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Background and Objectives: Sarcopenia is a progressive loss of skeletal muscle mass and function in elderly adults, posing a significant risk of frailty, falls, and morbidity. The current study designs and evaluates SarcoNet, a novel artificial neural network (ANN)-based classification framework developed in order to classify Sarcopenic from non-Sarcopenic subjects using a comprehensive real-time dataset. Methods: This pilot study involved 30 subjects, who were divided into Sarcopenic and non-Sarcopenic groups based on physician assessment. The collected dataset consists of thirty-one clinical parameters like skeletal muscle mass, which is collected using various equipment such as Body Composition Analyser, along with ten kinetic features which are derived from video-based gait analysis of joint angles obtained during walking on three terrain types such as slope, steps, and parallel path. The performance of the designed ANN-based SarcoNet was benchmarked against the traditional machine learning classifiers utilised including Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), and Random Forest (RF), as well as hard and soft voting ensemble classifiers. Results: SarcoNet achieved the highest overall classification accuracy of about 94%, with a specificity and precision of about 100%, an F1-score of about 92.4%, and an AUC of 0.94, outperforming all other models. The incorporation of lower-limb joint kinetics such as knee flexion, extension, ankle plantarflexion and dorsiflexion significantly enhanced predictive capability of the model and thus reflecting the functional deterioration characteristic of muscles in Sarcopenia. Conclusions: SarcoNet provides a promising AI-driven solution in Sarcopenia diagnosis, especially in low-resource healthcare settings. Future work will focus on improving the dataset, validating the model across diverse populations, and incorporating explainable AI to improve clinical adoption. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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28 pages, 492 KB

Open AccessReview

Inflammatory and Oxidative Biological Profiles in Mental Disorders: Perspectives on Diagnostics and Personalized Therapy

by Izabela Woźny-Rasała and Ewa Alicja Ogłodek

Int. J. Mol. Sci. 2025, 26(19), 9654; https://doi.org/10.3390/ijms26199654 (registering DOI) - 3 Oct 2025

Abstract

Personalized psychiatry represents an innovative therapeutic approach that integrates biological, genetic, and clinical data to optimize the treatment of mental disorders. Laboratory diagnostics play a fundamental role in this process by providing precise biomarkers that characterize pathophysiological mechanisms such as neuroinflammatory processes, oxidative stress, dysfunction of the Hypothalamic–Pituitary–Adrenal (HPA) axis, as well as disturbances in neuroplasticity and neurodegeneration. This article discusses the use of advanced analytical techniques, such as immunoenzymatic assays for pro-inflammatory cytokines (Interleukin-1β- IL-1β; Interleukin-6-IL-6; Interleukin-18-IL-18; and Tumor Necrosis Factor- α - TNF-α). It also emphasizes the role of pharmacogenomic diagnostics in the individualization of psychotropic therapy. Interdisciplinary collaboration between laboratory diagnosticians and clinicians supports the potential for multidimensional analysis of biomarker data in a clinical context, which supports precise patient profiling and monitoring of treatment responses. Despite progress, there are limitations, such as the lack of standardization in measurement methods, insufficient biomarker validation, and limited availability of tests in clinical practice. Development prospects include the integration of multi-marker panels, the use of point-of-care diagnostics, and the implementation of artificial intelligence tools for the analysis of multidimensional data. As a result, laboratory diagnostics are becoming an integral element of personalized psychiatry, enabling a better understanding of the neurobiology of mental disorders and the implementation of more effective therapeutic strategies. Full article

(This article belongs to the Section Molecular Genetics and Genomics)

21 pages, 636 KB

Open AccessArticle

Applying the Agent-Deed-Consequence (ADC) Model to Smart City Ethics

by Daniel Shussett and Veljko Dubljević

Algorithms 2025, 18(10), 625; https://doi.org/10.3390/a18100625 (registering DOI) - 3 Oct 2025

Abstract

Smart cities are an emerging technology that is receiving new ethical attention due to recent advancements in artificial intelligence. This paper provides an overview of smart city ethics while simultaneously performing novel theorization about the definition of smart cities and the complicated relationship between (smart) cities, ethics, and politics. We respond to these ethical issues by providing an innovative representation of the agent-deed-consequence (ADC) model in symbolic terms through deontic logic. The ADC model operationalizes human moral intuitions underpinning virtue ethics, deontology, and utilitarianism. With the ADC model made symbolically representable, human moral intuitions can be built into the algorithms that govern autonomous vehicles, social robots in healthcare settings, and smart city projects. Once the paper has introduced the ADC model and its symbolic representation through deontic logic, it demonstrates the ADC model’s promise for algorithmic ethical decision-making in four dimensions of smart city ethics, using examples relating to public safety and waste management. We particularly emphasize ADC-enhanced ethical decision-making in (economic and social) sustainability by advancing an understanding of smart cities and human-AI teams (HAIT) as group agents. The ADC model has significant merit in algorithmic ethical decision-making, especially through its elucidation in deontic logic. Algorithmic ethical decision-making, if structured by the ADC model, successfully addresses a significant portion of the perennial questions in smart city ethics, and smart cities built with the ADC model may in fact be a significant step toward resolving important social dilemmas of our time. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))

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37 pages, 3630 KB

Open AccessReview

Adaptive Antenna for Maritime LoRaWAN: A Systematic Review on Performance, Energy Efficiency, and Environmental Resilience

by Martine Lyimo, Bonny Mgawe, Judith Leo, Mussa Dida and Kisangiri Michael

Sensors 2025, 25(19), 6110; https://doi.org/10.3390/s25196110 (registering DOI) - 3 Oct 2025

Abstract

Long Range Wide Area Network (LoRaWAN) has become an attractive option for maritime communication because it is low-cost, long-range, and energy-efficient. Yet its performance at sea is often limited by fading, interference, and the strict energy budgets of maritime Internet of Things (IoT) devices. This review, prepared in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, examines 23 peer-reviewed studies published between 2019 and 2025 that explore adaptive antenna solutions for LoRaWAN in marine environments. The work covered four main categories: switched-beam, phased array, reconfigurable, and Artificial Intelligence or Machine Learning (AI/ML)-enabled antennas. Results across studies show that adaptive approaches improve gain, beam agility, and signal reliability even under unstable conditions. Switched-beam antennas dominate the literature (45%), followed by phased arrays (30%), reconfigurable designs (20%), and AI/ML-enabled systems (5%). Unlike previous reviews, this study emphasizes maritime propagation, environmental resilience, and energy use. Despite encouraging results in signal-to-noise ratio (SNR), packet delivery, and coverage range, clear gaps remain in protocol-level integration, lightweight AI for constrained nodes, and large-scale trials at sea. Research on reconfigurable intelligent surfaces (RIS) in maritime environments remains limited. However, these technologies could play an important role in enhancing spectral efficiency, coverage, and the scalability of maritime IoT networks. Full article

(This article belongs to the Special Issue LoRa Communication Technology for IoT Applications—2nd Edition)

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5 pages, 155 KB

Open AccessEditorial

AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning

by Mourad Kharbach

Foods 2025, 14(19), 3415; https://doi.org/10.3390/foods14193415 (registering DOI) - 3 Oct 2025

Abstract

The landscape of food analysis is being reshaped by the transformative power of data handling tools, including chemometrics, machine learning, and artificial intelligence (AI) [...] Full article

(This article belongs to the Special Issue AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning)

24 pages, 1454 KB

Open AccessArticle

AI-Driven Monitoring for Fish Welfare in Aquaponics: A Predictive Approach

by Jorge Saúl Fandiño Pelayo, Luis Sebastián Mendoza Castellanos, Rocío Cazes Ortega and Luis G. Hernández-Rojas

Sensors 2025, 25(19), 6107; https://doi.org/10.3390/s25196107 (registering DOI) - 3 Oct 2025

Abstract

This study addresses the growing need for intelligent monitoring in aquaponic systems by developing a predictive system based on artificial intelligence and environmental sensing. The goal is to improve fish welfare through the early detection of adverse water conditions. The system integrates low-cost digital sensors to continuously measure key physicochemical variables—pH, dissolved oxygen, and temperature—using these as inputs for real-time classification of fish health status. Four supervised machine learning models were evaluated: linear discriminant analysis (LDA), support vector machines (SVMs), neural networks (NNs), and random forest (RF). A dataset of 1823 instances was collected over eight months from a red tilapia aquaponic setup. The random forest model yielded the highest classification accuracy (99%), followed by NN (98%) and SVM (97%). LDA achieved 82% accuracy. Performance was validated using 5-fold cross-validation and label permutation tests to confirm model robustness. These results demonstrate that sensor-based predictive models can reliably detect early signs of fish stress or mortality, supporting the implementation of intelligent environmental monitoring and automation strategies in sustainable aquaponic production. Full article

(This article belongs to the Section Environmental Sensing)

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45 pages, 7902 KB

Open AccessReview

Artificial Intelligence-Guided Supervised Learning Models for Photocatalysis in Wastewater Treatment

by Asma Rehman, Muhammad Adnan Iqbal, Mohammad Tauseef Haider and Adnan Majeed

AI 2025, 6(10), 258; https://doi.org/10.3390/ai6100258 (registering DOI) - 3 Oct 2025

Abstract

Artificial intelligence (AI), when integrated with photocatalysis, has demonstrated high predictive accuracy in optimizing photocatalytic processes for wastewater treatment using a variety of catalysts such as TiO₂, ZnO, CdS, Zr, WO₂, and CeO₂. The progress of research in this area is greatly enhanced by advancements in data science and AI, which enable rapid analysis of large datasets in materials chemistry. This article presents a comprehensive review and critical assessment of AI-based supervised learning models, including support vector machines (SVMs), artificial neural networks (ANNs), and tree-based algorithms. Their predictive capabilities have been evaluated using statistical metrics such as the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), with numerous investigations documenting R² values greater than 0.95 and RMSE values as low as 0.02 in forecasting pollutant degradation. To enhance model interpretability, Shapley Additive Explanations (SHAP) have been employed to prioritize the relative significance of input variables, illustrating, for example, that pH and light intensity frequently exert the most substantial influence on photocatalytic performance. These AI frameworks not only attain dependable predictions of degradation efficiency for dyes, pharmaceuticals, and heavy metals, but also contribute to economically viable optimization strategies and the identification of novel photocatalysts. Overall, this review provides evidence-based guidance for researchers and practitioners seeking to advance wastewater treatment technologies by integrating supervised machine learning with photocatalysis. Full article

(This article belongs to the Special Issue The Application of Machine Learning and AI Technology Towards the Sustainable Development Goals)

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17 pages, 312 KB

Open AccessReview

Current Applications and Future Directions of Technologies Used in Adult Deformity Surgery for Personalized Alignment: A Narrative Review

by Janet Hsu, Taikhoom M. Dahodwala, Noel O. Akioyamen, Evan Mostafa, Rami Z. AbuQubo, Xiuyi Alexander Yang, Priya K. Singh, Daniel C. Berman, Rafael De la Garza Ramos, Yaroslav Gelfand, Saikiran G. Murthy, Jonathan D. Krystal, Ananth S. Eleswarapu and Mitchell S. Fourman

J. Pers. Med. 2025, 15(10), 480; https://doi.org/10.3390/jpm15100480 (registering DOI) - 3 Oct 2025

Abstract

Patient-specific technologies within the field of adult spinal deformity (ASD) aid surgeons in pre-surgical planning, accurately help identify anatomical landmarks, and can project optimal post-surgical sagittal alignment. This narrative review aims to discuss the current uses of patient-specific technologies in ASD and identify new innovations that may very soon be integrated into patient care. Pre-operatively, machine learning or artificial intelligence helps surgeons to simulate post-operative alignment and provide information for the 3D-printing of pre-contoured rods and patient-specific cages. Intraoperatively, robotic surgery and intraoperative guides allow for more accurate positioning of implants. Implant materials are being developed to allow for better osseointegration and patient outcome monitoring. Despite the significant promise of these technologies, work still needs to be performed to ensure their accuracy, safety, and cost efficacy. Full article

(This article belongs to the Special Issue Personalized Approaches in Spinal Deformity: Innovations in Diagnosis and Treatment)

15 pages, 1380 KB

Open AccessArticle

Impact of a Contextualized AI and Entrepreneurship-Based Training Program on Teacher Learning in the Ecuadorian Amazon

by Luis Quishpe-Quishpe, Irene Acosta-Vargas, Lorena Rodríguez-Rojas, Jessica Medina-Arias, Daniel Antonio Coronel-Navarro, Roldán Torres-Gutiérrez and Patricia Acosta-Vargas

Sustainability 2025, 17(19), 8850; https://doi.org/10.3390/su17198850 (registering DOI) - 3 Oct 2025

Abstract

The integration of emerging technologies is reshaping the teaching skills required in the 21st century, yet little evidence exists on how contextualized training supports rural teachers in adopting active methodologies and critically incorporating AI into entrepreneurship education. This study evaluated the impact of a 40-h professional development program implemented in Educational District 15D01 in the Ecuadorian Amazon. Thirty-nine secondary school teachers participated (mean age = 43.1 years); 36% lacked prior entrepreneurship training, and 44% had not recently mentored student projects. A sequential explanatory mixed-methods design was employed. The quantitative phase employed a 22-item questionnaire that addressed four dimensions: entrepreneurial knowledge, competencies, methodological strategies, and AI integration. Significant pre–post improvements were found (p < 0.001), with large effects for knowledge (d = 1.43), methodologies (d = 1.39), and AI integration (d = 1.30), and a moderate effect for competences (d = 0.66). The qualitative phase analyzed 312 open-ended responses, highlighting greater openness to innovation, enhanced teacher agency, and favorable perceptions of AI as a resource for ideation, prototyping, and evaluation. Overall, the findings suggest that situated, contextually aligned training can strengthen digital equity policies, foster pedagogical innovation, and empower educators in underserved rural communities, contributing to sustainable pathways for teacher professional development. Full article

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22 pages, 3621 KB

Open AccessArticle

Predictive Maintenance in Underground Mining Equipment Using Artificial Intelligence

by Nelson Chambi, Celso Sanga, Jorge Ortiz, Alejandra Sanga, Piero Sanga, Rosiand Manrique and Julio Lu-Chang-Say

Eng 2025, 6(10), 261; https://doi.org/10.3390/eng6100261 (registering DOI) - 3 Oct 2025

Abstract

Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing on early fault detection. The methodology integrates IoT sensors to monitor key parameters (temperature, pressure, oil analysis, and wear) in real time, combined with machine learning models to identify predictive patterns. The results demonstrate an 8% reduction in maintenance costs and a 10% increase in equipment availability, validating the system’s ability to anticipate failures and minimize unplanned downtime. It is concluded that this approach not only enhances productivity but also raises safety standards, offering a scalable model for critical industrial environments. The findings are supported by empirical data collected from actual operations, with no theoretical extrapolations. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)

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21 pages, 1625 KB

Open AccessArticle

Multi-Objective Feature Selection for Intrusion Detection Systems: A Comparative Analysis of Bio-Inspired Optimization Algorithms

by Anıl Sezgin, Mustafa Ulaş and Aytuğ Boyacı

Sensors 2025, 25(19), 6099; https://doi.org/10.3390/s25196099 (registering DOI) - 3 Oct 2025

Abstract

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article

(This article belongs to the Special Issue Security Challenges in Wireless Sensor Networks and the Internet of Things (IoTs))

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22 pages, 1567 KB

Open AccessArticle

Short-Term Displacement Prediction of Rainfall-Induced Landslides Through the Integration of Static and Dynamic Factors: A Case Study of China

by Chuyun Cheng, Wenyi Zhao, Lun Wu, Xiaoyin Chang, Bronte Scheuer, Jianxue Zhang, Ruhao Huang and Yuan Tian

Water 2025, 17(19), 2882; https://doi.org/10.3390/w17192882 - 2 Oct 2025

Abstract

Rainfall-induced landslide deformation is governed by both intrinsic geological conditions and external dynamic triggers. However, many existing predictive models rely primarily on rainfall inputs, which limits their interpretability and robustness. To address these shortcomings, this study introduces a group-based data augmentation method informed by displacement curve morphology and proposes a multi-slope predictive framework that integrates static geological attributes with dynamic triggering factors. Using monitoring data from 274 sites across China, the framework was implemented with a Temporal Fusion Transformer (TFT) and benchmarked against baseline models, including SVR, XGBoost, and LSTM models. The results demonstrate that group-based augmentation enhances the stability and accuracy of predictions, while the integrated dynamic–static TFT framework delivers superior accuracy and improved interpretability. Statistical significance testing further confirms consistent performance improvements across all groups. Collectively, these findings highlight the framework’s effectiveness for short-term landslide forecasting and underscore its potential to advance early warning systems. Full article

(This article belongs to the Special Issue Water-Related Landslide Hazard Process and Its Triggering Events)

22 pages, 2526 KB

Open AccessArticle

An Explainable Deep Learning Framework with Adaptive Feature Selection for Smart Lemon Disease Classification in Agriculture

by Naeem Ullah, Michelina Ruocco, Antonio Della Cioppa, Ivanoe De Falco and Giovanna Sannino

Electronics 2025, 14(19), 3928; https://doi.org/10.3390/electronics14193928 - 2 Oct 2025

Abstract

Early and accurate detection of lemon disease is necessary for effective citrus crop management. Traditional approaches often lack refined diagnosis, necessitating more powerful solutions. The article introduces adaptive PSO-LemonNetX, a novel framework integrating a novel deep learning model, adaptive Particle Swarm Optimization (PSO)-based feature selection, and explainable AI (XAI) using LIME. The approach improves the accuracy of classification while also enhancing the explainability of the model. Our end-to-end model obtained 97.01% testing and 98.55% validation accuracy. Performance was enhanced further with adaptive PSO and conventional classifiers—100% validation accuracy using Naive Bayes and 98.8% testing accuracy using Naive Bayes and an SVM. The suggested PSO-based feature selection performed better than ReliefF, Kruskal–Wallis, and Chi-squared approaches. Due to its lightweight design and good performance, this approach can be adapted for edge devices in IoT-enabled smart farms, contributing to sustainable and automated disease detection systems. These results show the potential of integrating deep learning, PSO, grid search, and XAI into smart agriculture workflows for enhancing agricultural disease detection and decision-making. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition)

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32 pages, 2827 KB

Open AccessArticle

Understanding Post-COVID-19 Household Vehicle Ownership Dynamics Through Explainable Machine Learning

by Mahbub Hassan, Saikat Sarkar Shraban, Ferdoushi Ahmed, Mohammad Bin Amin and Zoltán Nagy

Future Transp. 2025, 5(4), 136; https://doi.org/10.3390/futuretransp5040136 - 2 Oct 2025

Abstract

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

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16 pages, 1227 KB

Open AccessArticle

Multimodal Behavioral Sensors for Lie Detection: Integrating Visual, Auditory, and Generative Reasoning Cues

by Daniel Grabowski, Kamila Łuczaj and Khalid Saeed

Sensors 2025, 25(19), 6086; https://doi.org/10.3390/s25196086 - 2 Oct 2025

Abstract

Advances in multimodal artificial intelligence enable new sensor-inspired approaches to lie detection by combining behavioral perception with generative reasoning. This study presents a deception detection framework that integrates deep video and audio processing with large language models guided by chain-of-thought (CoT) prompting. We interpret neural architectures such as ViViT (for video) and HuBERT (for speech) as digital behavioral sensors that extract implicit emotional and cognitive cues, including micro-expressions, vocal stress, and timing irregularities. We further incorporate a GPT-5-based prompt-level fusion approach for video–language–emotion alignment and zero-shot inference. This method jointly processes visual frames, textual transcripts, and emotion recognition outputs, enabling the system to generate interpretable deception hypotheses without any task-specific fine-tuning. Facial expressions are treated as high-resolution affective signals captured via visual sensors, while audio encodes prosodic markers of stress. Our experimental setup is based on the DOLOS dataset, which provides high-quality multimodal recordings of deceptive and truthful behavior. We also evaluate a continual learning setup that transfers emotional understanding to deception classification. Results indicate that multimodal fusion and CoT-based reasoning increase classification accuracy and interpretability. The proposed system bridges the gap between raw behavioral data and semantic inference, laying a foundation for AI-driven lie detection with interpretable sensor analogues. Full article

(This article belongs to the Special Issue Sensor-Based Behavioral Biometrics)

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