Search Results (11,190)

This article investigates offline recognition of handwritten Kazakh text in the Latin script using a convolutional recurrent neural network. The relevance of the study is determined by the transition of the Kazakh language to the Latin alphabet and the need to automate the processing of handwritten documents. The proposed model consists of a convolutional neural network feature extractor, two bidirectional long short-term memory layers, and a Connectionist Temporal Classification decoder. The convolutional layers extract visual features from word images, the bidirectional recurrent layers model the sequential relationships between characters, and CTC enables end-to-end training without explicit character-level segmentation. A specialized dataset named KazEsim, containing 20,000 handwritten Kazakh name images, was created and divided into writer-independent training, validation, and test subsets. Experimental results showed a character accuracy rate of 96.5% and a word accuracy rate of 92.3%. Compared with a conventional CNN baseline, the proposed CRNN model improved character accuracy by 6.1 percentage points and word accuracy by 9.2 percentage points. The proposed model also outperformed the fine-tuned TrOCR-small comparative baseline while requiring fewer parameters and lower inference latency. These findings demonstrate the effectiveness of CNN–BiLSTM–CTC sequence modeling for offline recognition of handwritten Kazakh words in the Latin script. Full article

The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and confidentiality of personal data are presented that indicate difficulties in confirming authenticity. Using blockchain technology, the Ministry of Justice can exchange data and knowledge in a secure and transparent way. The goal of the proposed method is to automate the procedure for generating birth certificates to strengthen trust, security, and operational efficiency within the Moroccan judicial system. Full article

Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system. Full article

The automatic inspection of airport infrastructure, specifically horizontal surface markings and Airfield Ground Lighting (AGL), is a critical task for maintaining aviation safety and operational efficiency. As the aviation industry shifts from manual surveys to automated visual inspections utilizing unmanned aerial vehicles (UAVs) and smart service vehicles, the demand for robust, real-time computer vision systems has surged. However, deploying computationally intensive deep learning models in the field introduces severe Size, Weight, and Power (SWaP) constraints. This paper presents a comprehensive framework for the semantic segmentation of runway/taxiway markings and the point-localization of AGL lamps, specifically focusing on the deployment paradigm shift from the expensive, GPU-accelerated heterogeneous system on chip (SoC) to highly efficient, dedicated Neural Processing Units (NPUs). We evaluate the performance of U-Net, LinkNet, U-Net-Point and HRNet-Lite-Point architectures trained on a custom dataset from the Poznań-Ławica Airport. Crucially, this study conducts a rigorous comparative hardware analysis between the flagship NVIDIA Jetson AGX Orin and a highly cost-effective heterogeneous setup comprising a Raspberry Pi 5 augmented with an NPU Hailo-8 AI accelerator. Experimental results demonstrate that while both platforms achieve real-time inference, the Hailo-8 integration fundamentally disrupts the traditional cost-to-performance ratio. Furthermore, the Hailo-8 configuration consumed less electrical power and memory footprint required by the Jetson, proving that dedicated NPUs are vastly superior for continuous, battery-operated edge deployment in autonomous airport maintenance systems. Specifically, the Raspberry Pi setup with the Hailo-8 accelerator demonstrated superior energy efficiency, requiring a significantly lower energy consumption per processed video frame compared to the Jetson platform. Full article

The rapid growth of organisational textual data has increased the value of Natural Language Processing (NLP) and semantic analytics for strategic decision support. However, many employees still lack the knowledge and skills needed to evaluate AI-generated information critically. This study develops an explainable Management Information Systems (MIS) framework that integrates NLP-based semantic analytics with PLS-SEM to examine the relationship between AI illiteracy and strategic decision quality. A convergent mixed-methods design with sequential analytical integration was used with a valid sample of 200 knowledge workers from public organisations in Saudi Arabia across six industries. The sample included employees from Saudi Arabia, Egypt, Jordan, Sudan, Syria, India, and the Philippines. Quantitative data were analysed using PLS-SEM, while textual data were analysed using Sentence-BERT, BERTopic, semantic network analysis, and Aspect-Based Sentiment Analysis. The results showed that higher AI illiteracy was negatively associated with strategic decision quality and positively associated with automation bias, uncritical trust in AI, and cognitive offloading. Digital proficiency and AI governance awareness weakened the negative association between AI illiteracy and decision quality, while functional-background differences were examined through multigroup analysis. The semantic analysis identified six themes: AI competency, decision trust, AI governance, decision support, organisational learning, and risk awareness. Sentiment analysis showed positive views of productivity and decision support, together with concerns about algorithmic bias, explainability, transparency, and AI governance. The study contributes an integrated human–AI decision vulnerability framework in which semantic evidence complements structural modelling and provides a clearer understanding of AI-related competency, reliance, governance, and decision-support issues. Full article

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their evolution pathways. To address this issue, an automated surface disturbance detection method (Auto-SD) was developed for open-pit coal mines in arid and desert environments. This method integrates disturbance-type identification and temporal information extraction using the tasseled cap brightness (TCB) component to characterize changes associated with surface material exposure and accumulation. Using Landsat imagery from 1990 to 2023, Auto-SD was applied to 89 open-pit coal mines in China’s arid and desert regions, achieving an overall classification accuracy of 0.84. The cumulative disturbed area reached 423.10 km², while the internal dumping area reached 94.25 km², indicating limited backfilling recovery. Disturbance intensified after 2006, whereas backfilling lagged behind, forming a trajectory of rapid expansion, delayed recovery, and gradual stabilization. Spatially, mining areas exhibited a progressive transition from external dumping to internal dumping and backfilling. Furthermore, cumulative pit area generally followed an S-shaped growth pattern with mining duration. These findings provide new insights into long-term mining landscape evolution and support ecological restoration assessment and sustainable resource management in arid mining regions. Full article

The study addresses the design and development of a unified corporate information system for multimodal transport and logistics companies engaged in maritime and railway transportation. Analysis of the existing literature revealed the absence of a coherent methodological framework for the design of corporate information systems tailored to the specific operational characteristics of multimodal transport and logistics enterprises. To bridge this gap, a design methodology for corporate information system databases is proposed, intended for subsequent deployment in companies operating multimodal supply chains. The development of the unified corporate information system was guided by the principle of “total costs,” which requires that the decision-maker—when selecting transport modes, methods of transportation, carriers, routing, and auxiliary intermediaries (insurer, stevedore, bank, and customs broker)—address the problem as an integrated whole rather than optimizing individual components in isolation. The study encompasses information modeling of the business processes of multimodal transport and logistics companies, construction of an optimal model of the transport process for maritime and railway transportation using integrated computer automated manufacturing definition (IDEF) and structured analysis and design technique (SADT) modeling, and the design of a multilevel unified database structure for the coordination of different transport modes. A decision-making and support system has been developed for managing the operational activities of a multimodal transport and logistics company engaged in maritime and railway transportation. The proposed unified corporate information system enables the replacement of task resolution by local optimization criteria applied separately to each transport mode—such as freight cost and delivery time—with a single global optimization criterion for the multimodal supply chain. Full article

Traditional rule-based intrusion detection systems generally fail in identifying unknown or evolving threats; thus, automated and adaptive kinds of methods are crucial. Deep learning models provide promising solutions, but many recent studies depend on hybrid architecture, which increase the computational cost and reduce deploying ability on real-time or resource-limited systems. In this paper, we present and test a standalone LSTM model for multiclass cyberattack detection based on a CIC_IoT_Dataset2023, a recent labeled dataset that mirrors the actual network environment containing 33 attack categories. The dataset was extremely imbalanced as benign traffic accounted for most of the classes. To detect such attacks, we used the Synthetic Minority Oversampling Technique (SMOTE) to increase the frequency of less common types of address. The pre-processed dataset was then employed to train four models (RNN, CNN, DNN and the proposed LSTM) for performance analysis with sequential data. The proposed LSTM model achieved an accuracy between 2% and 7%. LSTM had good detection for frequent attacks and slow-changing patterns, which shows its capacity in learning long-lasting dependencies. The results demonstrate that a simple, lightweight standalone LSTM model can be used for effective and realistic intrusion detection without the need for complex hybrid architecture. Full article

Manual fruit thinning is labor-intensive and inefficient, making the development of intelligent visual detection systems a crucial approach for improving the quality and production efficiency of the pear industry. However, in natural orchard environments, young pear fruits are small in size with slender fruit stalks, and their texture and color characteristics are highly similar to those of tender branches. Furthermore, variations in illumination and occlusions caused by branches and leaves make it difficult for existing detection models to simultaneously and accurately identify fruits and fruit stalks, limiting their application in automated thinning equipment. In this study, Yuluxiang pear was selected as the research object, and image data were collected under diverse field conditions, including forward-lighting, backlighting, close-range shooting, long-range shooting and fruit overlapping. A dedicated dataset containing 3057 images was established. Based on the RT-DETR-R18 network, a lightweight and high-precision fruit–stalk synchronous detection model was proposed. Specifically, the backbone network was reconstructed by integrating GCConv with C2f modules to enhance global feature extraction for slender fruit stalks. The bottleneck structure was optimized using GCConvC3 to reduce feature degradation under occlusion conditions, and an additional 4× down-sampling P2 detection head was introduced to improve the detection capability for small targets. To fully validate the model performance and stability, three types of experiments were conducted in this study: ablation experiments, repeated experiments with different random seeds, and comparative experiments. Ablation experiments verified the cumulative performance improvements brought by the introduced modules. Repeated experiments with different random seeds were performed to explore training randomness-induced performance fluctuations, and the results demonstrated that the proposed model maintains stable overall detection accuracy with minor metric fluctuations. Comparative experiments demonstrated that the proposed model achieved a compact parameter size of only 15.97 M, with a precision of 95.0% for young pear fruit detection and an mAP50 of 83.0% for fruit stalk detection, outperforming all comparative models in overall mAP50. The training convergence curves and Grad-CAM++ visualization results further confirmed the stable optimization process and enhanced feature attention capability of the proposed model. By achieving a favorable balance between detection accuracy and model lightweightness, this approach provides effective technical support for the development of intelligent fruit thinning equipment and vision-based systems for smart pear orchards. Full article

The growing environmental impact of the construction sector is driving a shift toward sustainable design practices, in which digital technologies are integrated to enable designers to make informed decisions from the early design stages. In this study, a DSS is developed that combines BIM, VPL and TPL to identify the optimal wall stratigraphy for the building envelope. The process is structured into sequential phases, in which Autodesk Revit v2026.06.24.01, Dynamo v.3.6.1 and Python v3.9 are integrated within an end-to-end workflow. In the first phase, wall stratigraphies are modelled in BIM, and parametric variations in layers are allowed alongside customisation of the material database. In the second phase, an automated workflow calculates a set of indicators covering thermal performance, environmental assessments (LCA, MRc2 LEED and mandatory national requirements), and economic evaluations (LCC). In the third phase, indicators are imported into an automated Dynamo-based MCDM, where a hybrid AHP/PROMETHEE analysis is applied and results are directly integrated into BIM, thereby supporting sustainability-focused decisions. The tool is validated on different sustainable wall stratigraphies in warm-climate contexts. The hybrid solution is ranked first, followed by rammed earth, while platform frame and X-LAM are ranked lower. Full article

Automated pulmonary auscultation demands rapid and reliable anomaly detection. Contemporary deep learning frameworks frequently face deployment barriers due to their reliance on memory-intensive Transformer mechanisms and high-cost processing hardware. Addressing this limitation, the present study introduces the Neuro Scatter Mamba Kolmogorov–Arnold Neural Network (NSMK-Net), a lightweight, Transformer-free architecture. The model integrates 1D Wavelet Scattering, bi-directional Selective State Space Models (Mamba), and Kolmogorov–Arnold Networks (KAN). By substituting quadratic self-attention with continuous-time differential discretization, the framework achieves very good computational efficiency under severe hardware constraints. Model optimization followed an eco-friendly “Green-AI” methodology, successfully converging on a standard 4 GB VRAM graphics unit. Regarding real-world deployment, the finalized architecture can be considered as a possible candidate for future “Edge-AI” applications, because it requires only 0.34 MB of parameter storage (89,342 parameters) and executes inference in approximately 48 milliseconds per respiratory cycle. Evaluated on the SPRSound dataset, the proposed model achieved a cycle-level accuracy of 84.67% (Macro-F1: 0.48). When tested under the strict official 60/40 partition of the ICBHI 2017 dataset, the network delivered a global accuracy of 41.56% (Macro-F1: 0.31) alongside an official reported ICBHI Score of 49.38%. These metrics indicate a stable detection capability when processing highly imbalanced clinical data. By replacing fixed activation nodes with learnable edge non-linearities and utilizing linear sequence memory, this new structural approach reduces the dependency on high-end hardware for medical acoustic processing. Full article

Background/Objectives: Computed tomography perfusion is widely used in acute ischemic stroke, but its performance is less reliable for lacunar and infratentorial infarcts. This technical review examines the acquisition and processing factors that influence their detectability. Methods: A targeted technical review of PubMed/MEDLINE, Scopus, and Web of Science was performed, focusing on acquisition, reconstruction, vascular input selection, deconvolution, filtering, spatial sampling, automated classification, posterior circulation stroke, and lacunar infarction. Results: Detectability depends on lesion size and contrast, posterior fossa artifacts, spatial and temporal resolution, vascular curve quality, mathematical stabilization, and automated thresholds. Larger cerebellar infarcts may remain visible, whereas brainstem, deep cerebellar, and perforator lesions are more vulnerable. Temporal maps and direct review of parametric images may reveal abnormalities absent from automated summaries. Conclusions: This technical review shows that computed tomography perfusion should be interpreted as a derived estimate rather than a direct representation of cerebral hemodynamics. Negative automated findings do not exclude lacunar or infratentorial infarction when clinical suspicion remains high. Full article

Quality control in green coffee bean production is critical for food safety and economic sustainability. A persistent gap in existing automated inspection systems is their inability to capture both sides of each bean, which leads to systematic under-detection of surface defects. This work presents three contributions: (i) a novel mechatronic apparatus that mechanically guarantees dual-sided imaging of every bean, (ii) a public 12-class dataset of green coffee bean defects, and (iii) an embedded, real-time inspection pipeline validated on low-cost hardware. The apparatus sequentially presents each bean, from a standard 350 g sample, to two 16-megapixel cameras under controlled LED illumination. A dataset of 9600 images spanning 12 classes (11 defects and 1 normal) was generated from expert-classified samples and enriched through data augmentation. Four convolutional neural network (CNN) architectures, VGG-16, VGG-19, ResNet-50 and YOLOv8, were trained and benchmarked using precision, recall, F1-score and mean average precision. YOLOv8 achieved the best overall performance, with a precision of 97.4%, a recall of 99.6%, an F1-score of 0.930 and a mean average precision of 96.5%, outperforming VGG-16 (accuracy 86.07%), VGG-19 (accuracy 67.03%) and ResNet-50 (accuracy 87.76%). Dual-sided acquisition raised mean per-class detection accuracy from 0.727 to 0.908, a relative gain of 25.7% over an equivalent single-sided configuration. Deployed in real-time “track” mode on a Raspberry Pi 4, the system simultaneously classifies defects and counts beans by category, processing a 350 g sample in approximately 38 min. Combining mechanical innovation with lightweight deep learning enables practical, scalable, and cost-effective quality control for laboratories specialized in coffee analysis. Full article

This paper examines the relationship between digital transformation capabilities and perceived organizational performance in an entrepreneurial fintech SME and considers, at an interpretive level, how these capabilities may support more accessible digital financial services. The study focuses on PiggyVest, a Nigerian digital savings and investment platform. Methodologically, it adopts an embedded single-case research design that combines quantitative survey evidence with qualitative interpretation and secondary-source analysis of the firm’s inclusion- and governance-related practices. The quantitative component examines digital integration, process automation, change management, and training and capability development in relation to perceived organizational performance. The case-based analysis then considers how PiggyVest’s digitally enabled service model may reduce barriers to savings and investment services and support financial inclusion under appropriate governance conditions. Descriptive and correlational results indicate positive associations between the examined dimensions of digital transformation and perceived organizational performance. The broader implications for financial inclusion and social sustainability are not directly measured and are therefore presented as case-based interpretations rather than demonstrated societal effects. The paper contributes to research on digital transformation, SME innovation, entrepreneurship, and sustainability by clearly distinguishing between firm-level empirical evidence and broader interpretive implications. Full article

Sun-induced chlorophyll fluorescence (SIF) is an effective proxy for vegetation photosynthesis, but tower-based retrieval suffers from atmospheric path interference under humid and variable conditions. We present a DOAS-based SIF retrieval algorithm that operates in Fraunhofer lines (680–686 nm, 745–758 nm) and water vapour-sensitive bands (717–727 nm). It constructs an adaptive reference spectrum from SCOPE simulations and PCA and incorporates H₂O absorption cross-sections into the fitting process for active atmospheric correction. The algorithm is implemented in a dedicated tower-based system integrating a 1° scanning gimbal with a high-resolution spectrometer. Validation with simulated and field data demonstrates the following: (1) the algorithm retrieves SIF with high fidelity (correlation coefficients >0.9 across all windows); (2) it exhibits lower water-vapour sensitivity and greater cloudy-sky stability than FLD, 3FLD, and SFM, achieving the lowest coefficient of variation (CV = 0.356); (3) over a complete wheat–rice rotation, the retrieved SIF tracks crop growth and phenological stages. This work provides a reliable solution for automated, high-precision tower-based SIF observation under complex atmospheric conditions. Full article