be inconsistent or incomplete, which can cause problems during the creation of the model. In this paper, a new extension of time Petri nets is proposed which allows the creation of models with different types of time data that previously were able to be properly used in separate types of well-known time Petri nets. The new time Petri net introduced here solves this problem by integrating different aspects of already existing time Petri nets into one unified structure.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[The Less–is–More Approach to Variable Neighborhood Search: A Comparative Study]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0025</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0025</guid>
            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The less-is-more approach applied to metaheuristic variable neighborhood search combines simplicity and effectiveness in a unique way. With a minimal volume of source code, one can quickly obtain very good solutions. However, the time spent on algorithm implementation may grow significantly on attempts to fit its elementary components, such as, for example, selection of an initial solution, local search moves, or perturbation moves. This study aims to support such a process through a comparison of several possibilities of element composition, including new ideas for elementary moves. The systematic comparative analysis includes 36 versions of this metaheuristic tested on instances of two problems: the traveling salesman one, since it is a model problem commonly used in real-world applications, and that of DNA sequencing, which is a basic and well-studied problem in bioinformatics. The results give basis for conclusions about the compositions of procedures that perform better than others, in the sense of solution quality and computation time.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Adaptive Iterative Learning Control of Multi–Agent Systems Under Iterative Batch Random Missing Faults Based on Neural Networks]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0032</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0032</guid>
            <pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[For multi-agent systems with missing iteration batches and randomly varying lengths, a data-driven adaptive iterative learning control strategy based on neural networks is proposed. Considering the situation where the iteration batches of the system vary randomly with the number of iterations, first, an iterative batch missing fault model that follows a random probability distribution is designed. Secondly, based on the radial basis function neural network method, a novel data-driven adaptive iterative learning consistency control algorithm is proposed. The stability analysis of the system is strictly performed using the Lyapunov function, and the sufficient conditions for consistency of multi-agent systems are derived through relevant mathematical derivations. This enables multi-agent systems to achieve state consistency even when there are missing faults in the iterative batches. Finally, the feasibility of the algorithm is verified through numerical simulations in a multi-agent system.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Primal–Dual Interior Point Method for Complex–Variable Optimization Problems]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0013</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0013</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[In this paper, we propose a primal-dual interior-point method for solving convex optimization problems with complex variables, relying on a newly defined complex-valued kernel function. We extend classical kernel functions to the complex domain by establishing appropriate differentiability and convexity properties that guarantee the well-posedness and convergence of the proposed algorithm. Our theoretical approach encompasses the formulation of penalized optimality conditions, the definition of a modified Newton direction tailored to complex parametrization, and the design of a central pathtracking algorithm featuring adaptive barrier parameter updating. A rigorous complexity analysis yields polynomial bounds depending on the problem dimension and the desired accuracy. Numerical experiments on large-scale complex-variable problems demonstrate both the effectiveness and robustness of the proposed approach. The results validate the algorithm’s dimension-independence property, with iteration counts remaining stable across substantial increases in problem size, and reveal significant computational advantages over state-of-the-art general-purpose solvers including IPOPT (Interior Point Optimizer). This work advances the theoretical foundations of interior-point methods in the complex domain and opens new perspectives for high-dimensional complex optimization.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Managing Uncertainty in Federated Learning via Interval Fuzzy Sets and Entropy–Based Fusion]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0015</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0015</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper introduces a federated learning framework designed to improve the reliability of diagnostic models under conditions of uncertainty, with a particular focus on medical applications such as breast cancer diagnosis. The proposed method integrates interval-valued fuzzy sets to capture data imprecision and employs logistic regression enhanced with interval-based parameter estimation. Model parameters are aggregated across clients using the Choquet integral, extended with an entropy-based weighting scheme that accounts for both model performance and uncertainty. Experimental results on the Wisconsin breast cancer dataset demonstrate that the proposed federated architecture achieves superior performance compared to traditional methods, particularly in non-IID and unbalanced data scenarios. The framework offers robust privacy preservation, effective uncertainty modeling, and improved classification accuracy, making it suitable for high-stakes, privacy-sensitive domains.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Autoencoder–Based Image Representation Learning with Kolmogorov–Arnold Networks]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0019</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0019</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Autoencoders are widely used for learning compact latent representations of images. While convolutional architectures dominate this area due to their ability to exploit spatial locality, recent developments in neural network design have introduced Kolmogorov–Arnold networks (KANs), which replace fixed activation functions with learnable univariate mappings derived from the Kolmogorov–Arnold superposition theorem. Although KAN-based autoencoders have recently been explored for image representation tasks, existing studies typically focus on limited datasets, fixed KAN formulations, or reconstruction accuracy solely. In this work, we present a KAN-based image autoencoder model that emphasizes representation quality, model complexity, and computational cost. We design and evaluate KAN-based and FastKAN-based autoencoders under strictly matched latent dimensionality constraints and compare them with a convolutional autoencoder baseline across multiple image datasets. Reconstruction quality is assessed using the mean squared error (MSE), which quantifies pixel-wise reconstruction errors, the peak signal-to-noise ratio (PSNR), which measures signal fidelity on a logarithmic scale, and the structural similarity index (SSIM), which reflects perceptual structural similarity. Experimental results demonstrate that KAN-based autoencoders achieve reconstruction quality comparable to convolutional autoencoders of the same representation size, with statistically significant improvements in the PSNR observed in several scenarios, while exhibiting distinct efficiency trade-offs. These findings clarify the practical role of Kolmogorov–Arnold networks in image autoencoder representation learning and highlight them as an alternative to convolutional architectures in low-resolution image settings.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Plug–and–Produce Integration in Industrial Cyber–Physical Systems based on IEC 61499 and the Module Type Package]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0021</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0021</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[With the digital transformation of industrial automation systems, engineers must manage the increasing system size and complexity due to the operation and information technology convergence. To achieve massive customizations, functions provided by various field devices must be integrated flexibly with controllers. This paper proposes a plug-and-produce method for integrating field device functions with edge controllers using the IEC 61499 function block network as the process orchestration layer and the Module Type Package (MTP) as the standardized exchange file format. This seamless integration between IEC 61499 and the MTP standard significantly reduces reconfiguration time by 80% and improves the efficiency of developing industrial cyber-physical systems by up to 50%.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Explainable Hybrid Model for Decoding Silent Mental Health Symptoms through Social Media Interaction and Textual Withdrawal Patterns]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0023</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0023</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Mental health (MH) disorders, particularly depression and social withdrawal, represent critical global challenges, undermining both individual well-being and societal productivity. Social media provide a unique lens to capture digital behaviours that may serve as early indicators of MH states. While conventional diagnostic methods are often subjective and resource-intensive, digital behaviour analysis offers scalable, non-invasive alternatives. Yet, existing studies frequently isolate emotional, relational, and temporal dimensions, limiting predictive accuracy and interpretability. This study proposes digital behavioural continuum theory (DBCT), a framework integrating these dimensions to model MH states holistically. A hybrid machine learning architecture is developed, combining graph neural networks (GNNs) for relational structures, recurrent neural networks (RNNs) for temporal sequences, and Valence Aware Dictionary and Sentiment Reasoner (VADER) for the sentiment analysis technique to extract affective signals from user-generated content. Model transparency is ensured through Shapley additive explanations (SHAP), enabling identification of the most influential behavioural markers. Results demonstrate that emotional features (e.g., sentiment scores, sad reaction ratios) exert the greatest predictive influence, followed by temporal signals such as posting frequency and response latency, while relational attributes contextualise social withdrawal. The proposed model achieves an F1-score of 90.4%, a precision of 89.7%, and a recall of 91.2%, significantly surpassing baseline approaches. Importantly, the datasets analysed were not clinically diagnosed but were curated to reflect real-world social media behaviours associated with potential mental health signals. By advancing an interpretable, data-driven framework, this research bridges theoretical innovation with practical application, enhancing digital MH monitoring and supporting early, scalable interventions.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Design of a Smart Alarming System using Transfer Entropy–Based Causality Graphs]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0020</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0020</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper addresses the critical challenge of alarm floods in industrial automation systems, where cascading alarms obscure root causes of disturbances. The primary aim is to develop and validate an original smart alarming algorithm that identifies disturbance source by analyzing directional information flow between the system’s process variables. The proposed methodology integrates transfer entropy (TE), an information-theoretic measure of causal relationships, with median ensemble empirical mode decomposition (MEEMD) for signal preprocessing. The scope encompasses data normalization, delay estimation via cross-correlation (CC) and mutual information (MI), causality computation using TE, and causal graph construction to visualize disturbance propagation pathways. The algorithm is validated on two systems: a Simulink multi-loop control model and the Tennessee Eastman Process (TEP)—an industrial benchmark. The proposed methodology successfully traces disturbances and validates the method’s ability to identify the root cause. Key innovations include noise-resilient decomposition and dual-method delay estimation, which enhance robustness in high-dimensional systems. The method reduces alarm floods by isolating primary disturbances, even under nonlinear and noisy conditions.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Research on UAV Dynamic Adversarial Game Strategies using an Improved Marine Predator Algorithm]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0014</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0014</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper proposes an improved version of the marine predator algorithm (abbreviated as IMPA) to optimize unmanned aerial vehicle (UAV) dynamic adversarial game strategies, effectively addressing critical limitations of the original marine predator algorithm (MPA), including slow convergence speed and a pronounced tendency to fall into local optima. The IMPA incorporates four key innovations: opposition-based learning (OBL) for enhanced initial population quality, an adaptive mechanism for dynamic population resizing, nonlinear step-size control, and an inertia weight strategy. These improvements collectively accelerate convergence, balance global exploration with local exploitation, and significantly improve the ability to escape local optima. To validate the algorithm’s performance, a dynamic game model based on situation assessment and utility functions is established for red–blue UAV confrontation scenarios, and the IMPA is successfully applied to solve for Nash equilibrium. Comprehensive simulation results demonstrate that the proposed algorithm converges over 40% faster than the original MPA and other benchmark algorithms, and robustly achieves the Nash solution in all 100 consecutive tests, underscoring its superior effectiveness and reliability in dynamic adversarial environments.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Review of Explainable Semi–Supervised Methods in Multivariate Time Series Analysis]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0017</link>
            <guid isPermaLink="false">/article/10.61822/amcs-2026-0017</guid>
            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Recent advances in information and communication technologies have led to the widespread use of smart meters, wearable sensors, and related devices in healthcare, industrial monitoring (e.g., manufacturing and energy systems), and transportation. These systems generate large volumes of sequential data, yet fully annotating them remains expensive and often impractical. Consequently, only a small fraction of the data is typically labeled, limiting the applicability of fully supervised learning. At the same time, ignoring available labels altogether, as in fully unsupervised approaches, risks discarding valuable information. This tension has motivated growing interest in semi-supervised methods that learn from both labeled and unlabeled data. However, many such approaches rely on complex black-box models, making the decision-making process opaque, particularly in high-risk domains such as medicine and finance. Explainable AI (XAI) has therefore become essential for building trust and ensuring accountability in these settings. This review surveys recent advances in explainable semi-supervised methods for multivariate time-series analysis. We introduce a taxonomy based on how explainability is integrated, categorizing the approaches as white-box (transparent), post hoc (opaque), and intermediate. Within each category, we further classify them by their most distinctive characteristics: the model class for white-box, the interpretability integration strategy for intermediate, and the explanation technique for post-hoc. We also discuss common practices and lessons learned in dealing with partial supervision and model interpretability, and highlight key challenges in sequential data analysis, such as the choice of performance metrics and explanation techniques. We find that, although interest in explainable semi-supervised time-series methods is growing, the systematic evaluation of explanations remains underdeveloped and lacks standardized evaluation practices. Nearly 70% of the reviewed works report some form of explainability validation; however, it is typically indirect, qualitative, or limited in scope. Overall, explainable semi-supervised methods represent a promising direction for future research, with potential benefits across a wide range of real-world time-series applications.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[An Improved Chacha Algorithm Based on Quantum Random Numbers]]></title>
            <link>https://reference-global.com/article/10.61822/amcs-2026-0018</link>
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            <pubDate>Sat, 20 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Due to the merits of high efficiency and strong security against timing and side-channel attacks, ChaCha has been widely applied in real-time communication and data streaming scenarios. However, with the rapid development of AI-assisted cryptanalysis and quantum computing technologies, there are serious challenges to secure implementation of the ChaCha cipher. To further strengthen its security, we propose an improved variant based on quantum random numbers, i.e., quantum random number enhanced ChaCha (QRE-ChaCha). Specifically, the design XORs the initial constants with quantum random numbers and periodically injects the latter into selected state words during odd rounds to enhance diffusion. Compared with the original ChaCha, the present variant shows stronger resistance to differential attacks and generates a keystream with statistical randomness, thereby offering increased robustness against both classical and quantum attacks. To evaluate the security and performance of the present ChaCha, our analysis proceeds in three main parts. Firstly, we analyze its theoretical security in terms of quantum randomness and attack testing, and conduct differential cryptanalysis with an automated search method based on the Boolean satisfiability problem (SAT). Secondly, we subject the keystream generated by the cipher to randomness tests using the NIST Statistical Test Suite and the GM/T 0005-2021 randomness testing standard. Finally, we assess its encryption and decryption performance by measuring the encryption speed on files of various sizes. According to the results, the new ChaCha is significantly improved to resist differential attacks while maintaining the high efficiency of the original ChaCha cipher, and its keystream successfully passes statistical randomness tests using the NIST and GM/T 0005-2021 standards, meeting cryptographic application requirements.]]></description>
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