Applied System Innovation

15 pages, 2624 KB

Open AccessEditor’s ChoiceArticle

Design and Implementation of a Remote Water Level Control and Monitoring System in Rural Community Tanks Using LoRa and SMS Technology

by Ulises Balderrama-Rey, Rafael Verdugo-Miranda, Miguel Martínez-Gil, Joel Carvajal-Soto, Frank Romo-García, Luis Medina-Zazueta, Edgar Espinoza-Zallas and Rolando Flores-Ochoa

Appl. Syst. Innov. 2026, 9(4), 76; https://doi.org/10.3390/asi9040076 - 31 Mar 2026

Cited by 2 | Viewed by 2059

Abstract

This paper presents the design and implementation of a low-profile remote monitoring and control system for water level management in storage tanks located in rural communities. The system was developed to ensure a reliable water supply, prevent spills, reduce electrical energy consumption, and [...] Read more.

This paper presents the design and implementation of a low-profile remote monitoring and control system for water level management in storage tanks located in rural communities. The system was developed to ensure a reliable water supply, prevent spills, reduce electrical energy consumption, and mitigate theft and vandalism risks posed by a previously installed, highly exposed commercial system. The proposed system employs LoRa technology to transmit water level data from the storage tank to a receiver located 6 km from the water well. When the water level drops below a predefined threshold, the system transmits an activation signal through the LoRa network to start the well pump and trigger tank refilling. In addition, an SMS monitoring module enables users to remotely verify water level and pump operational status at any time. System notifications and operational data are automatically delivered via SMS to predefined phone numbers, enabling continuous supervision without requiring internet connectivity. The implementation of the proposed system thus provides an efficient and reliable solution for water resource management in rural environments, ensuring continuous water availability and preventing supply shortages. LoRa communication enables robust long-range data transmission, while SMS-based monitoring offers real-time operational awareness for end users. The system was validated through field testing in a pilot rural community, demonstrating operational robustness, improved water management efficiency, and measurable positive impacts on residents’ water service continuity. The low-profile physical design significantly reduced theft and vandalism incidents reported by the local water authority. Experimental results showed an average monthly reduction of 41.2% in electrical energy consumption while maintaining high system reliability, physical security, and real-time monitoring capability. Full article

(This article belongs to the Topic Collection Series on Applied System Innovation)

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38 pages, 6506 KB

Open AccessEditor’s ChoiceReview

Systemic Integration of Artificial Intelligence in Financial Project Management: A Systematic Literature Review and BERTopic-Based Analysis

by Styve L. Ndjonkin Simen, Simon P. Philbin and Gordon Hunter

Appl. Syst. Innov. 2026, 9(4), 68; https://doi.org/10.3390/asi9040068 - 24 Mar 2026

Cited by 1 | Viewed by 2429

Abstract

Artificial Intelligence (AI) is increasingly embedded in project management within the financial sector, yet existing research remains fragmented and largely focused on isolated technical applications. A systemic understanding of how AI reshapes financial project management as an integrated socio-technical capability is still lacking. [...] Read more.

Artificial Intelligence (AI) is increasingly embedded in project management within the financial sector, yet existing research remains fragmented and largely focused on isolated technical applications. A systemic understanding of how AI reshapes financial project management as an integrated socio-technical capability is still lacking. This study addresses this gap through a systematic literature review of 62 peer-reviewed articles (2022–2025), combined with BERTopic-based thematic analysis supported by large language model-assisted topic representation. The findings reveal the emergence of Agentic AI as a dominant theme, marking a shift from analytical support tools toward autonomous and collaborative agents embedded in project processes. While predictive analytics and automation are relatively mature, governance-oriented and human-centric dimensions remain underdeveloped and weakly integrated. This study contributes by: (1) presenting a computationally enhanced systematic mapping study that integrates a systematic literature review with BERTopic-based topic modelling to map the evolving research landscape; (2) identifying Agentic AI as a pivotal interface between technical execution and strategic governance; and (3) proposing a socio-technical target architecture that offers a structured roadmap for AI-enabled transformation in financial project management systems. Full article

(This article belongs to the Special Issue AI-Driven Decision Support for Systemic Innovation)

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22 pages, 2888 KB

Open AccessEditor’s ChoiceArticle

Bayesian Hyperparameter Optimization of GRU and LSTM Models for Short-Term Traffic Flow Prediction: A Case Study of Globe Roundabout in Saudi Arabia

by Sara Atef, Siraj Zahran and Ahmed Karam

Appl. Syst. Innov. 2026, 9(3), 57; https://doi.org/10.3390/asi9030057 - 10 Mar 2026

Cited by 3 | Viewed by 1643

Abstract

Accurate short-term traffic flow prediction is vital for effective signal control and sustainable urban mobility. Deep learning models, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, have demonstrated strong capability in modelling temporal traffic dynamics. However, the influence [...] Read more.

Accurate short-term traffic flow prediction is vital for effective signal control and sustainable urban mobility. Deep learning models, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, have demonstrated strong capability in modelling temporal traffic dynamics. However, the influence of their architectural and hyperparameter configurations remains underexplored. This study proposes a systematic methodology to assess the impact of hyperparameter optimization on GRU and LSTM models for predicting traffic flow at a signalized intersection. The methodology is evaluated using minute-level traffic data from the Globe Roundabout in Jeddah, Saudi Arabia. Bayesian optimization is applied to identify the best-performing hyperparameters. The results show that the optimized GRU model achieves a Root Mean Square Error (RMSE) of 0.0953, representing a 90.2% improvement compared to the baseline GRU (RMSE ≈ 0.969). Likewise, the optimized LSTM model attains an RMSE of 0.0960, corresponding to an 85.2% improvement relative to its baseline (RMSE ≈ 0.648). Similar gains are observed for the Mean Absolute Error. Visual analysis further shows that optimized models reduce smoothing bias, enhance the tracking of transient fluctuations, and produce stable, low-variance residuals. The findings demonstrate that hyperparameter optimization substantially improves predictive accuracy while preserving computational efficiency, enabling lightweight recurrent architectures to perform at a level comparable to more complex models. Full article

(This article belongs to the Special Issue Advances in Mathematical Models and Computational Intelligence for Transportation System Planning and Management)

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20 pages, 4579 KB

Open AccessEditor’s ChoiceArticle

Explainable Hybrid CNN–XGBoost Framework for Multi-Class IoT Intrusion Detection with Leakage-Aware Feature Selection

by Deemah AlFuraih, Lotfi Mhamdi and Abdullah S. Karar

Appl. Syst. Innov. 2026, 9(3), 49; https://doi.org/10.3390/asi9030049 - 26 Feb 2026

Cited by 2 | Viewed by 2096

Abstract

The rapid deployment of Internet of Things (IoT) devices has increased exposure to a diverse array of evolving cyberattacks, motivating the need for accurate and interpretable intrusion detection systems (IDS). In this work, we develop an explainable hybrid Convolutional Neural Network–Extreme Gradient Boosting [...] Read more.

The rapid deployment of Internet of Things (IoT) devices has increased exposure to a diverse array of evolving cyberattacks, motivating the need for accurate and interpretable intrusion detection systems (IDS). In this work, we develop an explainable hybrid Convolutional Neural Network–Extreme Gradient Boosting (CNN–XGBoost) framework for multi-class IoT attack classification using the CIC IoT-DIAD 2024 dataset. Network-traffic records are preprocessed and standardized using a scalable, chunk-wise workflow, after which a compact top-k subset of features is selected via Random Forest importance ranking. To reduce selection bias, a leakage-prone feature-ranking strategy is compared with a leakage-aware strategy in which features are ranked using only the training data within each split. Subsequently, a one-dimensional Convolutional Neural Network (CNN) learns a 128-dimensional representation from the selected predictors, and XGBoost performs the final multi-class classification. Under the leakage-aware protocol, the proposed model achieves 0.9324 accuracy with 0.5910 macro-F1. Results indicate that leakage-aware selection provides a more defensible estimate of generalization while maintaining competitive detection performance. Finally, SHapley Additive exPlanations (SHAP) is used to interpret the model’s decisions in the learned latent space. The analysis shows that only a small number of embedding dimensions contribute most of the decision evidence, which can aid analyst triage, although the explanations remain indirect with respect to the original traffic features. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 4373 KB

Open AccessEditor’s ChoiceArticle

Exploring Problem-Solving Strategies in Gifted and Regular Students: Education Insights from Eye-Tracking Analysis

by Po-Lei Lee, Shih-Ting Hung, Pao-Hsin Chang, Chun-Yen Chang, Lei Bao, Ting-Kuang Yeh and Li-Ching Lee

Appl. Syst. Innov. 2026, 9(2), 38; https://doi.org/10.3390/asi9020038 - 1 Feb 2026

Cited by 1 | Viewed by 1693

Abstract

This study investigated how gifted and regular high school students employ different cognitive strategies and integrate information during scientific problem solving, using eye-tracking techniques. Eighteen multiple-choice items were selected from the Investigating Scientific Thinking and Reasoning (iSTAR) assessment developed at The Ohio State [...] Read more.

This study investigated how gifted and regular high school students employ different cognitive strategies and integrate information during scientific problem solving, using eye-tracking techniques. Eighteen multiple-choice items were selected from the Investigating Scientific Thinking and Reasoning (iSTAR) assessment developed at The Ohio State University, including nine text-only questions (tMCQs) and nine picture-embedded questions (pMCQs). The items were chosen to ensure clear spatial separation among text, image, and answer areas, allowing reliable region-based eye-movement analysis. Eye-tracking data were analyzed using two indices: fixation time ratio (FTR), reflecting relative attention allocation, and saccade count ratio (SCR), capturing cross-region information integration. The results revealed clear group differences. Gifted students devoted a larger proportion of attention to pictorial information (0.38 vs. 0.32) and showed more frequent transitions between picture and answer regions (0.15 vs. 0.12), indicating more integrative processing and mental model construction. In contrast, regular students spent more time focusing on textual regions and exhibited higher within-text saccade activity, consistent with a direct translation strategy. Furthermore, SCR-based machine learning classification using a Random Forest model demonstrated meaningful discriminative capability between the two groups, particularly for picture-embedded questions, achieving an accuracy of 77.5%. Overall, the findings provide empirical evidence that question format influences students’ cognitive strategies during scientific reasoning. Methodologically, this study combines a validated reasoning assessment, a carefully defined ROI-based eye-tracking design, and interpretable behavioral indicators, offering practical implications for differentiated science instruction. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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28 pages, 6469 KB

Open AccessEditor’s ChoiceArticle

Planning Product Upgrades: A Method for Defining Release Types and Their Strategies for Software-Intensive Products

by Armin Stein, Umut Volkan Kizgin, Mohammad Albittar and Thomas Vietor

Appl. Syst. Innov. 2026, 9(2), 33; https://doi.org/10.3390/asi9020033 - 28 Jan 2026

Cited by 1 | Viewed by 1416

Abstract

The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product [...] Read more.

The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product portfolio consistently aligned with evolving market needs. Customers expect products that show continuous improvements in performance and functionality over time, making systematic product upgrading a key success factor. Release planning addresses this need by enabling continuous product evolution through planned product upgrades. It focuses on selecting and combining functional units for structured publication within releases. This proactive management of product value offers substantial potential but also demands comprehensive know-how, particularly given rising product complexity and the interplay of multiple technologies. The objective of this work is to develop a methodology that supports effective planning of product upgrades. The method assists in the product-specific selection of release types and the derivation of suitable release strategies. It yields release units defined by product structure and provides recommendations for appropriate release strategies. The methodology is demonstrated through its application to an electric vehicle, illustrating its practical relevance for software-intensive products. Full article

(This article belongs to the Section Industrial and Manufacturing Engineering)

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25 pages, 9359 KB

Open AccessEditor’s ChoiceArticle

A Multi-Stage Algorithm of Fringe Map Reconstruction for Fiber-End Surface Analysis and Non-Phase-Shifting Interferometry

by Ilya Galaktionov and Vladimir Toporovsky

Appl. Syst. Innov. 2026, 9(2), 31; https://doi.org/10.3390/asi9020031 - 27 Jan 2026

Cited by 5 | Viewed by 1733

Abstract

Interferometers are essential tools for quality control of optical surfaces. While interferometric techniques like phase-shifting interferometry offer high accuracy, they involve complex setups, require stringent calibration, and are sensitive to phase shift errors, noise, and surface inhomogeneities. In this research, we introduce an [...] Read more.

Interferometers are essential tools for quality control of optical surfaces. While interferometric techniques like phase-shifting interferometry offer high accuracy, they involve complex setups, require stringent calibration, and are sensitive to phase shift errors, noise, and surface inhomogeneities. In this research, we introduce an alternative algorithm that integrates Moving Average and Fast Fourier Transform (MAFFT) techniques with Polynomial Fitting. The proposed method achieves results comparable to a Zygo interferometer under standard conditions, with an error margin under 2%. It also maintains measurement stability in noisy environments and in the presence of significant local inhomogeneities, operating in real-time to enable wavefront measurements at 30 Hz. We have validated the algorithm through simulations assessing noise-induced errors and through experimental comparisons with a Zygo interferometer. Full article

(This article belongs to the Section Information Systems)

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23 pages, 1177 KB

Open AccessEditor’s ChoiceArticle

Scenario-Based Analysis of the Future Technological Trends in the Automotive Sector in Southeast Lower-Saxony

by Armin Stein, Lars Everding, Henrik Münchhausen, Björn Krüger, Bassem Hichri, Maximilian Flormann, Axel Wolfgang Sturm and Thomas Vietor

Appl. Syst. Innov. 2026, 9(2), 28; https://doi.org/10.3390/asi9020028 - 26 Jan 2026

Cited by 2 | Viewed by 1840

Abstract

The automotive industry faces radical technological change, driven by the adoption of electrification, automation, and digitalization. As a leading industrial hub with key OEMs and suppliers, such as Volkswagen, Southeast Lower Saxony is disproportionately impacted by this structural transformation. As a consequence of [...] Read more.

The automotive industry faces radical technological change, driven by the adoption of electrification, automation, and digitalization. As a leading industrial hub with key OEMs and suppliers, such as Volkswagen, Southeast Lower Saxony is disproportionately impacted by this structural transformation. As a consequence of these trends, the region’s automotive base faces economic uncertainties, local regulatory lag, and technological disruptions. In this study a scenario planning methodology is conducted, to identify three potential mobility futures for 2035: a Best-Case scenario, where innovation and favorable policies enable a stable growth environment for the local automotive industry; a Trend scenario, marked by incremental yet uneven progress, while maintaining the current status quo; and a Worst-Case scenario, defined by economic stagnation and regulatory impediments, leading to a slow degradation of the regional automotive industry. The scenarios are then evaluated based upon their impact and probability of occurrence, while individual impact factors were also prepared and categorized to support future decision-making on a topical basis. This study offers an overview of potential scenarios for the Southeast Lower Saxon automotive industry, supporting the strategic decision-making. Full article

(This article belongs to the Section Industrial and Manufacturing Engineering)

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18 pages, 626 KB

Open AccessEditor’s ChoiceArticle

Modeling a Reliable Intermodal Routing Problem for Emergency Materials in the Early Stage of Post-Disaster Recovery Under Uncertainty of Demand and Capacity

by Yu Huang, Haochu Cui, Yue Lu and Yan Sun

Appl. Syst. Innov. 2026, 9(2), 27; https://doi.org/10.3390/asi9020027 - 23 Jan 2026

Cited by 1 | Viewed by 1083

Abstract

This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the [...] Read more.

This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the uncertainty of both emergency materials’ demand and the network capacity by LR triangular fuzzy numbers, and thus explores a reliable routing problem for transporting emergency materials that is further formulated by a fuzzy linear programming model. Considering the decision makers’ cautious attitude on the transportation of emergency materials to avoid transportation interruption, this study adopts chance-constrained programming based on necessity measure to build a solvable reformulation of the proposed model. A numerical case study is carried out to reveal the conflicting relationship between improving the reliability and reducing the time of transporting emergency materials. The decision-makers of the emergency materials transportation organization should select a reasonable confidence level based on the actual decision-making scenario to plan the reliable intermodal route for emergency materials. By comparing with deterministic modeling, this study verifies the feasibility of the modeling the uncertainty of both demand and capacity in avoiding unreliable transportation and enhancing the flexibility of the intermodal routing for emergency materials. By comparing with chance-constrained programming using possibility measure, this study demonstrates the feasibility of the necessity measure in planning the reliable intermodal route. This study further analyzes how the capacity level of the intermodal network, demand level of the emergency materials and stability of the LR triangular fuzzy parameters influence the optimization results. Accordingly, this study emphasizes the importance of objectively evaluating the uncertain demand for emergency materials, and reveals that the enhancement of the capacity level of the intermodal network and stability of LR triangular fuzzy parameters is able to reduce the transportation time of emergency materials and meanwhile maintain a high reliability. Full article

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19 pages, 358 KB

Open AccessEditor’s ChoiceArticle

Enhancing Solar Cell Performance: Atan-Sinc Optimization Algorithm for Precise Parameter Extraction in the Three-Diode Model

by Diego Fernando Muñoz-Torres, Oscar Danilo Montoya, Jesús C. Hernández, Walter Gil-González and Luis Fernando Grisales-Noreña

Appl. Syst. Innov. 2026, 9(1), 26; https://doi.org/10.3390/asi9010026 - 22 Jan 2026

Cited by 1 | Viewed by 906

Abstract

This study focuses on estimating the nine parameters of the three-diode model (3DM) for photovoltaic (PV) cells by integrating the Atan-Sinc Optimization Algorithm (ASOA) with the Newton–Raphson (NR) method. The ASOA, a population-based metaheuristic approach inspired by the behaviors of the Sech and [...] Read more.

This study focuses on estimating the nine parameters of the three-diode model (3DM) for photovoltaic (PV) cells by integrating the Atan-Sinc Optimization Algorithm (ASOA) with the Newton–Raphson (NR) method. The ASOA, a population-based metaheuristic approach inspired by the behaviors of the Sech and Tanh functions, systematically generates candidate solutions for the complete set of parameters in the 3DM. For each of these solutions, the NR method is employed to solve the transcendental equation governing the solar cell model, facilitating a precise evaluation of the associated objective function. To guide the parameter estimation process, experimental current-voltage (I-V) and voltage-power (V-P) curves are utilized. The robustness of the proposed methodology is validated through studies on both monocrystalline and polycrystalline solar cells. Computational results reveal that the ASOA effectively navigates the parameter space, while the NR method provides accurate evaluations, resulting in reliable and precise parameter estimations. All numerical validations were conducted using MATLAB software, version 2024b. Full article

(This article belongs to the Special Issue Advanced Control Strategies and Optimization for Renewable Energy Systems)

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20 pages, 3417 KB

Open AccessEditor’s ChoiceArticle

Autonomous Frequency–Voltage Regulation Strategy for Weak-Grid Renewable-Energy Stations Based on Hybrid Supercapacitors and Cascaded H-Bridge Converters

by Geng Niu, Yu Ji, Ming Wu, Nan Zheng, Yongmei Liu, Xiangwu Yan and Yibo Gan

Appl. Syst. Innov. 2026, 9(1), 23; https://doi.org/10.3390/asi9010023 - 21 Jan 2026

Cited by 2 | Viewed by 1371

Abstract

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable [...] Read more.

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable energy stations. Based on this system, in this paper, a novel automatic frequency–voltage regulation strategy is proposed. First, a fast fault severity detection method is proposed. It evaluates the system’s fault condition by monitoring the voltage response and generates auxiliary signals to enable subsequent rapid compensation of voltage and frequency. Subsequently, fast automatic voltage and frequency regulation strategies are developed. These strategies leverage real-time fault assessment to deliver immediate power support to weak-grid renewable stations following a disturbance, thereby effectively stabilizing the terminal voltage magnitude and system frequency. The effectiveness of the proposed method is validated through simulations. A grid-connected model of a weak-grid renewable energy station is established in MATLAB (2023b)/Simulink. Tests under various fault scenarios with different short-circuit ratios and voltage sag depths demonstrate that the proposed strategy can rapidly stabilize both voltage and frequency after large disturbances. Full article

(This article belongs to the Topic Collection Series on Applied System Innovation)

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38 pages, 7660 KB

Open AccessEditor’s ChoiceArticle

Optimizing Energy Storage Systems with PSO: Improving Economics and Operations of PMGD—A Chilean Case Study

by Juan Tapia-Aguilera, Luis Fernando Grisales-Noreña, Roberto Eduardo Quintal-Palomo, Oscar Danilo Montoya and Daniel Sanin-Villa

Appl. Syst. Innov. 2026, 9(1), 22; https://doi.org/10.3390/asi9010022 - 14 Jan 2026

Cited by 4 | Viewed by 1567

Abstract

This work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to [...] Read more.

This work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to increase energy sales by the PMGD while ensuring compliance with operational constraints related to the grid, PMGD, and BESSs, and optimizing renewable energy use. A real distribution network from Compañía General de Electricidad (CGE) comprising 627 nodes was simplified into a validated three-node, two-line equivalent model to reduce computational complexity while maintaining accuracy. A mathematical model was designed to maximize economic benefits through optimal energy dispatch, considering solar generation variability, demand curves, and seasonal energy sales and purchasing prices. An energy management system was proposed based on a master–slave methodology composed of Particle Swarm Optimization (PSO) and an hourly power flow using the successive approximation method. Advanced optimization techniques such as Monte Carlo (MC) and the Genetic Algorithm (GAP) were employed as comparison methods, supported by a statistical analysis evaluating the best and average solutions, repeatability, and processing times to select the most effective optimization approach. Results demonstrate that BESS integration efficiently manages solar generation surpluses, injecting energy during peak demand and high-price periods to maximize revenue, alleviate grid congestion, and improve operational stability, with PSO proving particularly efficient. This work underscores the potential of BESS in PMGD to support a more sustainable and efficient energy matrix in Chile, despite regulatory and technical challenges that warrant further investigation. Full article

(This article belongs to the Section Applied Mathematics)

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30 pages, 4772 KB

Open AccessEditor’s ChoiceArticle

Beyond Histotrust: A Blockchain-Based Alert in Case of Tampering with an Embedded Neural Network in a Multi-Agent Context

by Antonio Pereira, Dylan Paulin and Christine Hennebert

Appl. Syst. Innov. 2026, 9(1), 19; https://doi.org/10.3390/asi9010019 - 8 Jan 2026

Cited by 1 | Viewed by 1450

Abstract

An intrusion into the operational network (OT) of a production site can cause serious damage by affecting productivity, reliability, and quality. The presence of embedded neural networks (NNs), such as classifiers, in physical devices opens the door to new attack vectors. Due to [...] Read more.

An intrusion into the operational network (OT) of a production site can cause serious damage by affecting productivity, reliability, and quality. The presence of embedded neural networks (NNs), such as classifiers, in physical devices opens the door to new attack vectors. Due to the stochastic behavior of the classifier and the difficulty of reproducing results, the Artificial Intelligence (AI) Act requires the NN’s behavior to be explainable. For this purpose, the platform HistoTrust enables tracing NN behavior, thanks to secure hardware components issuing attestations registered in a blockchain ledger. This solution helps to build trust between independent actors whose devices perform tasks in cooperation. This paper proposes going further by integrating a mechanism for detecting tampering of embedded NN, and using smart contracts executed on the blockchain to propagate the alert to the peer devices in a distributed manner. The use case of a bit-flip attack, targeting the weights of the NN model, is considered. This attack can be carried out by repeatedly injecting very small messages that can be missed by the Intrusion Detection System (IDS). Experiments are being conducted on the HistoTrust platform to demonstrate the feasibility of our distributed approach and to qualify the time required to detect intrusion and propagate the alert, in relation to the time it takes for the attack to impact decisions made by the AI. As a result, the blockchain may be a relevant technology to complement traditional IDS in order to face distributed attacks. Full article

(This article belongs to the Section Control and Systems Engineering)

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60 pages, 1807 KB

Open AccessEditor’s ChoiceReview

Recent Advances of Artificial Intelligence Methods in PMSM Condition Monitoring and Fault Diagnosis in Elevator Systems

by Vasileios I. Vlachou, Theoklitos S. Karakatsanis and Dimitrios E. Efstathiou

Appl. Syst. Innov. 2025, 8(5), 154; https://doi.org/10.3390/asi8050154 - 16 Oct 2025

Cited by 13 | Viewed by 8635

Abstract

Permanent magnet synchronous motors are the dominant technology in industrial applications such as elevator systems. Their unique advantages over induction motors give them higher energy efficiency and significant reduction in energy consumption. Accordingly, the elevator is one of the basic means of comfortable [...] Read more.

Permanent magnet synchronous motors are the dominant technology in industrial applications such as elevator systems. Their unique advantages over induction motors give them higher energy efficiency and significant reduction in energy consumption. Accordingly, the elevator is one of the basic means of comfortable and safe transportation. More generally, in elevator systems, electric motors are characterized by continuous use, increasing the risk of possible failure that may affect the operation of the system and the safety of passengers. The application of appropriate monitoring and artificial intelligence techniques contributes to the predictive maintenance of the motor and drive system. The main objective of this paper is a literature review on the application of modern monitoring methodologies using smart sensors and machine learning algorithms for early fault diagnosis and predictive maintenance generally. Thus, by exploiting the advantages and disadvantages of each method, a technique based on a multi-fault set is developed that can be integrated into an elevator control system offering desired results of immediate predictive maintenance. Full article

(This article belongs to the Section Industrial and Manufacturing Engineering)

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37 pages, 905 KB

Open AccessEditor’s ChoiceReview

Application of Fuzzy Logic Techniques in Solar Energy Systems: A Review

by Siviwe Maqekeni, KeChrist Obileke, Odilo Ndiweni and Patrick Mukumba

Appl. Syst. Innov. 2025, 8(5), 144; https://doi.org/10.3390/asi8050144 - 30 Sep 2025

Cited by 8 | Viewed by 3489

Abstract

Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, [...] Read more.

Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems. Full article

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21 pages, 2749 KB

Open AccessEditor’s ChoiceArticle

Performance Analysis of an Optical System for FSO Communications Utilizing Combined Stochastic Gradient Descent Optimization Algorithm

by Ilya Galaktionov and Vladimir Toporovsky

Appl. Syst. Innov. 2025, 8(5), 143; https://doi.org/10.3390/asi8050143 - 30 Sep 2025

Cited by 11 | Viewed by 4912

Abstract

Wavefront aberrations caused by thermal flows or arising from the quality of optical components can significantly impair wireless communication links. Such aberrations may result in an increased error rate in the received signal, leading to data loss in laser communication applications. In this [...] Read more.

Wavefront aberrations caused by thermal flows or arising from the quality of optical components can significantly impair wireless communication links. Such aberrations may result in an increased error rate in the received signal, leading to data loss in laser communication applications. In this study, we explored a newly developed combined stochastic gradient descent optimization algorithm aimed at compensating for optical distortions. The algorithm we developed exhibits linear time and space complexity and demonstrates low sensitivity to variations in input parameters. Furthermore, its implementation is relatively straightforward and does not necessitate an in-depth understanding of the underlying system, in contrast to the Stochastic Parallel Gradient Descent (SPGD) method. In addition, a developed switch-mode approach allows us to use a stochastic component of the algorithm as a rapid, rough-tuning mechanism, while the gradient descent component is used as a slower, more precise fine-tuning method. This dual-mode operation proves particularly advantageous in scenarios where there are no rapid dynamic wavefront distortions. The results demonstrated that the proposed algorithm significantly enhanced the total collected power of the beam passing through the 10 μm diaphragm that simulated a 10 μm fiber core, increasing it from 0.33 mW to 2.3 mW. Furthermore, the residual root mean square (RMS) aberration was reduced from 0.63 μm to 0.12 μm, which suggests a potential improvement in coupling efficiency from 0.1 to 0.6. Full article

(This article belongs to the Section Information Systems)

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41 pages, 4171 KB

Open AccessEditor’s ChoiceArticle

Development of a System for Recognising and Classifying Motor Activity to Control an Upper-Limb Exoskeleton

by Artem Obukhov, Mikhail Krasnyansky, Yaroslav Merkuryev and Maxim Rybachok

Appl. Syst. Innov. 2025, 8(4), 114; https://doi.org/10.3390/asi8040114 - 19 Aug 2025

Cited by 4 | Viewed by 2172

Abstract

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is [...] Read more.

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is proposed, which provides highly accurate detection of users’ movements. Signal preprocessing (noise filtering, segmentation, normalisation) and feature extraction were performed to generate input data for regression and classification models. Various machine learning algorithms are used to recognise motor activity, ranging from classical algorithms (logistic regression, k-nearest neighbors, decision trees) and ensemble methods (random forest, AdaBoost, eXtreme Gradient Boosting, stacking, voting) to deep neural networks, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformers. The algorithm for integrating machine learning models into the exoskeleton control system is considered. In experiments aimed at abandoning proprietary tracking systems (VR trackers), absolute position regression was performed using data from IMU sensors with 14 regression algorithms: The random forest ensemble provided the best accuracy (mean absolute error = 0.0022 metres). The task of classifying activity categories out of nine types is considered below. Ablation analysis showed that IMU and VR trackers produce a sufficient informative minimum, while adding EMG also introduces noise, which degrades the performance of simpler models but is successfully compensated for by deep networks. In the classification task using all signals, the maximum result (99.2%) was obtained on Transformer; the fully connected neural network generated slightly worse results (98.4%). When using only IMU data, fully connected neural network, Transformer, and CNN–GRU networks provide 100% accuracy. Experimental results confirm the effectiveness of the proposed architectures for motor activity classification, as well as the use of a multi-sensor approach that allows one to compensate for the limitations of individual types of sensors. The obtained results make it possible to continue research in this direction towards the creation of control systems for upper exoskeletons, including those used in rehabilitation and virtual simulation systems. Full article

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27 pages, 19279 KB

Open AccessEditor’s ChoiceArticle

Smart Hydroponic Cultivation System for Lettuce (Lactuca sativa L.) Growth Under Different Nutrient Solution Concentrations in a Controlled Environment

by Raul Herrera-Arroyo, Juan Martínez-Nolasco, Enrique Botello-Álvarez, Víctor Sámano-Ortega, Coral Martínez-Nolasco and Cristal Moreno-Aguilera

Appl. Syst. Innov. 2025, 8(4), 110; https://doi.org/10.3390/asi8040110 - 7 Aug 2025

Cited by 8 | Viewed by 11611

Abstract

The inclusion of the Internet of Things (IoT) in indoor agricultural systems has become a fundamental tool for improving cultivation systems by providing key information for decision-making in pursuit of better performance. This article presents the design and implementation of an IoT-based agricultural [...] Read more.

The inclusion of the Internet of Things (IoT) in indoor agricultural systems has become a fundamental tool for improving cultivation systems by providing key information for decision-making in pursuit of better performance. This article presents the design and implementation of an IoT-based agricultural system installed in a plant growth chamber for hydroponic cultivation under controlled conditions. The growth chamber is equipped with sensors for air temperature, relative humidity (RH), carbon dioxide (CO₂) and photosynthetically active photon flux, as well as control mechanisms such as humidifiers, full-spectrum Light Emitting Diode (LED) lamps, mini split air conditioner, pumps, a Wi-Fi surveillance camera, remote monitoring via a web application and three Nutrient Film Technique (NFT) hydroponic systems with a capacity of ten plants each. An ATmega2560 microcontroller manages the smart system using the MODBUS RS-485 communication protocol. To validate the proper functionality of the proposed system, a case study was conducted using lettuce crops, in which the impact of different nutrient solution concentrations (50%, 75% and 100%) on the phenotypic development and nutritional content of the plants was evaluated. The results obtained from the cultivation experiment, analyzed through analysis of variance (ANOVA), show that the treatment with 75% nutrient concentration provides an appropriate balance between resource use and nutritional quality, without affecting the chlorophyll content. This system represents a scalable and replicable alternative for protected agriculture. Full article

(This article belongs to the Special Issue Smart Sensors and Devices: Recent Advances and Applications Volume II)

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25 pages, 4186 KB

Open AccessEditor’s ChoiceReview

Total Productive Maintenance and Industry 4.0: A Literature-Based Path Toward a Proposed Standardized Framework

by Zineb Mouhib, Maryam Gallab, Safae Merzouk, Aziz Soulhi and Mario Di Nardo

Appl. Syst. Innov. 2025, 8(4), 98; https://doi.org/10.3390/asi8040098 - 21 Jul 2025

Cited by 6 | Viewed by 10855

Abstract

In the context of Industry 4.0, Total Productive Maintenance (TPM) is undergoing a major shift driven by digital technologies such as the IoT, AI, cloud computing, and Cyber–Physical systems. This study explores how these technologies reshape traditional TPM pillars and practices through a [...] Read more.

In the context of Industry 4.0, Total Productive Maintenance (TPM) is undergoing a major shift driven by digital technologies such as the IoT, AI, cloud computing, and Cyber–Physical systems. This study explores how these technologies reshape traditional TPM pillars and practices through a two-phase methodology: bibliometric analysis, which reveals global research trends, key contributors, and emerging themes, and a systematic review, which discusses how core TPM practices are being transformed by advanced technologies. It also identifies key challenges of this transition, including data aggregation, a lack of skills, and resistance. However, despite the growing body of research on digital TPM, a major gap persists: the lack of a standardized model applicable across industries. Existing approaches are often fragmented or too context-specific, limiting scalability. Addressing this gap requires a structured approach that aligns technological advancements with TPM’s foundational principles. Taking a cue from these findings, this article formulates a systematic and scalable framework for TPM 4.0 deployment. The framework is based on four pillars: modular technological architecture, phased deployment, workforce integration, and standardized performance indicators. The ultimate goal is to provide a basis for a universal digital TPM standard that enhances the efficiency, resilience, and efficacy of smart maintenance systems. Full article

(This article belongs to the Section Industrial and Manufacturing Engineering)

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32 pages, 1156 KB

Open AccessEditor’s ChoiceArticle

A Study of the Response Surface Methodology Model with Regression Analysis in Three Fields of Engineering

by Hsuan-Yu Chen and Chiachung Chen

Appl. Syst. Innov. 2025, 8(4), 99; https://doi.org/10.3390/asi8040099 - 21 Jul 2025

Cited by 50 | Viewed by 16259

Abstract

Researchers conduct experiments to discover factors influencing the experimental subjects, so the experimental design is essential. The response surface methodology (RSM) is a special experimental design used to evaluate factors significantly affecting a process and determine the optimal conditions for different factors. The [...] Read more.

Researchers conduct experiments to discover factors influencing the experimental subjects, so the experimental design is essential. The response surface methodology (RSM) is a special experimental design used to evaluate factors significantly affecting a process and determine the optimal conditions for different factors. The relationship between response values and influencing factors is mainly established using regression analysis techniques. These equations are then used to generate contour and surface response plots to provide researchers with further insights. The impact of regression techniques on response surface methodology (RSM) model building has not been studied in detail. This study uses complete regression techniques to analyze sixteen datasets from the literature on semiconductor manufacturing, steel materials, and nanomaterials. Whether each variable significantly affected the response value was assessed using backward elimination and a t-test. The complete regression techniques used in this study included considering the significant influencing variables of the model, testing for normality and constant variance, using predictive performance criteria, and examining influential data points. The results of this study revealed some problems with model building in RSM studies in the literature from three engineering fields, including the direct use of complete equations without statistical testing, deletion of variables with p-values above a preset value without further examination, existence of non-normality and non-constant variance conditions of the dataset without testing, and presence of some influential data points without examination. Researchers should strengthen training in regression techniques to enhance the RSM model-building process. Full article

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27 pages, 2527 KB

Open AccessEditor’s ChoiceReview

A Systematic Review of Responsible Artificial Intelligence Principles and Practice

by Lakshitha Gunasekara, Nicole El-Haber, Swati Nagpal, Harsha Moraliyage, Zafar Issadeen, Milos Manic and Daswin De Silva

Appl. Syst. Innov. 2025, 8(4), 97; https://doi.org/10.3390/asi8040097 - 21 Jul 2025

Cited by 25 | Viewed by 18674

Abstract

The accelerated development of Artificial Intelligence (AI) capabilities and systems is driving a paradigm shift in productivity, innovation and growth. Despite this generational opportunity, AI is fraught with significant challenges and risks. To address these challenges, responsible AI has emerged as a modus [...] Read more.

The accelerated development of Artificial Intelligence (AI) capabilities and systems is driving a paradigm shift in productivity, innovation and growth. Despite this generational opportunity, AI is fraught with significant challenges and risks. To address these challenges, responsible AI has emerged as a modus operandi that ensures protections while not stifling innovations. Responsible AI minimizes risks to people, society, and the environment. However, responsible AI principles and practice are impacted by ‘principle proliferation’ as they are diverse and distributed across the applications, stakeholders, risks, and downstream impact of AI systems. This article presents a systematic review of responsible AI principles and practice with the objectives of discovering the current state, the foundations and the need for responsible AI, followed by the principles of responsible AI, and translation of these principles into the responsible practice of AI. Starting with 22,711 relevant peer-reviewed articles from comprehensive bibliographic databases, the review filters through to 9700 at de-duplication, 5205 at abstract screening, 1230 at semantic screening and 553 at final full-text screening. The analysis of this final corpus is presented as six findings that contribute towards the increased understanding and informed implementation of responsible AI. Full article

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26 pages, 3622 KB

Open AccessEditor’s ChoiceArticle

Shear Strength Prediction for RCDBs Utilizing Data-Driven Machine Learning Approach: Enhanced CatBoost with SHAP and PDPs Analyses

by Imad Shakir Abbood, Noorhazlinda Abd Rahman and Badorul Hisham Abu Bakar

Appl. Syst. Innov. 2025, 8(4), 96; https://doi.org/10.3390/asi8040096 - 10 Jul 2025

Cited by 20 | Viewed by 2988

Abstract

Reinforced concrete deep beams (RCDBs) provide significant strength and serviceability for building structures. However, a simple, general, and universally accepted procedure for predicting their shear strength (SS) has yet to be established. This study proposes a novel data-driven approach to predicting the SS [...] Read more.

Reinforced concrete deep beams (RCDBs) provide significant strength and serviceability for building structures. However, a simple, general, and universally accepted procedure for predicting their shear strength (SS) has yet to be established. This study proposes a novel data-driven approach to predicting the SS of RCDBs using an enhanced CatBoost (CB) model. For this purpose, a newly comprehensive database of RCDBs with shear failure, including 950 experimental specimens, was established and adopted. The model was developed through a customized procedure including feature selection, data preprocessing, hyperparameter tuning, and model evaluation. The CB model was further evaluated against three data-driven models (e.g., Random Forest, Extra Trees, and AdaBoost) as well as three prominent mechanics-driven models (e.g., ACI 318, CSA A23.3, and EU2). Finally, the SHAP algorithm was employed for interpretation to increase the model’s reliability. The results revealed that the CB model yielded a superior accuracy and outperformed all other models. In addition, the interpretation results showed similar trends between the CB model and mechanics-driven models. The geometric dimensions and concrete properties are the most influential input features on the SS, followed by reinforcement properties. In which the SS can be significantly improved by increasing beam width and concert strength, and by reducing shear span-to-depth ratio. Thus, the proposed interpretable data-driven model has a high potential to be an alternative approach for design practice in structural engineering. Full article

(This article belongs to the Special Issue Recent Developments in Data Science and Knowledge Discovery)

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39 pages, 1072 KB

Open AccessEditor’s ChoiceArticle

Efficient BESS Scheduling in AC Microgrids via Multiverse Optimizer: A Grid-Dependent and Self-Powered Strategy to Minimize Power Losses and CO₂ Footprint

by Daniel Sanin-Villa, Hugo Alessandro Figueroa-Saavedra and Luis Fernando Grisales-Noreña

Appl. Syst. Innov. 2025, 8(3), 85; https://doi.org/10.3390/asi8030085 - 19 Jun 2025

Cited by 19 | Viewed by 2996

Abstract

This paper presents a novel energy management system for AC microgrids that integrates a parallel implementation of the Multi-Verse Optimizer (MVO) with the Successive Approximations method for power flow analysis. The proposed approach optimally schedules battery energy storage systems (BESSs) in both grid-connected [...] Read more.

This paper presents a novel energy management system for AC microgrids that integrates a parallel implementation of the Multi-Verse Optimizer (MVO) with the Successive Approximations method for power flow analysis. The proposed approach optimally schedules battery energy storage systems (BESSs) in both grid-connected and islanded modes, aiming to minimize energy losses and reduce

{CO}_{2}

emissions. Numerical evaluations on a 33-node AC microgrid demonstrate significant improvements: in the grid-dependent mode, energy losses drop from 2484.57 kWh (base case) to 2374.85 kWh, and emissions fall from 9.8874 Ton(CO₂) to 9.8693 Ton(CO₂). Under the self-powered configuration, energy losses and emissions are curtailed from 2484.57 kWh to 2373.53 kWh and from 16.0659 Ton(CO₂) to 16.0364 Ton(CO₂), respectively. The results highlight that the proposed method outperforms existing metaheuristics in solution quality and consistency. This work advances microgrid scheduling by ensuring technical feasibility, reducing carbon footprint, and maintaining voltage stability under diverse operational conditions. Full article

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27 pages, 10754 KB

Open AccessEditor’s ChoiceArticle

Efficient and Explainable Human Activity Recognition Using Deep Residual Network with Squeeze-and-Excitation Mechanism

by Sakorn Mekruksavanich and Anuchit Jitpattanakul

Appl. Syst. Innov. 2025, 8(3), 57; https://doi.org/10.3390/asi8030057 - 24 Apr 2025

Cited by 14 | Viewed by 5129

Abstract

Wearable sensors for human activity recognition (HAR) have gained significant attention across multiple domains, such as personal health monitoring and intelligent home systems. Despite notable advancements in deep learning for HAR, understanding the decision-making process of complex models remains challenging. This study introduces [...] Read more.

Wearable sensors for human activity recognition (HAR) have gained significant attention across multiple domains, such as personal health monitoring and intelligent home systems. Despite notable advancements in deep learning for HAR, understanding the decision-making process of complex models remains challenging. This study introduces an advanced deep residual network integrated with a squeeze-and-excitation (SE) mechanism to improve recognition accuracy and model interpretability. The proposed model, ConvResBiGRU-SE, was tested using the UCI-HAR and WISDM datasets. It achieved remarkable accuracies of 99.18% and 98.78%, respectively, surpassing existing state-of-the-art methods. The SE mechanism enhanced the model’s ability to focus on essential features, while gradient-weighted class activation mapping (Grad-CAM) increased interpretability by highlighting essential sensory data influencing predictions. Additionally, ablation experiments validated the contribution of each component to the model’s overall performance. This research advances HAR technology by offering a more transparent and efficient recognition system. The enhanced transparency and predictive accuracy may increase user trust and facilitate smoother integration into real-world applications. Full article

(This article belongs to the Special Issue Smart Sensors and Devices: Recent Advances and Applications Volume II)

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25 pages, 4755 KB

Open AccessEditor’s ChoiceArticle

Detecting Personally Identifiable Information Through Natural Language Processing: A Step Forward

by Luca Mainetti and Andrea Elia

Appl. Syst. Innov. 2025, 8(2), 55; https://doi.org/10.3390/asi8020055 - 18 Apr 2025

Cited by 14 | Viewed by 9498

Abstract

The protection of personally identifiable information (PII) is being increasingly demanded by customers and governments via data protection regulations. Private and public organizations store and exchange through the Internet a large amount of data that include the personal information of users, employees, and [...] Read more.

The protection of personally identifiable information (PII) is being increasingly demanded by customers and governments via data protection regulations. Private and public organizations store and exchange through the Internet a large amount of data that include the personal information of users, employees, and customers. While discovering PII from a large unstructured text corpus is still challenging, a lot of research work has focused on identifying methods and tools for the detection of PII in real-time scenarios and the ability to discover data exfiltration attacks. In those research attempts, natural language processing (NLP)-based schemas are widely adopted. Our work combines NLP with deep learning to identify PII in unstructured texts. NLP is used to extract semantic information and the syntactic structure of the text. This information is then processed by a pre-trained Bidirectional Encoder Representations from Transformers (BERT) algorithm. We achieved high performance in detecting PII, reaching an accuracy of 99.558%. This represents an improvement of 7.47 percentage points over the current state-of-the-art model that we analyzed. However, the experimental results show that there is still room for improvement to obtain better accuracy in detecting PII, including working on a new, balanced, and higher-quality training dataset for pre-trained models. Our study contributions encourage researchers to enhance NLP-based PII detection models and practitioners to transform those models into privacy detection tools to be deployed in security operation centers. Full article

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32 pages, 7418 KB

Open AccessEditor’s ChoiceArticle

Real-Time Large-Scale Intrusion Detection and Prevention System (IDPS) CICIoT Dataset Traffic Assessment Based on Deep Learning

by Samuel Kofi Erskine

Appl. Syst. Innov. 2025, 8(2), 52; https://doi.org/10.3390/asi8020052 - 11 Apr 2025

Cited by 19 | Viewed by 9626

Abstract

This research utilizes machine learning (ML), and especially deep learning (DL), techniques for efficient feature extraction of intrusion attacks. We use DL to provide better learning and utilize machine learning multilayer perceptron (MLP) as an intrusion detection (IDS) and intrusion prevention (IPS) system [...] Read more.

This research utilizes machine learning (ML), and especially deep learning (DL), techniques for efficient feature extraction of intrusion attacks. We use DL to provide better learning and utilize machine learning multilayer perceptron (MLP) as an intrusion detection (IDS) and intrusion prevention (IPS) system (IDPS) method. We deploy DL and MLP together as DLMLP. DLMLP improves the high detection of all intrusion attack features on the Internet of Things (IoT) device dataset, known as the CICIoT2023 dataset. We reference the CICIoT2023 dataset from the Canadian Institute of Cybersecurity (CIC) IoT device dataset. Our proposed method, the deep learning multilayer perceptron intrusion detection and prevention system model (DLMIDPSM), provides IDPST (intrusion detection and prevention system topology) capability. We use our proposed IDPST to capture, analyze, and prevent all intrusion attacks in the dataset. Moreover, our proposed DLMIDPSM employs a combination of artificial neural networks, ANNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Consequently, this project aims to develop a robust real-time intrusion detection and prevention system model. DLMIDPSM can predict, detect, and prevent intrusion attacks in the CICIoT2023 IoT dataset, with a high accuracy of above 85% and a high precision rate of 99%. Comparing the DLMIDPSM to the other literature, deep learning models and machine learning (ML) models have used decision tree (DT) and support vector machine (SVM), achieving a detection and prevention rate of 81% accuracy with only 72% precision. Furthermore, this research project breaks new ground by incorporating combined machine learning and deep learning models with IDPS capability, known as ML and DLMIDPSMs. We train, validate, or test the ML and DLMIDPSMs on the CICIoT2023 dataset, which helps to achieve higher accuracy and precision than the other deep learning models discussed above. Thus, our proposed combined ML and DLMIDPSMs achieved higher intrusion detection and prevention based on the confusion matrix’s high-rate attack detection and prevention values. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)

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18 pages, 2495 KB

Open AccessEditor’s ChoiceArticle

Development of a Control System for Pressure Distribution During Gas Production in a Structurally Complex Field

by Tatyana Kukharova, Pavel Maltsev and Igor Novozhilov

Appl. Syst. Innov. 2025, 8(2), 51; https://doi.org/10.3390/asi8020051 - 10 Apr 2025

Cited by 28 | Viewed by 2433

Abstract

In recent times, gas is becoming one of the most significant resources utilised worldwide. The continuous increase in demand requires an increase in the production and preparation of gas for further utilisation. Conventional sources cannot satisfy this need, so it is necessary to [...] Read more.

In recent times, gas is becoming one of the most significant resources utilised worldwide. The continuous increase in demand requires an increase in the production and preparation of gas for further utilisation. Conventional sources cannot satisfy this need, so it is necessary to resort to alternative methods of obtaining raw materials; one of the most promising is the development of unconventional reservoirs. The study considers a structurally complex gas-bearing reservoir; due to the peculiarities of the structure, the use of traditional approaches to gas production causes a number of difficulties and significantly reduces efficiency. A structurally inhomogeneous reservoir is considered a distributed object; a pressure field control system is synthesised. As a result, the efficiency of the system is evaluated, and its scalability is analysed. Full article

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16 pages, 5590 KB

Open AccessEditor’s ChoiceArticle

Experimental and Computational Study of the Aerodynamic Characteristics of a Darrieus Rotor with Asymmetrical Blades to Increase Turbine Efficiency Under Low Wind Velocity Conditions

by Muhtar Isataev, Rustem Manatbayev, Zhanibek Seydulla, Nurdaulet Kalassov, Ainagul Yershina and Zhandos Baizhuma

Appl. Syst. Innov. 2025, 8(2), 49; https://doi.org/10.3390/asi8020049 - 3 Apr 2025

Cited by 7 | Viewed by 3049

Abstract

In this study, we conducted experimental and numerical investigations of a Darrieus rotor with asymmetrical blades, which has two structural configurations—with and without horizontal parallel plates. Experimental tests were conducted in a wind tunnel at various air flow velocities (ranging from 3 m/s [...] Read more.

In this study, we conducted experimental and numerical investigations of a Darrieus rotor with asymmetrical blades, which has two structural configurations—with and without horizontal parallel plates. Experimental tests were conducted in a wind tunnel at various air flow velocities (ranging from 3 m/s to 15 m/s), measuring rotor rotation frequency, torque, and thrust force. The computational simulation used the ANSYS 2022 R2 Fluent software package, where CFD simulations of air flow around both rotor configurations were performed. The calculations employed the Realizable k-ε turbulence model, while an unstructured mesh with local refinement in the blade–flow interaction zones was used for grid generation. The study results showed that the rotor with horizontal parallel plates exhibits higher aerodynamic efficiency at low wind velocities compared to the no-plates rotor. The experimental findings indicated that at wind speeds of 3–6 m/s, the rotor with plates demonstrates 18–22% higher torque, which facilitates the self-start process and stabilizes turbine operation. The numerical simulations confirmed that horizontal plates contribute to stabilizing the air flow by reducing the intensity of vortex structures behind the blades, thereby decreasing aerodynamic drag and minimizing energy losses. It was also found that the presence of plates creates a directed flow effect, increasing the lift force on the blades and improving the power coefficient (Cp). In the case of the rotor without plates, the CFD simulations identified significant low-pressure zones and high turbulence regions behind the blades, leading to increased aerodynamic losses and reduced efficiency. Thus, the experimental and numerical modeling results confirm that the Darrieus rotor with horizontal parallel plates is a more efficient solution for operation under low and variable wind conditions. The optimized design with plates ensures more stable flow, reduces energy losses, and increases the turbine’s power coefficient. These findings may be useful for designing small-scale wind energy systems intended for areas with low wind speeds. Full article

(This article belongs to the Special Issue Wind Energy and Wind Turbine System)

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22 pages, 2821 KB

Open AccessEditor’s ChoiceReview

Pixel Circuit Designs for Active Matrix Displays

by Dan-Mei Wei, Hua Zheng, Chun-Hua Tan, Shenghao Zhang, Hua-Dan Li, Lv Zhou, Yuanrui Chen, Chenchen Wei, Miao Xu, Lei Wang, Wei-Jing Wu, Honglong Ning and Baohua Jia

Appl. Syst. Innov. 2025, 8(2), 46; https://doi.org/10.3390/asi8020046 - 31 Mar 2025

Cited by 13 | Viewed by 11669

Abstract

Pixel circuits are key components of flat panel displays, including liquid crystal displays (LCDs), organic light-emitting diode displays (OLEDs), and micro light-emitting diode displays (micro-LEDs). Depending on the active layer material of the thin film transistor (TFT), pixel circuits are categorised into amorphous [...] Read more.

Pixel circuits are key components of flat panel displays, including liquid crystal displays (LCDs), organic light-emitting diode displays (OLEDs), and micro light-emitting diode displays (micro-LEDs). Depending on the active layer material of the thin film transistor (TFT), pixel circuits are categorised into amorphous silicon (a-Si) technology, low-temperature polycrystalline silicon (LTPS) technology, metal oxide (MO) technology, and low-temperature polycrystalline silicon and oxide (LTPO) technology. In this review, we outline the fundamental display principles and four major TFT technologies, covering conventional single-gated TFTs to novel two-gated TFTs. We focus on novel pixel circuits for three glass-based display technologies with additional mention of pixel circuits for silicon-based OLED and silicon-based micro-LED. Full article

(This article belongs to the Section Control and Systems Engineering)

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35 pages, 5077 KB

Open AccessEditor’s ChoiceArticle

Data Management Maturity Model—Process Dimensions and Capabilities to Leverage Data-Driven Organizations Towards Industry 5.0

by Lara Pörtner, Andreas Riel, Benedikt Schmidt, Marcel Leclaire and Robert Möske

Appl. Syst. Innov. 2025, 8(2), 41; https://doi.org/10.3390/asi8020041 - 21 Mar 2025

Cited by 5 | Viewed by 12491

Abstract

Data-driven organizations aim to control business decisions based on data. However, despite significant investments in digitalization, studies show that many organizations continue to face challenges in fully realizing the benefits of data. Existing maturity models for digital transformation, data management, and data-driven organizations [...] Read more.

Data-driven organizations aim to control business decisions based on data. However, despite significant investments in digitalization, studies show that many organizations continue to face challenges in fully realizing the benefits of data. Existing maturity models for digital transformation, data management, and data-driven organizations lack a comprehensive, industry-agnostic, and practically validated approach to addressing industry challenges. This work introduces a refined data management maturity model developed using De Bruin’s maturity model assessment methodology. The model aims to incorporate all key elements of a data-driven organization, emphasizing the interdependencies required to evaluate maturity levels and provide targeted recommendations for addressing data-related challenges during the transition to a data-driven organization. An initial validation with 31 industry experts confirmed the model’s feasibility and practical applicability. As the next step, we plan to validate the model further by deploying the full questionnaire and deriving the maturity of each process dimension, along with its weighting, through assessments with industry partners from various sectors, including automotive, aviation, consumer goods/manufacturing, pharma, and media. Preliminary findings also underscored the importance of a deeper focus on the organization dimension, particularly in the context of Industry 5.0. Future research will refine the model through iterative development phases to address this critical area. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

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28 pages, 6705 KB

Open AccessEditor’s ChoiceArticle

Multimodal AI and Large Language Models for Orthopantomography Radiology Report Generation and Q&A

by Chirath Dasanayaka, Kanishka Dandeniya, Maheshi B. Dissanayake, Chandira Gunasena and Ruwan Jayasinghe

Appl. Syst. Innov. 2025, 8(2), 39; https://doi.org/10.3390/asi8020039 - 17 Mar 2025

Cited by 16 | Viewed by 6704

Abstract

Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative [...] Read more.

Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative solution to address these shortcomings while improving continuous patient support in dental healthcare. This study proposes a viable solution with the aid of multimodal artificial intelligence (AI) and large language models (LLMs), focusing on their application for generating orthopantomography radiology reports and answering questions in the dental domain. This work also discusses efficient adaptation methods of LLMs for specific language and application domains. The proposed system primarily consists of a Blip-2-based caption generator tuned on DPT images followed by a Llama 3 8B based LLM for radiology report generation. The performance of the entire system is evaluated in two ways. The diagnostic performance of the system achieved an overall accuracy of 81.3%, with specific detection rates of 87.9% for dental caries, 89.7% for impacted teeth, 88% for bone loss, and 81.8% for periapical lesions. Subjective evaluation of AI-generated radiology reports by certified dental professionals demonstrates an overall accuracy score of 7.5 out of 10. In addition, the proposed solution includes a question-answering platform in the native Sinhala language, alongside the English language, designed to function as a chatbot for dental-related queries. We hope that this platform will eventually bridge the gap between dental services and patients, created due to a lack of human resources. Overall, our proposed solution creates new opportunities for LLMs in healthcare by introducing a robust end-to-end system for the automated generation of dental radiology reports and enhancing patient interaction and awareness. Full article

(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)

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22 pages, 6439 KB

Open AccessEditor’s ChoiceArticle

Enhancing Education in Agriculture via XR-Based Digital Twins: A Novel Approach for the Next Generation

by Orestis Spyrou, Mar Ariza-Sentís and Sergio Vélez

Appl. Syst. Innov. 2025, 8(2), 38; https://doi.org/10.3390/asi8020038 - 17 Mar 2025

Cited by 30 | Viewed by 5571

Abstract

Integrating Artificial Intelligence (AI) and Extended Reality (XR) technologies into agriculture presents a transformative opportunity to modernize education and sustainable food production. Traditional agriculture training remains resource-intensive, time-consuming, and geographically restrictive, limiting scalability. This study explores an AI-driven Digital Twin (DT) system embedded [...] Read more.

Integrating Artificial Intelligence (AI) and Extended Reality (XR) technologies into agriculture presents a transformative opportunity to modernize education and sustainable food production. Traditional agriculture training remains resource-intensive, time-consuming, and geographically restrictive, limiting scalability. This study explores an AI-driven Digital Twin (DT) system embedded within a gamified XR environment designed to enhance decision-making, resource management, and practical training in viticulture as well as woody crop management. A survey among stakeholders in the viticultural sector revealed that participants are increasingly open to adopting Virtual Reality (VR) combined with AI-enhanced technologies, signaling a readiness for digital learning transformation in the field. The survey revealed a 4.48/7 willingness to adopt XR-based training, a 4.85/7 interest in digital solutions for precision agriculture, and a moderate climate change concern of 4.16/7, indicating a strong readiness for digital learning transformation. Our findings confirm that combining AI-powered virtual educators with DT simulations provides interactive, real-time feedback, allowing users to experiment with vineyard management strategies in a risk-free setting. Unlike previous studies focusing on crop monitoring or AI-based decision support, this study examines the potential of combining Digital Twins (DTs) with AI-driven personal assistants to improve decision-making, resource management, and overall productivity in agriculture. Proof-of-concept implementations in Unity and Oculus Quest 3 demonstrate how AI-driven NPC educators can personalize training, simulate climate adaptation strategies, and enhance stakeholder engagement. The research employs a design-oriented approach, integrating feedback from industry experts and end-users to refine the educational and practical applications of DTs in agriculture. Furthermore, this study highlights proof-of-concept implementations using the Unity cross game engine platform, showcasing virtual environments where students can interact with AI-powered educators in simulated vineyard settings. Digital innovations support students and farmers in enhancing crop yields and play an important role in educating the next generation of digital farmers. Full article

(This article belongs to the Special Issue Advanced Technologies and Methodologies in Education 4.0)

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19 pages, 6995 KB

Open AccessEditor’s ChoiceArticle

Investigating Stress During a Virtual Reality Game Through Fractal and Multifractal Analysis of Heart Rate Variability

by Penio Lebamovski and Evgeniya Gospodinova

Appl. Syst. Innov. 2025, 8(1), 16; https://doi.org/10.3390/asi8010016 - 21 Jan 2025

Cited by 8 | Viewed by 6046

Abstract

This article presents the process of creating a virtual reality (VR) game designed to assess the impact of stress on heart rate variability (HRV). The game features dynamic and challenging scenarios to induce stress responses, incorporating advanced 3D modelling and 3D animation techniques. [...] Read more.

This article presents the process of creating a virtual reality (VR) game designed to assess the impact of stress on heart rate variability (HRV). The game features dynamic and challenging scenarios to induce stress responses, incorporating advanced 3D modelling and 3D animation techniques. A study involving 20 volunteers was conducted, with electrocardiographic (ECG) data collected before and during game play. HRV analysis focused on fractal and multifractal characteristics, utilizing detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MFDFA) methods. DFA results revealed decreased values of α₁, α₂, and α_all, indicating alterations in short-term and long-term correlations under stress. MFDFA further analyzed changes in fluctuation function Fq(s), generalized Hurst exponent Hq, multifractal scaling exponent τ(q), and multifractal spectrum f(α), showing significant differences in these parameters under stress. These findings validate the game’s effectiveness in simulating stress and its impact on HRV. The present study not only demonstrates the relationship between stress and the fractal characteristics of HRV but also offers a new foundation for future applications in psychology, physiology, and the development of VR technologies for stress management. Full article

(This article belongs to the Section Information Systems)

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40 pages, 1517 KB

Open AccessEditor’s ChoiceReview

Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review

by Amjad Baset and Muhyiddine Jradi

Appl. Syst. Innov. 2025, 8(1), 5; https://doi.org/10.3390/asi8010005 - 27 Dec 2024

Cited by 36 | Viewed by 8059

Abstract

This review explores the novel integration of data-driven approaches, including artificial intelligence (AI) and machine learning (ML), in advancing building energy retrofits. This study uniquely emphasizes the emerging role of explainable AI (XAI) in addressing transparency and interpretability challenges, fostering the broader adoption [...] Read more.

This review explores the novel integration of data-driven approaches, including artificial intelligence (AI) and machine learning (ML), in advancing building energy retrofits. This study uniquely emphasizes the emerging role of explainable AI (XAI) in addressing transparency and interpretability challenges, fostering the broader adoption of data-driven solutions among stakeholders. A critical contribution of this review is its in-depth analysis of innovative applications of AI techniques to handle incomplete data, optimize energy performance, and predict retrofit outcomes with enhanced accuracy. Furthermore, the review identifies previously underexplored areas, such as scaling data-driven methods to diverse building typologies and incorporating future climate scenarios in retrofit planning. Future research directions include improving data availability and quality, developing scalable urban simulation tools, advancing modeling techniques to include life-cycle impacts, and creating practical decision-support systems that integrate economic and environmental metrics, paving the way for efficient and sustainable retrofitting solutions. Full article

(This article belongs to the Special Issue AI and Data-Driven Strategies for Control and Optimization of Building Energy Systems)

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14 pages, 302 KB

Open AccessEditor’s ChoiceReview

Robotics Applications in the Hospital Domain: A Literature Review

by Elijah M. G. N. Vera Cruz, Sancho Oliveira and Américo Correia

Appl. Syst. Innov. 2024, 7(6), 125; https://doi.org/10.3390/asi7060125 - 12 Dec 2024

Cited by 24 | Viewed by 18381

Abstract

Robotic systems are increasingly being used in healthcare. These systems improve patient care both by freeing healthcare professionals from repetitive tasks and by assisting them with complex procedures. This analysis examines the development and implementation of the use of robotic systems in healthcare. [...] Read more.

Robotic systems are increasingly being used in healthcare. These systems improve patient care both by freeing healthcare professionals from repetitive tasks and by assisting them with complex procedures. This analysis examines the development and implementation of the use of robotic systems in healthcare. It also examines the application of artificial intelligence (AI), which focuses on the autonomy of robotic systems, enabling them to perform tasks autonomously. It describes the main areas of use of robots in hospitals, gives examples of the main commercial or research robots, and analyzes the main practical and safety issues associated with the use of these systems. Using the main databases, including PubMed, IEEE Xplore, MDPI, ScienceDirect, ACM Digital Library, BioMed Central, Springer, and others, an extensive search for papers related to the topic was conducted. This resulted in 59 papers being identified as eligible for this review. The article concludes with a discussion of future research areas that will ensure the effective integration of autonomous robotic systems in healthcare. Full article

28 pages, 3568 KB

Open AccessEditor’s ChoiceSystematic Review

eXplainable Artificial Intelligence in Process Engineering: Promises, Facts, and Current Limitations

by Luigi Piero Di Bonito, Lelio Campanile, Francesco Di Natale, Michele Mastroianni and Mauro Iacono

Appl. Syst. Innov. 2024, 7(6), 121; https://doi.org/10.3390/asi7060121 - 30 Nov 2024

Cited by 19 | Viewed by 9356

Abstract

Artificial Intelligence (AI) has been swiftly incorporated into the industry to become a part of both customer services and manufacturing operations. To effectively address the ethical issues now being examined by the government, AI models must be explainable in order to be used [...] Read more.

Artificial Intelligence (AI) has been swiftly incorporated into the industry to become a part of both customer services and manufacturing operations. To effectively address the ethical issues now being examined by the government, AI models must be explainable in order to be used in both scientific and societal contexts. The current state of eXplainable artificial intelligence (XAI) in process engineering is examined in this study through a systematic literature review (SLR), with particular attention paid to the technology’s effect, degree of adoption, and potential to improve process and product quality. Due to restricted access to sizable, reliable datasets, XAI research in process engineering is still primarily exploratory or propositional, despite noteworthy applicability in well-known case studies. According to our research, XAI is becoming more and more positioned as a tool for decision support, with a focus on robustness and dependability in process optimization, maintenance, and quality assurance. This study, however, emphasizes that the use of XAI in process engineering is still in its early stages, and there is significant potential for methodological development and wider use across technical domains. Full article

(This article belongs to the Section Artificial Intelligence)

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21 pages, 2804 KB

Open AccessEditor’s ChoiceArticle

Addressing the Interoperability of Electronic Health Records: The Technical and Semantic Interoperability, Preserving Privacy and Security Framework

by Adetunji Ademola, Carlisle George and Glenford Mapp

Appl. Syst. Innov. 2024, 7(6), 116; https://doi.org/10.3390/asi7060116 - 19 Nov 2024

Cited by 21 | Viewed by 20012

Abstract

Interoperability has become crucial in the world of electronic health records, allowing for seamless data exchange and integration across diverse settings. It facilitates the integration of disparate systems, ensures that patient records are accessible, and enhances the care-delivery process. The current interoperability landscape [...] Read more.

Interoperability has become crucial in the world of electronic health records, allowing for seamless data exchange and integration across diverse settings. It facilitates the integration of disparate systems, ensures that patient records are accessible, and enhances the care-delivery process. The current interoperability landscape of electronic health records is saddled with challenges hindering efficient interoperability. Existing interoperability frameworks have not adequately addressed many of the challenges relating to data exchange, security and privacy. To address these challenges, the TASIPPS (Technical and Semantic Interoperability, Preserving Privacy and Security) framework is proposed as a comprehensive approach to achieving efficient interoperability. The TASIPPS framework integrates robust security and privacy measures, providing real-time access to electronic health records that enable precise diagnoses, timely treatment plans and improved patient outcomes. The TASIPPS framework offers a holistic and effective solution to healthcare interoperability challenges. A comparison of the framework with existing frameworks showed that the TASIPPS framework addresses key limitations in privacy, security, and scalability, while providing enhanced interoperability across distinct healthcare systems, positioning it as a more comprehensive solution for modern healthcare needs. Full article

(This article belongs to the Section Medical Informatics and Healthcare Engineering)

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30 pages, 459 KB

Open AccessEditor’s ChoiceReview

Factors Impacting the Adoption and Acceptance of ChatGPT in Educational Settings: A Narrative Review of Empirical Studies

by Mousa Al-kfairy

Appl. Syst. Innov. 2024, 7(6), 110; https://doi.org/10.3390/asi7060110 - 7 Nov 2024

Cited by 98 | Viewed by 25108

Abstract

This narrative review synthesizes and analyzes empirical studies on the adoption and acceptance of ChatGPT in higher education, addressing the need to understand the key factors influencing its use by students and educators. Anchored in theoretical frameworks such as the Technology Acceptance Model [...] Read more.

This narrative review synthesizes and analyzes empirical studies on the adoption and acceptance of ChatGPT in higher education, addressing the need to understand the key factors influencing its use by students and educators. Anchored in theoretical frameworks such as the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DoI) Theory, Technology–Organization–Environment (TOE) model, and Theory of Planned Behavior, this review highlights the central constructs shaping adoption behavior. The confirmed factors include hedonic motivation, usability, perceived benefits, system responsiveness, and relative advantage, whereas the effects of social influence, facilitating conditions, privacy, and security vary. Conversely, technology readiness and extrinsic motivation remain unconfirmed as consistent predictors. This study employs a qualitative synthesis of 40 peer-reviewed empirical studies, applying thematic analysis to uncover patterns in the factors driving ChatGPT adoption. The findings reveal that, while the traditional technology adoption models offer valuable insights, a deeper exploration of the contextual and psychological factors is necessary. The study’s implications inform future research directions and institutional strategies for integrating AI to support educational innovation. Full article

(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)

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49 pages, 5929 KB

Open AccessEditor’s ChoiceReview

Innovating Patent Retrieval: A Comprehensive Review of Techniques, Trends, and Challenges in Prior Art Searches

by Amna Ali, Ali Tufail, Liyanage Chandratilak De Silva and Pg Emeroylariffion Abas

Appl. Syst. Innov. 2024, 7(5), 91; https://doi.org/10.3390/asi7050091 - 26 Sep 2024

Cited by 35 | Viewed by 15152

Abstract

As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents [...] Read more.

As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents significant challenges due to its volume and complexity. This systematic literature review surveys patent retrieval techniques over the past decade, focusing on ‘prior art’ and ‘novelty’ searches. Adhering to the PRISMA 2020 guidelines, our research includes 78 pertinent articles selected from a corpus of 1441, providing an in-depth overview of recent advancements, emerging trends, challenges, and future directions in the field of patent prior art retrieval. The review addresses six research questions: defining the current state of the art, evaluating the efficacy of various approaches, examining commonly used patent data collections, exploring the impact of semantic search and natural language processing (NLP) technologies, identifying frequently used components of patent documents, and discussing ongoing challenges in the domain of patent prior art search and retrieval. Our findings highlight the growing use of NLP to enhance the precision and comprehensiveness of patent searches, particularly on the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) and the United States Patent and Trademark Office (USPTO) databases. Despite advancements, the specialized and technical nature of patent language continues to pose significant challenges in achieving high accuracy in patent retrieval. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)

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24 pages, 3726 KB

Open AccessEditor’s ChoiceReview

Machine Learning and Deep Learning Models for Demand Forecasting in Supply Chain Management: A Critical Review

by Kaoutar Douaioui, Rachid Oucheikh, Othmane Benmoussa and Charif Mabrouki

Appl. Syst. Innov. 2024, 7(5), 93; https://doi.org/10.3390/asi7050093 - 26 Sep 2024

Cited by 87 | Viewed by 53033

Abstract

This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) models used for demand forecasting in supply chain management. By analyzing 119 papers from the Scopus database covering the period from 2015 to 2024, this study provides both macro- [...] Read more.

This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) models used for demand forecasting in supply chain management. By analyzing 119 papers from the Scopus database covering the period from 2015 to 2024, this study provides both macro- and micro-level insights into the effectiveness of AI-based methodologies. The macro-level analysis illustrates the overall trajectory and trends in ML and DL applications, while the micro-level analysis explores the specific distinctions and advantages of these models. This review aims to serve as a valuable resource for improving demand forecasting in supply chain management using ML and DL techniques. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)

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23 pages, 4841 KB

Open AccessEditor’s ChoiceArticle

Neural Network System for Predicting Anomalous Data in Applied Sensor Systems

by Serhii Vladov, Victoria Vysotska, Valerii Sokurenko, Oleksandr Muzychuk, Mariia Nazarkevych and Vasyl Lytvyn

Appl. Syst. Innov. 2024, 7(5), 88; https://doi.org/10.3390/asi7050088 - 23 Sep 2024

Cited by 36 | Viewed by 3286

Abstract

This article advances the research on the intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within the helicopter turboshaft engine monitoring and control expert system. A SARIMAX-based preprocessor [...] Read more.

This article advances the research on the intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within the helicopter turboshaft engine monitoring and control expert system. A SARIMAX-based preprocessor model was developed to determine autocorrelation and partial autocorrelation in training data, accounting for dynamic changes and external factors, achieving a prediction accuracy of up to 97.9%. A modified LSTM-based predictor model with Dropout and Dense layers predicted sensor data, with a tested error margin of 0.218% for predicting the TV3-117 aircraft engine gas temperature values before the compressor turbine during one minute of helicopter flight. A reconstructor model restored missing time series values and replaced outliers with synthetic values, achieving up to 98.73% accuracy. An anomaly detector model using the concept of dissonance successfully identified two anomalies: a sensor malfunction and a sharp temperature drop within two minutes of sensor activity, with type I and II errors below 1.12 and 1.01% and a detection time under 1.611 s. The system’s AUC-ROC value of 0.818 confirms its strong ability to differentiate between normal and anomalous data, ensuring reliable and accurate anomaly detection. The limitations involve the dependency on the quality of data from onboard sensors, affected by malfunctions or noise, with the LSTM network’s accuracy (up to 97.9%) varying with helicopter conditions, and the model’s high computational demand potentially limiting real-time use in resource-constrained environments. Full article

(This article belongs to the Section Information Systems)

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11 pages, 3172 KB

Open AccessEditor’s ChoiceArticle

AI Asthma Guard: Predictive Wearable Technology for Asthma Management in Vulnerable Populations

by Hajar Almuhanna, Manayer Alenezi, Mariam Abualhasan, Shouq Alajmi, Raghad Alfadhli and Abdullah S. Karar

Appl. Syst. Innov. 2024, 7(5), 78; https://doi.org/10.3390/asi7050078 - 30 Aug 2024

Cited by 18 | Viewed by 10878

Abstract

This paper presents AI Asthma Guard, a novel wearable device designed to predict and alert users of impending asthma attacks using artificial intelligence. The system integrates physiological and environmental sensors to monitor health metrics such as the heart rate, oxygen saturation, and exposure [...] Read more.

This paper presents AI Asthma Guard, a novel wearable device designed to predict and alert users of impending asthma attacks using artificial intelligence. The system integrates physiological and environmental sensors to monitor health metrics such as the heart rate, oxygen saturation, and exposure to specific air pollutants, which are crucial in managing asthma in children and individuals with mental disabilities. Utilizing machine learning models, including support vector machines and random forest, AI Asthma Guard classifies the risk levels of asthma attacks and provides timely notifications. This study details the device’s design, implementation, and preliminary testing results, underscoring its potential to improve health outcomes by enabling proactive asthma management. The implications of this technology reflect its alignment with the Sustainable Development Goals by enhancing individual health and well-being. The integration of a companion app leveraging large language models like ChatGPT facilitates user interaction, providing personalized advice and educational content about asthma management. Full article

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14 pages, 7195 KB

Open AccessFeature PaperEditor’s ChoiceArticle

RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction

by Alaa Eleyan, Ebrahim AlBoghbaish, Abdulwahab AlShatti, Ahmad AlSultan and Darbi AlDarbi

Appl. Syst. Innov. 2024, 7(5), 77; https://doi.org/10.3390/asi7050077 - 29 Aug 2024

Cited by 30 | Viewed by 7102

Abstract

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, [...] Read more.

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future. Full article

(This article belongs to the Special Issue Advancing Healthcare Through Intelligent Clinical Decision Support Systems: Techniques, Applications, and Future Directions)

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26 pages, 7436 KB

Open AccessEditor’s ChoiceArticle

Use of Digital Technology in Integrated Mathematics Education

by Andrada-Livia Cirneanu and Cristian-Emil Moldoveanu

Appl. Syst. Innov. 2024, 7(4), 66; https://doi.org/10.3390/asi7040066 - 26 Jul 2024

Cited by 45 | Viewed by 18906

Abstract

Digital learning environments create a dynamic and engaging learning and teaching context that promotes a deeper understanding of complex concepts, eases the teaching process and fosters a passion for learning. Moreover, integrating interactive materials into pilot courses can assist teachers in better assessing [...] Read more.

Digital learning environments create a dynamic and engaging learning and teaching context that promotes a deeper understanding of complex concepts, eases the teaching process and fosters a passion for learning. Moreover, integrating interactive materials into pilot courses can assist teachers in better assessing student learning and adjusting their teaching strategies accordingly. The teachers can also receive valuable insights into students’ strengths and weaknesses, allowing them to provide targeted support and intervention when needed. For students from the defence and security fields, digital learning environments can create realistic simulations and virtual training scenarios that allow students to practise their skills in a controlled and safe environment, develop hands-on experience, and enhance their decision-making abilities without the need for real-world training exercises. In this context, the purpose of this paper is to introduce an approach for solving mathematical problems embedded in technical scenarios within the defence and security fields with the aid of digital technology using different software environments such as Python, Matlab, or SolidWorks. In this way, students can visualise abstract concepts, experiment with different scenarios, and receive instant feedback on their understanding. At the same time, the use of didactic and interactive materials can increase the interest among students and teachers for utilising mathematical models and digital technologies in the educational process. This paper also helps to reinforce key concepts and enhance problem-solving skills, sparking curiosity and creativity, and encouraging active participation and collaboration. Throughout the development of this proposal, based on survey analysis, good practices are presented, and advice for improvement is collected while having a wide range of users giving feedback, and participating in discussions and testing (pilot) short-term learning/teaching/training activities. Full article

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16 pages, 1038 KB

Open AccessEditor’s ChoiceArticle

Examining Hybrid Nanofluid Flow Dynamics in the Conical Gap between a Rotating Disk and Cone Surface: An Artificial Neural Network Approach

by Julien Moussa H. Barakat, Zaher Al Barakeh and Raymond Ghandour

Appl. Syst. Innov. 2024, 7(4), 63; https://doi.org/10.3390/asi7040063 - 22 Jul 2024

Cited by 15 | Viewed by 3821

Abstract

To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg–Marquardt neural networks (LM-NNs). A unique hybrid nanoliquid comprising aluminum oxide, [...] Read more.

To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg–Marquardt neural networks (LM-NNs). A unique hybrid nanoliquid comprising aluminum oxide,

A l_{2} O_{3}

, nanoparticles and copper,

C u

, nanoparticles is specifically addressed. Through the application of similarity transformations, the mathematical model formulated in terms of partial differential equations (PDEs) is converted into ordinary differential equations (ODEs). The BVP4C method is employed to generate a dataset encompassing various TRHNF-DC scenarios by varying magnetic parameters and nanoparticles. Subsequently, the intelligent LM-NN solver is trained, tested, and validated to ascertain the TRHNF-DC solution under diverse conditions. The accuracy of the LM-NN approach in solving the TRHNF-DC model is verified through different analyses, demonstrating a high level of accuracy, with discrepancies ranging from

10^{- 10}

to

10^{- 8}

when compared with standard solutions. The efficacy of the framework is further underscored by the close agreement of recommended outcomes with reference solutions, thereby validating its integrity. Full article

(This article belongs to the Section Artificial Intelligence)

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29 pages, 5934 KB

Open AccessEditor’s ChoiceArticle

The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks

by Serhii Vladov, Ruslan Yakovliev, Victoria Vysotska, Mariia Nazarkevych and Vasyl Lytvyn

Appl. Syst. Innov. 2024, 7(3), 53; https://doi.org/10.3390/asi7030053 - 20 Jun 2024

Cited by 36 | Viewed by 3465

Abstract

The research aims to develop a neural network-based lost information restoration method when the complex nonlinear technical object (using the example of helicopter turboshaft engines) sensors fail during operation. The basis of the research is an auto-associative neural network (autoencoder), which makes it [...] Read more.

The research aims to develop a neural network-based lost information restoration method when the complex nonlinear technical object (using the example of helicopter turboshaft engines) sensors fail during operation. The basis of the research is an auto-associative neural network (autoencoder), which makes it possible to restore lost information due to the sensor failure with an accuracy of more than 99%. An auto-associative neural network (autoencoder)-modified training method is proposed. It uses regularization coefficients that consist of the loss function to create a more stable and common model. It works well on the training sample of data and can produce good results on new data. Also, it reduces its overtraining risk when it adapts too much to the training data sample and loses its ability to generalize new data. This is especially important for small amounts of data or complex models. It has been determined based on the computational experiment results (the example of the TV3-117 turboshaft engine) that lost information restoration based on an auto-associative neural network provides a data restoring error of no more than 0.45% in the case of single failures and no more than 0.6% in case of double failures of the engine parameter registration sensor event. Full article

(This article belongs to the Section Information Systems)

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46 pages, 4394 KB

Open AccessEditor’s ChoiceArticle

Empowering Healthcare: A Comprehensive Guide to Implementing a Robust Medical Information System—Components, Benefits, Objectives, Evaluation Criteria, and Seamless Deployment Strategies

by Ana-Maria Ștefan, Nicu-Răzvan Rusu, Elena Ovreiu and Mihai Ciuc

Appl. Syst. Innov. 2024, 7(3), 51; https://doi.org/10.3390/asi7030051 - 14 Jun 2024

Cited by 32 | Viewed by 40189

Abstract

In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components [...] Read more.

In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components that constitute this innovative system, we explore its fundamental architecture and how each element contributes to seamless information flow. The benefits of adopting a medical information system are highlighted, emphasizing improved patient care, streamlined processes, and enhanced decision making for healthcare professionals. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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57 pages, 30349 KB

Open AccessEditor’s ChoiceReview

Recent Trends of Authentication Methods in Extended Reality: A Survey

by Louisa Hallal, Jason Rhinelander and Ramesh Venkat

Appl. Syst. Innov. 2024, 7(3), 45; https://doi.org/10.3390/asi7030045 - 28 May 2024

Cited by 21 | Viewed by 11078

Abstract

Extended Reality (XR) is increasingly gaining momentum in industries such as retail, health, and education. To protect users’ personal data, establishing a secure authentication system for XR devices becomes essential. Recently, the focus on authentication methods for XR devices has been limited. To [...] Read more.

Extended Reality (XR) is increasingly gaining momentum in industries such as retail, health, and education. To protect users’ personal data, establishing a secure authentication system for XR devices becomes essential. Recently, the focus on authentication methods for XR devices has been limited. To further our understanding of this topic, we surveyed authentication schemes, particularly systems and methods deployed in XR settings. In this survey, we focused on reviewing and evaluating papers published during the last decade (between 2014 and 2023). We compared knowledge-based authentication, physical biometrics, behavioral biometrics, and multi-model methods in terms of accuracy, security, and usability. We also highlighted the benefits and drawbacks of those methods. These highlights will direct future Human–computer Interaction (HCI) and security research to develop secure, reliable, and practical authentication systems. Full article

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13 pages, 1512 KB

Open AccessEditor’s ChoiceArticle

Technology Readiness Levels (TRLs) in the Era of Co-Creation

by Sofia Yfanti and Nikos Sakkas

Appl. Syst. Innov. 2024, 7(2), 32; https://doi.org/10.3390/asi7020032 - 16 Apr 2024

Cited by 52 | Viewed by 17623

Abstract

Technology readiness levels (TRLs) is a well-established and widely used approach for defining the readiness of new technology. It assesses technology maturity against specific benchmarks, ranging from level 1 (concept) to level 9 (market solution). Although this is a useful classification service that [...] Read more.

Technology readiness levels (TRLs) is a well-established and widely used approach for defining the readiness of new technology. It assesses technology maturity against specific benchmarks, ranging from level 1 (concept) to level 9 (market solution). Although this is a useful classification service that allows us to establish a common language, there are cases where we find that this conceptual approach cannot adequately highlight the maturity of certain innovative endeavors and effectively steer their development to higher TRLs. We will present an empirical case where the TRL approach presented a critical shortcoming in highlighting the true and effective readiness of a specific technological development and could not suggest the next natural step in ascending the maturity ladder. We will seek to generalize for the case of co-creation at large, analyze why co-creation may be poorly serviced by the current TRL model, and suggest an amendment that would allow the observed shortcomings of the traditional TRL approach to be overcome and its use extended into such co-creative settings, thus allowing stakeholders to enhance the effectiveness and impact of their collaborative innovation efforts. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 2686 KB

Open AccessEditor’s ChoiceArticle

Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control

by Lucas Schmidt Goecks, Anderson Felipe Habekost, Antonio Maria Coruzzolo and Miguel Afonso Sellitto

Appl. Syst. Innov. 2024, 7(2), 24; https://doi.org/10.3390/asi7020024 - 16 Mar 2024

Cited by 44 | Viewed by 15691

Abstract

Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration [...] Read more.

Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

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