Advanced Detection Methods Research Articles

Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy.

Rapid detection of bacterial pathogens is essential for food safety and public health, yet bacteria can evade detection by entering a viable but nonculturable (VBNC) state under sublethal stress, such as antimicrobial residues. These bacteria remain active but undetectable by standard culture-based methods without extensive enrichment, necessitating advanced detection methods. This study developed an AI-enabled hyperspectral microscope imaging (HMI) framework for rapid VBNC detection under low-level antimicrobials. The objectives were to (i) induce the VBNC state in Escherichia coli K-12 by exposure to selected antimicrobial stressors, (ii) obtain HMI data capturing physiological changes in VBNC cells, and (iii) automate the classification of normal and VBNC cells using deep learning image classification. The VBNC state was induced by low-level oxidative (0.01% hydrogen peroxide) and acidic (0.001% peracetic acid) stressors for 3days, confirmed by live-dead staining and plate counting. HMI provided spatial and spectral data, extracted into pseudo-RGB images using three characteristic spectral wavelengths. An EfficientNetV2-based convolutional neural network architecture was trained on these pseudo-RGB images, achieving 97.1% accuracy of VBNC classification (n=200), outperforming the model trained on RGB images at 83.3%. The results highlight the potential for rapid, automated VBNC detection using AI-enabled hyperspectral microscopy, contributing to timely intervention to prevent foodborne illnesses and outbreaks.

Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Cloud Environments

Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud computing environments, necessitating advanced detection methods. This review examines the application of Machine Learning (ML) and Deep Learning (DL) techniques for DDoS detection in cloud settings, focusing on research from 2019 to 2024. It evaluates the effectiveness of various ML and DL approaches, including traditional algorithms, ensemble methods, and advanced neural network architectures, while critically analyzing commonly used datasets for their relevance and limitations in cloud-specific scenarios. Despite improvements in detection accuracy and efficiency, challenges such as outdated datasets, scalability issues, and the need for real-time adaptive learning persist. Future research should focus on developing cloud-specific datasets, advanced feature engineering, explainable AI, and cross-layer detection approaches, with potential exploration of emerging technologies like quantum machine learning.

Novel Framework for Artificial Bubble Image Generation and Boundary Detection Using Superformula Regression and Computer Vision Techniques

Bubble multiphase systems are crucial in industries such as biotechnology, medicine, oil and gas, and water treatment. Optical data analysis provides critical insights into bubble characteristics, such as the shape and size, complementing physical sensor data. Existing detection techniques rely on classical computer vision algorithms and neural network models. While neural networks achieve a higher accuracy, they require extensive annotated datasets, and classical methods often struggle with complex systems due to their lower accuracy. This study proposes a novel framework to address these limitations. Using Superformula parameter regression, we introduce an advanced border detection method for accurately identifying gas inclusions and complex-shaped objects in multiphase environments. The framework also includes a new approach for generating realistic artificial bubble images based on physical flow conditions, leveraging the Superformula to create extensive, labeled datasets without manual annotation. Tested on real bubble flows in mass transfer equipment, the algorithms enable bubble classification by shape and size, enhance detection accuracy, and reduce development time for neural network solutions. This work provides a robust method for object detection and dataset generation in multiphase systems, paving the way for more precise modeling and analysis.

A Hybrid Framework for Dynamic Clustering and Anomaly Detection in SAP ERP Systems

The growing complexity of enterprise data in SAP ERP systems demands advanced methods for real-time anomaly detection and dynamic clustering optimization. Traditional clustering techniques, such as Standard K-Means, need to adapt to evolving data patterns, while existing AI/ML-based methods like GMM with EM and Self-Organizing Maps (SOM) face challenges in scalability and computational efficiency. This research proposes a hybrid model that integrates GMM, SOM, and adaptive K-Means to address these limitations, offering improved detection accuracy, precision, and recall. Tested on datasets of varying sizes (small, medium, large), the hybrid model consistently outperforms baseline methods, achieving up to 94% detection accuracy and high precision (95.5%) with strong scalability and interpretability. The model leverages distributed computing on a Spark cluster for efficient processing, ensuring seamless integration with SAP ERP environments. Results demonstrate the hybrid model’s ability to detect anomalies effectively, adapt to dynamic data streams, and provide actionable insights through intuitive visualizations. This comprehensive approach makes it a robust solution for maintaining data integrity and enhancing operational decision-making in increasingly complex and data-rich enterprise environments.

BIBLIOMETRIC ANALYSIS OF RESEARCH ON NON-DESTRUCTIVE TESTING IN AEROSPACE

This study presents a bibliometric analysis of non-destructive testing (NDT) in aerospace, emphasising trends, collaborations, and advancements. Using Scopus data and VOSviewer software, 1,385 publications spanning 2014 to 2025 were analysed. The methodology included keyword co-occurrence, citation analysis, and collaborative network visualisation to identify influential authors, emerging topics, and interdisciplinary connections. Results indicate a steady increase in publications, peaking in 2024, reflecting sustained global interest and advancements in aerospace NDT. Dominant keywords include "non-destructive testing," "structural health monitoring," and "composites," highlighting a primary focus on structural integrity and material evaluation. Recent trends show the integration of artificial intelligence, evidenced by the rise of terms such as "machine learning" and "deep learning," signalling a shift toward digitalised, intelligent NDT systems. Collaboration analysis revealed distinct author clusters and international partnerships, with significant contributions from China, the United States (US), and the United Kingdom (UK), supported by major funding organisations. Integrating engineering, materials science, and emerging technologies like infrared thermography and additive manufacturing underscores the field’s interdisciplinary nature. Findings also reveal an evolving focus on lightweight composite materials and advanced defect detection methods, essential for aerospace safety and efficiency. This study concludes that bibliometric analysis is a valuable tool for identifying research gaps, fostering interdisciplinary collaboration, and guiding future innovations in aerospace NDT.

FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks

The rapid growth of Internet of Things (IoT) devices has significantly increased reliance on sensor-generated data, which are essential to a wide range of systems and services. Wireless sensor networks (WSNs), crucial to this ecosystem, are often deployed in diverse and challenging environments, making them susceptible to faults such as software bugs, communication breakdowns, and hardware malfunctions. These issues can compromise data accuracy, stability, and reliability, ultimately jeopardizing system security. While advanced sensor fault detection methods in WSNs leverage a machine learning approach to achieve high accuracy, they typically rely on centralized learning, and face scalability and privacy challenges, especially when transferring large volumes of data. In our experimental setup, we employ a decentralized approach using federated learning with long short-term memory (FedLSTM) for sensor fault detection in WSNs, thereby preserving client privacy. This study utilizes temperature data enhanced with synthetic sensor data to simulate various common sensor faults: bias, drift, spike, erratic, stuck, and data-loss. We evaluate the performance of FedLSTM against the centralized approach based on accuracy, precision, sensitivity, and F1-score. Additionally, we analyze the impacts of varying the client participation rates and the number of local training epochs. In federated learning environments, comparative analysis with established models like the one-dimensional convolutional neural network and multilayer perceptron demonstrate the promising results of FedLSTM in maintaining client privacy while reducing communication overheads and the server load.

From Soil to Salad: Strategies for Reducing Foodborne Illness Outbreaks.

This study addresses the global issue of foodborne illness, specifically focusing on those resulting from the consumption of leafy green vegetables. It explores the rising trend of consuming minimally processed or raw foods and the imperative of maintaining safety standards starting at the preharvest stage to prevent pathogenic bacterial contamination. The study identifies soil and irrigation water as key sources of pathogens and emphasizes the need for strict preventive measures during production and preharvest. It discusses the challenges of postharvest decontamination and highlights the importance of early-stage prevention strategies. The paper also examines advanced pathogen detection methods and food safety practices recommended by USDA and FSMA's PSR, including HACCP and LGMA strategies. Aimed at providing insights for consumers and producers, the study underscores the necessity of effective manufacturing strategies to ensure the safety of leafy greens.

Intelligent detection of airport pavement subtle crack based on one-stage anchor-free deep neural network

ABSTRACT To solve issues with low-detection accuracy and high-missed detection rate in subtle pavement cracks, this research proposed an intelligent detection method based on a one-stage anchor-free deep neural network, which consists of a feature extraction network, detection neck, and detection head. The feature extraction network with an extended deep attention network is responsible for extracting the features and output at different scales. The detection neck containing the special spatial pyramid structure fuses the contextual information of different scales to improve the abstract features. The detection head is composed of the anchor-free mechanism with decoupling property and the reparameterized structure to improve the detection accuracy and balance the algorithm complexity. The experimental results demonstrate that the Precision, Recall, and F1-Score are, 92.74%, 91.92%, and 0.9233 respectively. It has advantages over the most advanced one-stage detection method. Based on testing results, it has prominent stability and robustness for subtle pavement crack detection.

Network Intrusion Detection Using Transformer Models and Natural Language Processing for Enhanced Web Application Attack Detection

The increasing frequency and complexity of web application attacks necessitate more advanced detection methods. This research explores integrating Transformer models and Natural Language Processing (NLP) techniques to enhance network intrusion detection systems (NIDS). Traditional NIDS often rely on predefined signatures and rules, limiting their effectiveness against new attacks. By leveraging the Transformer's ability to capture long-term dependencies and the contextual richness of NLP, this study aims to develop a more adaptive and intelligent intrusion detection framework. Utilizing the CSIC 2010 dataset, comprehensive preprocessing steps such as tokenization, stemming, lemmatization, and normalization were applied. Techniques like Word2Vec, BERT, and TF-IDF were used for text representation, followed by the application of the Transformer architecture. Performance evaluation using accuracy, precision, recall, F1 score, and AUC demonstrated the superiority of the Transformer-NLP model over traditional machine learning methods. Statistical validation through Friedman and T-tests confirmed the model's robustness and practical significance. Despite promising results, limitations include the dataset's scope, computational complexity, and the need for further research to generalize the model to other types of network attacks. This study indicates significant improvements in detecting complex web application attacks, reducing false positives, and enhancing overall security, making it a viable solution for addressing increasingly sophisticated cybersecurity threats

A probabilistic approach driven credit card anomaly detection with CBLOF and isolation forest models

Businesses throughout the world are starting to accept more and more electronic payment alternatives for in-person and online purchases. Unusual behaviour such as internet fraud and payment defaults, which may lead to significant monetary losses, have become increasingly widespread as credit card use in online buying has expanded. To find a solution to this problem, researchers looked into a number of different machine learning classifiers that may identify irregularities in the data pertaining to credit card transactions. Overlapping class samples and an uneven distribution of classes make it hard to find outliers in this data. General learning algorithms may favour samples from the majority class, and the detection rate of anomalies in samples from minority classes is thus low. Our proposed Credit Card Outlier Detection (CCOD) model incorporates many machine learning algorithms to enhance the detection rates of credit card anomalies. Utilising the stratified sampling technique and the k-fold cross-validation process, we address data imbalance and overfitting. Working with optimized selected features rather than all features reduces the danger of overfitting and improves model performance. It has been noted that the findings of Cluster-Based Local Outlier Factor (CBLOF) classifier on European Credit Card Dataset and Isolation Forest classifier on German credit card dataset were superior to those of the other classifiers with Mathew correlation coefficient value of 0.95 and 0.97 respectively. The CBLOF, an advanced outlier detection method that evaluates the local density of data points within clusters. By comparing the density of a point to the densities of its surrounding clusters, CBLOF identifies outliers based on their deviation from normal cluster behaviour. In general, this CCOD model shows that it is better at dealing with issues like uneven class distribution, overfitting, and classes that overlap.

Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset

Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. Therefore, advanced malware detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.

Usage of Safety Helmet Warning System Using Deep Learning Method

K3 Helmet Usage Warning System Using Deep Method Learning Algorithm YOLOv8 is a technological innovation that combines the advanced object detection method YOLOv8 (You Only Look Once version 8) with the needs of safety in the work environment, especially the use of K3 (Occupational Health and Safety) helmets. This research aims to improve compliance with the use of K3 helmets through an automation approach using Deep Learning technology.The YOLOv8 algorithm is used to detect whether individuals in the work area are wearing K3 helmets or not. The detection results will be processed by the system to provide automatic warnings if individuals do not wearing an OHS helmet according to safety regulations. The use of Deep Learning technology in this method enables fast and accurate detection, contributing to increased awareness and compliance with workplace safety policies.With proper implementation, the system is expected to help create a safer work environment and improve the safety of workers, making the workplace more compliant with established OHS standards.

Enhancing High-Availability Database Systems: An AI-Driven Approach to Anomaly Detection

High-availability database systems are critical components in modern IT infrastructure, demanding robust mechanisms for ensuring continuous operation and data integrity. This article explores integrating artificial intelligence (AI) techniques into anomaly detection processes for such systems, addressing the limitations of traditional rule-based and statistical methods. We present a comprehensive analysis of machine learning and deep learning approaches, including supervised and unsupervised learning models, autoencoders, recurrent neural networks, and hybrid solutions that combine AI with conventional techniques. The article examines the challenges of implementing AI-powered anomaly detection in high-availability environments, such as scalability, real-time processing, and the balance between sensitivity and specificity. Through case studies in the financial and e-commerce sectors, we demonstrate these advanced detection methods' practical applications and benefits. Our findings indicate that AI-driven approaches significantly enhance the accuracy and efficiency of anomaly detection, leading to improved system reliability and performance. The article concludes by discussing emerging trends, including edge computing and explainable AI, and their potential impact on the future of database management and anomaly detection.

Enhancing preservation: Addressing humidity challenges in Indonesian heritage buildings through advanced detection methods point cloud data

Heritage buildings are valuable assets that represent national cultural identity. Proper building maintenance is a major issue for preservation, as building monitoring aspects and preventive measures are often only taken after physical damage happens. In the context of Indonesian heritage buildings, high levels of humidity which may cause condensation and soil dampness are often overlooked. Early detection methods are urgently required to effectively detect potential risks of condensation. This study aims to investigate condensation risk for heritage building surfaces by calculating thermal properties (i.e., emissivity, albedo) and Blinn–Phong BRDF values through the integration of thermal imaging and 3D scanning techniques. This approach supports architects and conservators in making informed decisions to protect and maintain cultural heritage structures. The study also highlights gaps in current Indonesian regulations regarding moisture presence and condensation risk detection in heritage buildings.

Boosting Point Set-Based Network with Optimal Transport Optimization for Oriented Object Detection

When handling complex remote sensing scenarios, rotational angle information can improve detection accuracy and enhance algorithm robustness, providing support for fine-grained detection. Point set representation is one of the most commonly used methods in arbitrary-oriented object detection tasks, leveraging discrete feature points to represent oriented targets and achieve high accuracy in angle prediction. However, due to the inherent discreteness of point set representation, it is prone to significant impact from isolated points and representational ambiguity in harsh application scenarios, leading to inaccurate detection. To address this issue, an efficient aerial object detector named BE-Det is proposed, which uses the optimal transport (OT) strategy to constrain the positions of isolated points. Additionally, a candidate point set quality evaluation scheme is designed to effectively assess the quality of candidate point sets. Experimental results on two challenging aerial datasets demonstrate that the proposed method outperforms several advanced detection methods.

An advanced sensitive detection method for internal and external exposures assessment of para-phenylenediamines and their quinone derivatives based on hierarchical pore-structured nitro-microporous organic networks

para-Phenylenediamines (PPDs) and their quinone derivatives (PPD-Qs) are emerging pollutants that are associated with neurotoxicity, reproductive toxicity, and genetic toxicity, raising significant public health concerns globally. In this study, a novel hierarchical pore-structured nitro-microporous organic networks (NO2-MONs) characterized by multiple adsorption interactions and rapid mass transfer rates was synthesized. Model fitting and adsorption experiments confirmed diverse interaction mechanisms, including pore effects, hydrogen bonding, π-π stacking, and hydrophobic interactions. Utilizing these advantages, NO2-MONs-coated solid-phase microextraction fibers facilitated the effective extraction of trace PPDs and PPD-Qs. Subsequently, a highly sensitive analytical approach was developed for detecting trace amounts of PPDs and PPD-Qs using GC-MS/MS analysis. This method significantly improved upon existing techniques, addressing the low accuracy associated with current semi-quantitative methods and enhancing sensitivity by two orders of magnitude. Notably, the achieved detection limits ranged from 0.38 to 2.6 pg mL−1, with recovery rates between 75.7 % and 117.5 % (RSD ≤ 9.8 %). This proposed approach provides technical support for investigating concentration-dose-effect relationships related to the environmental presence of PPDs and PDD-Qs, facilitating the development of relevant regulatory standards.

Smartphone-integrated inkjet-printed paper sensor and UV/Vis spectrophotometric method for on-site detection of As3+ and L-cysteine in food samples using novel AMTPP-functionalized silver nanoparticles

The increasing prevalence of arsenic (As3+) in water and its health impacts necessitate advanced detection methods. Similarly, monitoring L-Cysteine, a vital thiol-containing amino acid, is crucial for assessing physiological processes and disorders. This study presents a novel method for detecting As3+ and L-Cysteine in food samples using 4-amino-3-(D-galactopentitol-1-yl)-5-mercapto-1,2,4-triazole (AMTPP) functionalized silver nanoparticles (AMTPP-AgNPs) by UV/Vis spectrophotometric method and smartphone-assisted inkjet-printed paper-based sensors. AMTPP-AgNPs, synthesized through a detailed process, show exceptional selectivity and sensitivity to As3+ and L-Cysteine, undisturbed by other metal ions and amino acids. Characterization techniques like FTIR, DLS, Zeta Potential, AFM, and SEM detailed their morphological and interaction properties. Optimizing detection parameters, such as response time, pH, and temperature, improved sensitivity and achieved low detection limits. The limits of detection (LODs) were 0.0052 μM for L-cysteine and 0.0092 μM for As3+, as calculated using UV–Vis spectrophotometric methods. The method also demonstrated high recovery rates in spiked meat and corn samples. This research offers a cost-effective, rapid method for on-site food analysis, significantly aiding environmental and public health monitoring.

Predicting food adulterants in milk using Support Vector Machine (SVM)

Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety.

Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations.

Ship detection method based on attention guidance and multi-sample decision making

Single-stage target detection methods have the characteristics of fast training speed and short detection time. However, its feature pyramid network is difficult to suppress the background and noise information of SAR ship images, and the detection head has prediction errors. To address this problem, this paper proposes a detection model based on attention guidance and multi-sample decision for synthetic aperture radar ship detection. Firstly, an attention guidance network is proposed and added to the highest level of the feature pyramid to suppress background and noise interference, thereby improving the representation ability of features. Secondly, a multi-sample decision network is proposed to participate in the prediction of target position. This network alleviates the impact of prediction errors on detection results by increasing the number of samples output in the regression branch. Finally, a novel maximum likelihood loss function is designed. This loss function constructs a maximum likelihood function using the samples output from the multi-sample decision network, which is used to standardize the training of the decision network and further improve the accuracy of target positioning. Taking the RetinaNet network model as the baseline method, compared with the baseline method and the current advanced target detection methods, this method shows the highest detection accuracy on the ship detection dataset SSDD, with AP reaching 52.8%. Compared with the baseline method, the proposed method improves the AP evaluation index 3.4% ∼ 5.7%, and the training parameter Params only increases by 2.03 M, and the frame rate FPS only decreases 0.5Iter/s.