Search Results (102)

Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

Privacy-preserving record linkage (PPRL) aims to link records from different data sources while ensuring sensitive information is not disclosed. Utilizing blockchain as a trusted third party is an effective strategy for enhancing transparency and auditability in PPRL. However, to ensure data privacy during computation, such approaches often require computationally intensive cryptographic techniques. This can introduce significant computational overhead, limiting the method’s efficiency and scalability. To address this performance bottleneck, we combine blockchain with the distributed computation of secret sharing to propose a PPRL method based on blockchain-coordinated distributed computation. At its core, the approach utilizes Bloom filters to encode data and employs Boolean and arithmetic secret sharing to decompose the data into secret shares, which are uploaded to the InterPlanetary File System (IPFS). Combined with masking and random permutation mechanisms, it enhances privacy protection. Computing nodes perform similarity calculations locally, interacting with IPFS only a limited number of times, effectively reducing communication overhead. Furthermore, blockchain manages the entire computation process through smart contracts, ensuring transparency and correctness of the computation, achieving efficient and secure record linkage. Experimental results demonstrate that this method effectively safeguards data privacy while exhibiting high linkage quality and scalability. Full article

Artificial-intelligence-based robotics has recently garnered considerable attention, with the prediction of sample attributes becoming essential for artificial-intelligence-based environmental data analysis and decision-making processes in smart equipment and IoT devices. Based on a masked autoencoder (MAE), this study introduces the FedMAE regressor, a federated learning regression framework designed to precisely predict critical nutrients such as nitrogen, phosphorus, and potassium in agricultural and environmental monitoring devices while ensuring data privacy. The proposed adaptive regressor integrates deep learning methodologies within a federated learning architecture. Layer normalization is employed to enhance the model’s stability in distributed environments, and its structure is optimized with residual connections and GELU activation functions. An adaptive normalization method, a multi-layer feature transformation system, and a balanced data allocation technique are introduced to mitigate data distribution biases in edge devices. Furthermore, the AdaBelief optimizer and a dynamic learning rate scheduling approach are implemented to improve the model’s resilience. Experimental results show that the proposed method outperforms baseline and state-of-the-art models in terms of nitrogen prediction and demonstrates notable adaptability in phosphorus and potassium prediction tasks. This research paves the way for the application of federated-learning-based approaches in various ecological and industrial contexts, providing a robust solution for time-series prediction challenges in diverse domains. Full article

The rapid rise of flexible AMOLED displays has prompted manufacturers to advance technologies to meet growing global demand. However, high costs and quality inconsistencies hinder industry competitiveness and sustainability. This study addresses these challenges by developing an intelligent optimization system for the fine metal mask (FMM) etching process, a critical step in producing high-resolution AMOLED panels. The system integrates advanced optimization techniques, including the Taguchi method, analysis of variance (ANOVA), back-propagation neural network (BPNN), and a hybrid particle swarm optimization–genetic algorithm (PSO-GA) approach to identify optimal process parameters. Experimental results demonstrate a marked improvement in product yield and process stability while reducing manufacturing costs. By ensuring consistent quality and efficiency, this system overcomes limitations of traditional process control; strengthens the AMOLED industry’s global competitiveness; and provides a scalable, sustainable solution for smart manufacturing in next-generation display technologies. Full article

Agricultural Internet of Things (IoT) deployments face unique cybersecurity challenges due to resource constraints and direct impact on food production. This paper introduces a vulnerable-by-design, containerized IoT framework simulating both cybersecurity vulnerabilities and sensor health anomalies in agricultural settings. We demonstrate its agricultural relevance through a tomato greenhouse case study where combined DDoS attacks and sensor faults masked critical temperature increases to 43 °C, potentially reducing yields by up to 30%. Our masking analysis revealed counter-intuitive relationships between sensor faults and attack detectability: spike faults enhanced BOLA attack detectability by up to 95.9%, while dropout faults masked command injection attacks by 18.0%. We identified distinctive temporal signatures for each attack type and quantified these relationships through a composite detectability score. Our LSTM-based validation achieved moderate recall (0.5473 average) with significant variation across fault conditions (0.3194–0.8145), while maintaining strong precision (0.8285). The LSTM model performed best with drift fault conditions (0.9749 accuracy), while DDoS attacks were most consistently detectable (avg. score: 0.6886) and resource exhaustion attacks the most difficult (0.3056). These findings challenge conventional approaches that treat sensor health and security as separate domains. Our open-source implementation with systematic dataset generation capabilities addresses reproducibility challenges in agricultural IoT security while demonstrating that integrated health-security monitoring could significantly improve threat detection in smart agriculture deployments. Full article

The broadening adoption of interconnected systems within smart city environments is fundamental for the progression of digitally driven economies, enabling the refinement of city administration, the enhancement of public service delivery, and the fostering of ecologically sustainable progress, thereby aligning with global sustainability benchmarks. However, the pervasive distribution of Internet of things (IoT) apparatuses introduces substantial security risks, attributable to the confidential nature of processed data and the heightened susceptibility to cybernetic intrusions targeting essential infrastructure. Commonly, these devices exhibit deficiencies stemming from restricted computational capabilities and the absence of uniform security standards. The resolution of these security challenges is paramount for the full realization of the advantages afforded by IoT without compromising system integrity. Cryptographic protocols represent the most viable solutions for the mitigation of these security vulnerabilities. However, the limitations inherent in IoT edge nodes complicate the deployment of robust cryptographic algorithms, which are fundamentally reliant on finite-field multiplication operations. Consequently, the streamlined execution of this operation is pivotal, as it will facilitate the effective deployment of encryption algorithms on these resource-limited devices. Therefore, the presented research concentrates on the formulation of a spatially and energetically efficient hardware implementation for the finite-field multiplication operation. The proposed arithmetic unit demonstrates significant improvements in hardware efficiency and energy consumption compared to state-of-the-art designs, while its systolic architecture provides inherent timing-attack resistance through deterministic operation. The regular structure not only enables these performance advantages but also facilitates future integration of error-detection and masking techniques for comprehensive side-channel protection. This combination of efficiency and security makes the multiplier particularly suitable for integration within encryption processors in resource-constrained IoT edge nodes, where it can enable secure data communication in smart city applications without compromising operational effectiveness or urban development goals. Full article

Recognizing plant leaves in complex agricultural scenes is challenging due to high manual annotation costs and real-time detection demands. Current deep learning methods, such as YOLOv8 and SAM, face trade-offs between annotation efficiency and inference speed. This paper proposes an automated framework integrating SAM for offline semantic segmentation and YOLOv8 for real-time detection. SAM generates pixel-level leaf masks, which are converted to YOLOv8-compatible bounding boxes, eliminating manual labeling. Experiments on three plant species show the framework achieves 87% detection accuracy and 0.03 s per image inference time, reducing annotation labor by 100% compared to traditional methods. The proposed pipeline balances high-quality annotation and lightweight detection, enabling scalable smart agriculture applications. Full article

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for monitoring apple tree growth and fruit production processes in the past seven years. Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). These models have been successfully applied to detect pests and diseases (located on leaves, fruits, and trunks), organ growth (including fruits, apple blossoms, and branches), yield, and post-harvest fruit defects. This study introduced deep learning and computer vision methods, outlined in the current research on these methods for apple tree growth and fruit production. The advantages and disadvantages of deep learning were discussed, and the difficulties faced and future trends were summarized. It is believed that this research is important for the construction of smart apple orchards. Full article

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification of plant disease in a real-time environment is a significant challenge for agriculture production. For accurate plant disease detection, precise detection of plant leaves is a meaningful and challenging task for developing smart agricultural systems. Most researchers train and test models on synthetic images. So, when using that model in a real-time scenario, it does not give a satisfactory result because when a model trained on images of leaves is fed with the image of the plant, then its accuracy is affected. In this research work, we have integrated two models, the Segment Anything Model (SAM) with YOLOv8, to detect the tomato leaf of a tomato plant, mask the leaf, and extract the leaf in a real-time environment. To improve the performance of leaf disease detection in plant leaves in a real-time environment, we need to detect leaves accurately. We developed a system that will detect the leaf, mask the leaf, extract the leaf, and then detect the disease in that specific leaf. For leaf detection, the modified YOLOv8 is used, and for masking and extraction of the leaf images from the tomato plant, the Segment Anything Model (SAM) is used. Then, for that specific leaf, an image is provided to the deep neural network to detect the disease. Full article

Parking management systems play a crucial role in addressing parking shortages and operational challenges; however, high initial costs and infrastructure requirements often hinder their implementation. Edge computing offers a promising solution by reducing latency and network traffic, thus optimizing operational costs. Nonetheless, the limited computational resources of edge devices remain a significant challenge. This study developed a real-time vehicle occupancy detection system utilizing SSD-MobileNetv2 on edge devices to process video streams from multiple IP cameras. The system incorporates a dual-trigger mechanism, combining periodic triggers and parking space mask triggers, to optimize computational efficiency and resource usage while maintaining high accuracy and reliability. Experimental results demonstrated that the parking space mask trigger significantly reduced unnecessary AI model executions compared to periodic triggers, while the dual-trigger mechanism ensured consistent updates even under unstable network conditions. The SSD-MobileNetv2 model achieved a frame processing time of 0.32 s and maintained robust detection performance with an F1-score of 0.9848 during a four-month field validation. These findings validate the suitability of the system for real-time parking management in resource-constrained environments. Thus, the proposed smart parking system offers an economical, viable, and practical solution that can significantly contribute to developing smart cities. Full article

Electricity theft, emerging as one of the severe cyberattacks in smart grids, causes significant economic losses. Due to the powerful expressive ability of deep neural networks (DNN), supervised and unsupervised DNN-based electricity theft detection (ETD) schemes have experienced widespread deployment. However, existing works have the following weak points: Supervised DNN-based schemes require abundant labeled anomalous samples for training, and even worse, cannot detect unseen theft patterns. To avoid the extensively labor-consuming activity of labeling anomalous samples, unsupervised DNNs-based schemes aim to learn the normality of time-series and infer an anomaly score for each data instance, but they fail to capture periodic features effectively. To address these challenges, this paper proposes a novel periodicity-enhanced deep one-class classification framework (PE-DOCC) based on a periodicity-enhanced transformer encoder, named Periodicformer encoder. Specifically, within the encoder, a novel criss-cross periodic attention is proposed to capture both horizontal and vertical periodic features. The Periodicformer encoder is pre-trained by reconstructing partially masked input sequences, and the learned latent representations are then fed into a one-class classification for anomaly detection. Extensive experiments on real-world datasets demonstrate that our proposed PE-DOCC framework outperforms state-of-the-art unsupervised ETD methods. Full article

This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. Experimental results demonstrate the framework’s adaptability and high performance in the financial sector (accuracy: 94%, recall: 95%), healthcare (accuracy: 96%, precision: 93%), and smart city infrastructures (accuracy: 91%, F1 score: 90%). The framework achieves a balanced trade-off between accuracy (0.96) and computational efficiency (processing time: 1.5 s per transaction), making it ideal for real-time enterprise deployments. Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. Future work aims to enhance real-time scalability further and explore reinforcement learning to advance proactive threat mitigation measures. This research provides a scalable, adaptive, and practical solution for enterprise-level cybersecurity and data privacy management. Full article

Sleep apnea-hypopnea syndrome (SAHS) is a respiratory disorder characterized by cessation of breathing during sleep, resulting in daytime somnolence and various comorbidities. SAHS encompasses obstructive sleep apnea (OSA), caused by upper airway obstruction, and central sleep apnea (CSA), resulting from lack of brainstem signaling for respiration. Continuous positive airway pressure (CPAP) therapy is the gold standard treatment for SAHS, reducing apnea and hypopnea episodes by providing continuous airflow. CPAP enhances sleep quality and improves overall health by reducing the risk of comorbidities such as hypertension, type 2 diabetes mellitus, cardiovascular disease and stroke. CPAP nonadherence leads to health deterioration and occurs due to mask discomfort, unsupportive partners, upper respiratory dryness, and claustrophobia. Technological advancements such as auto-titrating positive airway pressure (APAP) systems, smart fit mask interface systems, and telemonitoring devices offer patients greater comfort and enhance adherence. Future research should focus on new technological developments, such as artificial intelligence, which may detect treatment failure and alert providers to intervene accordingly. Full article

Face masks are essential pieces of personal protective equipment for preventing inhalation of airborne pathogens and aerosols. Various face masks are used to prevent the spread of virus contamination, including blue surgical and N95 filtering masks intended for single use. Traditional face masks with self-sanitisation features have an average filtration efficiency of 50% against airborne viruses. Incorporating nanomaterials in face masks can enhance their filtration efficiency; however, using nanomaterials combined with thermal heaters can offer up to 99% efficiency. Bacterial contamination is reduced through a self-sterilisation method that employs nanomaterials with antimicrobial properties and thermoregulation as a sanitisation process. By combining functional nanomaterials with conductive and functional polymeric materials, smart textiles can sense and act on airborne viruses. This research evaluates the evidence behind the effectiveness of nanomaterials and thermoregulation-based smart textiles used in self-sanitising face masks, as well as their potential, as they overcome the shortcomings of conventional face masks. It also highlights the challenges associated with embedding textiles within nanomaterials. Finally, it makes recommendations regarding safety, reusability, and enhancing the protection of the wearer from the environment and underscores the benefits of reusable masks, which would otherwise pollute the environment. These self-sanitising face masks are environmentally sustainable and ideal for healthcare, the food industry, packaging, and manufacturing. Full article