Search Results (5,028)

Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

Electromyography (EMG)-based sensing is an important tool for assessing neuromuscular activity and monitoring athlete development; its reliability depends on electrode placement, signal quality, and accurate identification of muscle activation periods. This study proposes an intelligent EMG sensing framework integrating preliminary electrode placement assessment, muscle activity detection, feature extraction, and regression-based progression prediction. A placement assessment indicated that positioning the electrode adjacent to the innervation zone produced the highest RMS under the tested conditions. A two-stage activity detection method based on clustering and probabilistic modeling achieved an average error of 1.5% and a temporal deviation of 19 ms. Nine time-domain EMG features extracted from the detected activity segments were used to characterize athlete progression and estimate the time required to reach a reference neuromuscular profile. Among the methods, Linear Regression provided the best fit to the data, obtaining R² = 0.987 and RMSE = 4.21 and suggesting a predominantly linear relationship between the EMG-derived features and training duration within the dataset. However, these results were obtained from only six longitudinal observation periods for a single representative athlete, with each period represented by a 90-dimensional EMG feature vector derived from the ten movement classes. Therefore, the results should be interpreted as preliminary, athlete-specific goodness-of-fit findings rather than evidence of generalizable predictive performance. Validation using larger longitudinal cohorts and independent datasets is required. The proposed framework is compatible with future IoT-enabled wearable and edge-computing architectures; however, hardware-level implementation was beyond the scope of this study. Full article

Intelligent methods such as machine learning, deep learning, and related reasoning-based techniques are widely credited with transforming digital forensics, yet it remains unclear where that transformation has actually taken hold. This study presents a systematic mapping review, conducted in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR), that treats the adoption of intelligent methods not as an assumption but as a variable to be measured. Following searches across eight bibliographic databases, 82 primary studies were mapped onto six digital forensics domains and coded by intelligence method type, enabling a domain-by-method analysis of the field. The results reveal markedly uneven penetration. Intelligent methods dominate multimedia forensics (93% of included studies) and mobile and IoT forensics (77%), yet remain the exception in frameworks and governance (33%), imaging and acquisition (28%), memory forensics (25%), and data reduction (21%), where classical, deterministic techniques still prevail. Resolved onto a five-level maturity ladder, the domains differ not only in how much intelligence they have adopted but in its kind: some reach deep learning and explainable reasoning while others advance only to automation, and several skip intermediate stages entirely. These findings recast intelligence in digital forensics as a set of unevenly developed capabilities rather than a uniform pipeline, and identify where learning-based research has the furthest still to travel. Full article

In-storage food loss is a persistent yet under-addressed source of economic, environmental, and social waste in small and medium-sized enterprise (SME) cold chains, where continuous monitoring and lean workflows are typically absent. This study asks how such loss can be reduced under SME constraints and what benefits an intervention designed for that setting could yield. Following a design science approach in a Thai chilled warehouse case, it develops an integrated intervention coupling an Internet of Things (IoT) early-warning platform—ESP-32 and DHT22 sensing with commodity-specific alerting through the LINE Messaging API—with value stream mapping (VSM) of the depositing and withdrawing workflows. Monitoring showed that 7.4% of quality-controlled readings exceeded the 6 °C control threshold. Value stream analysis established that elapsed time is governed by information latency rather than physical work and that produce spends 290 min per handling cycle outside controlled conditions. The redesigned workflows project lead-time reductions of 47.5% and 69.4% and remove 125 min of that exposure. An ex ante Triple Bottom Line assessment estimates approximately 19,700 kg of avoided produce loss, 6500 kg CO₂e, and 590,000 THB retained annually. This study contributes a complementarity account of digital monitoring and process improvement, advancing SDG Target 12.3. Full article

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes. Full article

Maritime routes ensure connectivity between nations, carrying a vast flow of goods across borders, while ports serve as the critical junctions within this network, managing a wide spectrum of commodities from raw materials to finished goods. Ports also generate employment across numerous sectors and underpin a broad range of allied industries. A seaport is a maritime facility equipped with docks, cranes, and storage infrastructure for international trade, where ships load and unload cargo, containers, and passengers. Key functions of seaports include customs processing, warehousing, and vessel services, with major global hubs such as Shanghai, PSA Singapore, DP World, and Rotterdam handling immense volumes of cargo each year. In contrast, Bangladesh’s ports, Chittagong, Mongla, and Payra, play a vital role in sustaining regional commerce. Today, ports are widely recognized as essential capital infrastructure and prime movers of economic activity. Smart ports are automated facilities that leverage advanced digital technologies, including sensors, big data analytics, artificial intelligence (AI), machine learning (ML), deep learning (DL), augmented reality (AR), digital twins, the Internet of Things (IoT), and various automation systems, to optimize overall operational efficiency. These tools streamline cargo movement while embedding sustainable practices to protect the environment. Beyond operational gains, smart ports deliver faster, more advanced services to all stakeholders involved in port operations, including shipping companies, customs agencies, local communities, and other relevant parties. Renewable energy sources, electric vehicle charging stations, onshore power supply, and smart logistics infrastructure are among the defining sustainability features of smart ports in the present-day context. This study examines the current status and future development trajectory of Bangladesh’s sea ports in relation to the broader global imperative toward smart port transformation. Full article

This study proposes an integrated approach to the digital documentation and system-level analysis of heritage buildings, combining LiDAR-based data acquisition, H-BIM modelling and DEMATEL analysis. The novelty of the study lies in combining a Scan-to-BIM workflow with DEMATEL-based system analysis in order to identify causal and dependent stages in the heritage building digitisation process. The research was carried out on two heritage buildings in south-eastern Poland: the Church of St Onuphrius in Posada Rybotycka and a wooden manor house from Brzeziny preserved in the ethnographic park in Kolbuszowa. Terrestrial laser scanning was used to acquire high-resolution point clouds of the buildings, which then provided the basis for developing parametric H-BIM models within a Scan-to-BIM workflow. For the church case study, the geometric accuracy of the Scan-to-BIM output was verified by comparing measurements derived from the point cloud with traditional surveying data. The results confirmed the suitability of Scan-to-BIM for heritage documentation, with an average absolute deviation of approximately 7 mm and a maximum deviation not exceeding 31 mm. DEMATEL analysis was used to examine cause–effect relationships between seven stages of the digitisation process and to determine which stages have the greatest influence on subsequent activities. Preliminary assessment, LiDAR scanning and H-BIM modelling were identified as causal stages, while validation, IoT integration, AI-based predictive analysis and digital twin development were classified as effect stages. The study also outlines how H-BIM models may be extended through IoT sensors and AI-based analytics as a basis for future digital twin development. Full article

The Internet of Things (IoT) market is currently undergoing a period of unprecedented, rapid evolution, leading to the enabling of novel and diverse applications spanning both the civilian and industrial sectors. A significant proportion of these emerging use cases, particularly those in domains such as maritime communications and forestry management, require service continuity and connectivity within geographically remote regions, where conventional terrestrial infrastructure is often absent or economically unfeasible. To bridge this coverage gap and achieve truly ubiquitous connectivity, the recent 3GPP initiative to extend 5G services into Non-Terrestrial Segments (NTNs) holds substantial promise. This expansion is crucial for ensuring that massive Machine-Type Communication (mMTC) services can be reliably provisioned globally. This paper aims to detail the progress in standardization and academic activities towards the design and deployment of NTN-based Narrowband IoT (NB-IoT) systems, which are the leading NTN mMTC enabler in the 3GPP portfolio. We will specify the challenges faced by these systems and outline the solutions proposed thus far. We conclude the paper with a discussion on already operational systems and lessons learned from their deployment and operation. Full article

As educators are increasingly expected to integrate technology into meaningful learning experiences, higher education institutions face growing demands to prepare digitally competent STEM teachers. This study examines an interdisciplinary IoT-enhanced project-based training involving pre-service teachers from the Computer Education and Instructional Technology (CEIT) and Science Education programmes, focusing on pre–post changes in computational thinking, collaborative processes, project development experiences, and participants’ perceptions of how the training contributed to their professional development. The study involved 36 pre-service teachers from CEIT and Science Education programmes and employed an embedded mixed-methods design combining computational thinking assessments with qualitative analyses of project and collaboration processes. Results showed statistically significant pre–post increases in decomposition in both groups and in algorithmic thinking among Science Education pre-service teachers. The overall Computational Thinking Skills Test (CTS Test) score did not change significantly in the CEIT group, whereas the corresponding change in the Science Education group was interpreted cautiously because it was at the conventional significance threshold. No statistically significant changes were found in pattern recognition or abstraction in either group, and the subdimension findings were treated as exploratory given the small sample and the absence of correction for multiple testing. Furthermore, active participation in collaborative meetings was associated with stronger teamwork practices, while limited engagement was associated with reported implementation challenges. Because attendance was self-selected, these associations cannot be interpreted causally. Participants perceived their IoT learning experience as making a positive contribution to their professional development and future teaching practice. Taken together, the findings suggest that interdisciplinary IoT-supported project-based learning (PBL) may provide a promising framework for supporting selected dimensions of computational thinking and collaboration in pre-service teacher education, while also offering an accessible instructional model that supports participation among learners with different levels of prior technical expertise. Full article

Plant disease forecasting plays an important role in modern crop protection by enabling early disease prediction and supporting optimized management decisions. With the rapid development of digital agriculture, artificial intelligence, and environmental monitoring technologies, forecasting systems have evolved from traditional empirical and mechanistic models to machine learning, deep learning, multi-source data fusion, and hybrid forecasting frameworks. Unlike previous reviews that mainly focused on specific model types, decision support systems, or disease recognition technologies, this review provides a comprehensive synthesis of different forecasting approaches and their practical applications. The strengths and limitations of various models are comparatively analyzed in terms of predictive performance, interpretability, fungicide reduction potential, and practical applicability. In addition, recent advances in climate-driven disease forecasting, precision disease management, and intelligent decision support systems are discussed. Finally, current challenges and future directions, including AI-mechanistic model integration, multi-disease forecasting, IoT and remote sensing data fusion, and climate-adaptive forecasting systems, are highlighted to support the development of sustainable and intelligent crop protection strategies. Full article

Hydrogen leakage detection and mapping are essential in hydrogen-rich environments to ensure safe utilization in industrial and commercial applications. In this study, a wireless IoT-enabled hydrogen leak-mapping system was developed using machine learning and a LoRa-coupled wireless sensor network. A miniature model of a hydrogen production system was used, featuring a functioning electrolyzer that generates pure hydrogen by splitting water. To perform efficient leakage mapping, the leak location and watch time were varied, and six readings from commercial hydrogen gas sensors were recorded for better analysis. The relative sensor responses recorded by the six hydrogen sensors were used as input features for the machine learning models. The model accuracy was approximately 88.13%. LoRa communication technology was also used to demonstrate its use in harsh conditions, along with the IoT protocol, to deliver data over the Internet for better accessibility and monitoring. The developed localization technology enables safe monitoring of hazardous, highly flammable hydrogen gas, and machine learning can help prevent fatal accidents. Full article

Currently, controlled-environment agriculture (CEA) and hydroponic production systems represent sustainable alternatives for improving leafy vegetable production by controlling the variables that influence their growth. This research studied the effect of variations in photoperiod and nutrient solution concentration on the development of a hydroponic crop. The response variables analyzed were growth, physiological response, and nutrient content of chard. The hydroponic cultivation technique used was the Nutrient Film Technique (NFT) under controlled environmental conditions. The experimental design was developed with two photo-period treatments: 12 h light/12 h darkness and 10 h light/14 h darkness, combined with three levels of nutrient concentration at 100%, 75% and 50% of the standard Steiner formulation. The data were analyzed using two complementary approaches: (1) ANOVA with Tukey’s post-hoc test to identify specific group differences, and (2) principal component analysis (PCA). It was observed that the concentration of the nutrient solution had a significant effect on the growth and nutritional quality variables of the crop. Treatments with a 100% nutrient solution resulted in higher values for plant height, biomass, protein, potassium, and calcium. Reducing the nutrient solution concentration resulted in a clear decrease in crop growth variables. The effect of photoperiod on crop development was found to be less significant. The PCA accounted for 96.8% of the total variability. This indicates that nutrient concentration was the primary factor associated with growth and nutritional content in chard. The results show that nutrient reduction directly affects crop productivity and nutritional quality. However, moderate variations in photoperiod had a secondary effect under the conditions evaluated. This research provides relevant information for developing nutritional management strategies and sustainable production in controlled-environment hydroponic systems. It also contributes new knowledge on integrating hydroponic crops in controlled environments into sustainable agri-food systems using Internet of Things (IoT) technologies for efficient crop management. Full article

Large language model–based agentic AI systems can independently interpret user goals, develop plans, and interact with external tools. These capabilities introduce security concerns that extend beyond traditional access control. However, existing Zero Trust Maturity Models, such as the CISA ZTMM, mainly focus on how resources are accessed and provide limited guidance on how to evaluate actions taken after access has been granted. This paper proposes AI-ZTMM, which extends CISA’s five-pillar structure to action-level trust evaluation. The model defines forty-one security Functions based on ten threat categories and thirty-one security requirements and introduces Action Space and seven Action Risk Factors for organizational self-assessment. Its scope includes software agents and the software action layer of agents in IoT, robotic, and OT/ICS environments. The model was refined through reviews by eleven domain experts and evaluated using thirty-eight MITRE ATLAS case studies. The CISA ZTMM lacked directly relevant controls for 59.7% of the analyzed attack stages, whereas AI-ZTMM addressed 75.4% of this gap, achieving a combined direct coverage of 84.4%. These results show that AI-ZTMM complements the CISA ZTMM by providing action-level security controls. Full article

The development of indoor photovoltaic devices for powering Internet of Things (IoT) sensors requires low-cost and sustainable components, making dye-sensitized solar cells (DSSCs) an ideal candidate for artificial light harvesting. The counter electrode plays a critical role in transferring electrons and catalyzing the reduction in the redox electrolyte. However, the traditional use of expensive and scarce platinum (Pt) limits the cost-effective, large-scale commercialization of these devices. While carbon-based materials offer a highly porous, conductive, and abundant alternative, commercial carbon pastes frequently require energy-intensive high-temperature sintering. In this study, we propose a sustainable, low-temperature, and screen-printable carbon composite counter electrode (LoT-HPC) using bio-derived hydroxypropyl cellulose (HPC) as a highly effective binder. Rheological characterizations confirm that the formulated LoT-HPC ink possesses an ideal shear-thinning profile and rapid structural recovery, ensuring excellent printability and film homogeneity. By comparing the custom LoT-HPC composite against a commercial high-temperature screen-printed graphite paste (HT-Elco) and a standard sputtered Pt-FTO electrode, we demonstrate the structural and electrocatalytic advantages of this material. When integrated into full DSSC devices and evaluated under low indoor illumination (1000 lux), the LoT-HPC cell delivers a power conversion efficiency (PCE) of 14.8% and a high short-circuit current density of 103.9 µA cm⁻². Furthermore, the custom device demonstrated exceptional operational stability, retaining 98.6% of its initial efficiency (from 14.8% to 14.6%) after 200 h of continuous light-soaking and J-V cycling under 1000 lux. Ultimately, the successful implementation of the HPC binder enables the low-temperature fabrication of sustainable carbon counter electrodes without the need for energy-intensive thermal treatments, presenting a highly scalable pathway for indoor DSSC manufacturing. Full article