Detection Method Research Articles

Utilizing the '5 in 1 Magic Touch' CCRTV method for early detection of severe dengue warning signs in pediatric patients: A case study from a Type C Hospital in Indonesia

Background: Dengue fever, prevalent in tropical and subtropical regions, is recognized by WHO as a significant global health threat. Early detection of severe dengue symptoms, especially in resource-limited settings, is critical for patient outcomes. The "5 in 1 Magic Touch - CCRTV" method, which evaluates Color, Capillary Refill Time, Temperature, Pulse Rate, and Pulse Volume, provides a simple yet effective approach to monitor hemodynamics and identify early warning signs of severe dengue. Case Description: In a case study, a 3-year-old boy was admitted with fever, shortness of breath, weakness, nausea, vomiting, and epigastric pain. On the third day, he entered a critical phase with symptoms of pale extremities, delayed capillary refill time, high fever, and a weak, rapid pulse. Thorax examination revealed severe plasma leakage, pulmonary edema, and right pleural effusion, indicating a pre-shock state. The patient required close clinical monitoring, regular fluid balance management, and frequent blood tests to guide treatment. Conclusion:The CCRTV assessment method allowed for the early identification of the pre-shock condition, facilitating timely and appropriate intervention. This case highlights the potential of the CCRTV method for improving prognosis in severe dengue cases. The effectiveness of this method warrants further research, particularly in its ability to predict outcomes and improve recovery rates in children with severe dengue.

Proactive fraud defense: Machine learning’s evolving role in protecting against online fraud

As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in addressing these challenges by offering more advanced, scalable, and adaptable solutions for fraud detection and prevention. By analyzing key models such as Random Forest, Neural Networks, and Gradient Boosting, this paper highlights the strengths of machine learning in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions that enable a proactive approach to fraud prevention. Unlike rule-based systems that react after fraud has occurred, machine learning models continuously learn from new data, adapting to emerging fraud schemes and reducing false positives, which ultimately minimizes financial losses. This research emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries. Future developments in machine learning, including deep learning and hybrid models, are expected to further enhance the predictive accuracy and applicability of these systems, ensuring that organizations remain resilient in the face of new and emerging fraud tactics.

Nano-theranostics: A novel platform in drug therapy

A fundamental component of customized medicine, theranostics is a therapy approach that combines therapeutics and diagnostics with the goal of increasing drug efficacy and safety while also monitoring treatment response. It will require significant advancements in predictive medicine. Theranostics is linked to tailored drug administration based on test results as well as a diagnostic that screens patients for potential adverse drug reactions. Early diagnosis and treatment have benefited from the designed nanoparticles with unique physicochemical characteristics. This allows treatments to be delivered and the detection method to be employed concurrently, not just before or after treatment but throughout the entire regimen. The development of nanotechnology presents more opportunities for theranostics applications using nanoparticle engineering, and it has demonstrated encouraging outcomes, particularly in cancer therapy when compared with conventional treatments.

Enhancing breast cancer detection accuracy through transfer learning: A case study using efficient net

Breast cancer continues to pose a significant global health challenge, emphasizing the need for advancements in early detection methods. This study explores the application of transfer learning techniques, specifically utilizing EfficientNet, to enhance the accuracy of breast cancer detection through medical imaging. Leveraging a dataset of mammography images from the Digital Database for Screening Mammography (DDSM), the research implements various data preprocessing methods, including median filtering, contrast enhancement, and artifact removal, to ensure the quality of input data. The EfficientNet model, trained with these preprocessed images, is evaluated against other transfer learning architectures, such as DenseNet and ResNeXt50, using metrics like accuracy, AUC, precision, and F1-score. The results demonstrate that EfficientNet outperforms other models, achieving an accuracy of 95.23%, with a sensitivity of 96.67% and specificity of 93.82%. These findings suggest that transfer learning, particularly with EfficientNet, can significantly improve the predictive accuracy of breast cancer detection, offering a reliable tool for early diagnosis and personalized treatment planning. The study also discusses the potential integration of these models into clinical workflows, addressing challenges such as data privacy, model generalizability, and clinical applicability. Future research will focus on expanding the dataset and exploring the use of other advanced deep learning techniques to further enhance detection accuracy and robustness.

Culture and other direct detection methods to diagnose human granulocytic anaplasmosis

Culture and other direct detection methods to diagnose human granulocytic anaplasmosis

A comprehensive review on crack detection schemes and modelling techniques in high-speed rotor systems

ABSTRACT The combination of a rotor and bearings plays a pivotal role in various industrial applications, including gas turbines, compressors, turbochargers, aircraft engines, and wind generators. Ensuring the structural integrity of the rotor-bearing assembly during static and dynamic conditions is most important, as any failure could lead to unpredictable losses. Due to their continuous operation, high-speed rotors are more susceptible to faults such as misalignment, bow, eccentricity, and cracks. Among these faults, cracks are the most predominant fault that affects the system’s reliability. Despite significant advancements in rotor dynamics, cracks still threaten the integrity and performance of the rotor-bearing assembly. This review paper aims to thoroughly review the challenges and issues associated with crack formation in high-speed rotor systems. Various crack detection methods are also explored using vibration signals obtained from the rotor-bearing unit and their associated processing techniques. This study also gives an extensive overview of different crack modelling and analysis techniques. Furthermore, a novel rotor-bearing model is modelled using the finite element method, and the numerical results obtained from this model are validated with the existing literature model. This review paper will serve as a valuable resource for researchers that offers insights into the latest advancements and emphasises the importance of crack detection for ensuring the reliability of high-speed rotor systems.

Rapid detection of Impatiens necrotic spot virus from thrips vectors using reverse transcription-recombinase polymerase amplification

The plant virus, Impatiens necrotic spot virus (INSV), is an economically important pathogen of vegetables, fruits, and ornamental crops. INSV is vectored by the western flower thrips, Frankliniella occidentalis, a small insect pest that is globally distributed. In recent years, INSV outbreaks have reached epidemic levels in the Salinas Valley of California—an agriculturally rich region where most of the lettuce (Lactuca sativa) is produced in the United States. Due to the obligate nature in which virus transmission occurs, new tools that could rapidly detect INSV from thrips vectors would enhance our ability to predict where virus outbreaks may occur. Here, we report on the development of a reverse transcription-recombinase polymerase amplification (RT-RPA) assay that can detect INSV from individual thrips. The assay uses crude extraction methods, is performed at a single temperature of 42 °C, can be completed in 25 min, and provides sensitivity levels that are comparable to other available detection methods. When the assay was used on field populations of thrips, INSV was successfully identified and quantified from individual larvae and adults. The work provides a new cost-effective surveillance tool that can rapidly detect INSV from its insect vector and from plants.

Monocular visual detection of coal flow rate in scraper conveyor based on template matching background differencing

Abstract The monitoring of coal flow is a crucial aspect of the intelligent regulation and control of comprehensive mining equipment. In recent years, machine vision technology has become a mainstream method for quickly and efficiently extracting coal flow information. However, the majority of research in this field has focused on belt conveyors, with relatively limited investigation into the use of this technology with scraper conveyors. In order to address the need for monitoring coal flow in scraper conveyors, a monocular visual detection method of coal flow rates based on template matching-background differencing is proposed. First, the region of interet in the images captured using a monocular camera mounted at a specific location is quickly identified using an enhanced template matching method. Second, the image motion region is segmented using interframe and background differencing. Finally, the coal flow rate is calculated on the basis of the number of pixel points in the segmented image. Experimental verification is performed using scraper conveyor test bench and real underground data. The results demonstrate that the proposed coal flow detection method is capable of achieving real-time detection of coal flow in scraper conveyor and provides a theoretical basis for the monitoring of coal flow of the scraper conveyor.

Detecting the dimensionality of genuine multiparticle entanglement

Complex forms of quantum entanglement can arise in two qualitatively different ways: either between many qubits or between two particles with higher-than-qubit dimension. While both the many-qubit frontier and the high-dimension frontier are well established, state-of-the-art quantum technology is becoming increasingly able to create and manipulate entangled states that simultaneously feature many particles and high dimension. Here, we investigate generic states that can be considered both genuinely high-dimensional and genuine multiparticle entangled. We consider a natural quantity that characterizes this key property. To detect it, we develop three different classes of criteria. These enable us both to probe the ultimate noise tolerance of this form of entanglement and to make detection schemes using sparse or even minimal measurement resources. The approach provides a simple way of benchmarking entanglement dimensionality in the multiparticle regime and general, platform-independent, detection methods that readily apply to experimental use.

Comparison of methods for cancer stem cell detection in prognosis of early stages NSCLC

Comparison of methods for cancer stem cell detection in prognosis of early stages NSCLC

Relevance Pooling Guidance and Class-Balanced Feature Enhancement for Fine-Grained Oriented Object Detection in Remote Sensing Images

Fine-grained object detection in remote sensing images is highly challenging due to class imbalance and high inter-class indistinguishability. The strategies employed by most existing methods to resolve these two challenges are relatively rudimentary, resulting in suboptimal model performance. To address these issues, we propose a fine-grained object-oriented detection method based on relevance pooling guidance and class balance feature enhancement. Firstly, we propose a global attention mechanism that dynamically retains spatial features pertinent to objects through relevance pooling during down-sampling, thereby enabling the model to acquire more discriminative features. Next, a class balance correction module is proposed to alleviate the class imbalance problem. This module employs feature translation and a learnable reinforcement coefficient to highlight the boundaries of tail class features while maintaining their distinctiveness. Furthermore, we present an enhanced contrastive learning strategy. By dynamically adjusting the contribution of inter-class samples and intra-class similarity measures, this strategy not only constrains inter-class feature distances but also facilitates tighter intra-class clustering, making it more suitable for imbalanced datasets. Evaluation on the FAIR1M and MAR20 datasets demonstrates that our method is superior compared to other methods in object detection and achieves 46.44 and 85.05% mean average precision, respectively.

Effective dense detection method for tampering detection on high quality images using PH-SIFT and RANSAC algorithm

Effective dense detection method for tampering detection on high quality images using PH-SIFT and RANSAC algorithm

License Plate Recognition Using Three-Dimensional Rotated Character Recognition and Instance Segmentation by Deep Learning

License plate recognition is currently used in various situations, such as parking lot vehicle management and the tracking of wanted vehicles. These applications require recognition from all possible camera angles. Therefore, we propose a method for license plate recognition in images captured from various camera angles. First, the license plate area is detected from the input image using the YOLOv5 object detection method. The subsequent process can be performed in two manners: by rotating the license plate to the front using projective transformation and trapezoidal correction, or by using graph matching. The first method performs Hough transforms or linear approximations as preprocessing, and then calculates the rectangle surrounding the license plate from the YOLOv5 output. After performing trapezoidal correction, the maximally stable extremal regions (MSER) are used to detect character candidates, and characters are recognized using 3D rotated character recognition. The second method performs character candidate detection and recognition without trapezoidal correction, followed by graph matching. These methods are versatile because they do not depend on the layout of license plates, which varies among countries and regions. Two types of datasets were used: a set of images containing Japanese license plates collected by ourselves, and a set of publicly available images containing Taiwanese license plates. Two recognition rates were evaluated: the license plate character recognition rate and license plate recognition rate in which all characters in one plate are recognized. The license plate character recognition rates using graph matching were 91.9% for the Japanese license plates and 89.3% for the Taiwanese license plates. The license plate recognition rates were 84.0% and 84.5%, respectively.

Automatic detection method of polar cap arc based on YOLOX embedded with CBAM

Automatic detection method of polar cap arc based on YOLOX embedded with CBAM

Evaluation of molecular and bacteriological detection methods performed on the formalin-fixed paraffin-embedded biopsy samples collected from endometrial and lymph node tuberculosis suspected patients

BackgroundEndometrial Tuberculosis is one of the most common gynecological problems known to have serious implications for the quality of life like infertility. The commonly practiced histopathology solely relies on the suggestive feature of Tuberculosis (TB) with low specificity. Regarding the alternative bacteriological and molecular detection tools, little evidence was generated on their utility in the diagnosis of endometrial tuberculosis in Ethiopia. Therefore, we aim to investigate the detection rate of molecular and bacteriological detection methods on formalin-fixed paraffin-embedded biopsy samples for the diagnosis of endometrial and lymph node TB.MethodsA retrospective cross-sectional study was conducted on 90 formalin fixed paraffin embedded biopsy samples from patients with gynecologic and lymph problems collected between 2018 and 2022 at St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia. SPSS version 26 was used for statistical analysis. The diagnostic performance was calculated using the histopathology method as the reference standard. Cohen’s Kappa value was used to measure the level of agreement. A test with a P-value of < 0.05 was considered statistically significant.ResultsA total of 90 samples were analyzed in the current study. Auramine O, GeneXpert MTB/RIF assay, and Real-Time PCR tests have shown a detection rate of 32/90 (36%), 43/90 (47.8%), and 54/90 (60%) respectively (P ≤ 0.01). The sensitivity and specificity of AO were 38.1% and 95% respectively. RT PCR showed superior sensitivity followed by GeneXpert MTB/RIF assay, 70% and 58.6%. AO and molecular methods have shown a similarly low level of agreement with histopathology (Kappa value = 0.2).ConclusionsIn a resource-limited setting, the selection of diagnostic tools needs careful attention. Putting the patients on anti-TB treatments based solely on histopathological findings may lead to undesired and adverse complications. Therefore, applying molecular and bacteriological detection methods along with histopathology, could help minimize inappropriate antimicrobial use.

Investigating hailstorm updrafts and nowcasting hail size using a novel radar-based updraft detection

Abstract Nowcasting hail size poses a major challenge in operational practice due to physical limitations of weather radar technology once hailstones are sufficiently large to enter the resonance scattering regime. Numerous radar-based hail size proxies have been derived in recent decades, but their performance is generally poor in identifying giant hail (≥ 10 cm). Using a novel thunderstorm updraft detection method, we examine the updraft characteristics of hailstorms in the U.S. Great Plains based on a NEXRAD dataset of 114 hail events between 2013 and 2023. We find that some radar-derived variables within the detected updraft are well suited for discriminating between small (1.0 - 3.0 cm) and severe (≥ 3.5 cm) hail, e.g. minimum co-polar cross-correlation coefficient in the mid-level updraft, whereas other radar metrics such as the area of reflectivity &gt; 50 dBZ in the upper portion of the updraft suggest the presence of giant hail. However, the statistical distributions of each variable overlap for different hail sizes and there is no single metric which performs well across the entire hail size spectrum. Therefore, we trained a Random Forest model to nowcast hail size categories using a multitude of these radar metrics. The model shows promising performance for discriminating hail sizes &gt; 5 cm but requires further refinement for smaller hail. We showcase the model’s capabilities for a set of hailstorms in the Great Plains.

A composite power quality disturbance detection method based on extremum extension optimized SVMD and Teager Energy Operator

A composite power quality disturbance detection method based on extremum extension optimized SVMD and Teager Energy Operator

A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection.

Evaluation of Exchangeable Potassium Concentration in Sri Lankan Rice Paddy Soils with ICP-MS Using CaCl2 Extraction

ABSTRACT With the advancement of analytical techniques, multi-element extraction and detection have been developed. Universal extractans like CaCl2 and detection technologies like inductively coupled plasma-mass spectrometry (ICP-MS) could facilitate simultaneous analysis of multiple-elements, and would be useful in commercial scale soil analytical laboratories. The type of extractant and detection method could influence the concentration of exchangeable potassium (ex-K) determined in different soils. This study evaluated the effectiveness of 0.01 M CaCl2 as a multi-element extractant (CC) and ICP-MS and flame photometry (FP) as detection technologies when determining ex-K in soils. The widely used protocol for Sri Lankan soils, i.e. 1 M ammonium acetate (AA) as an extractant and FP as detection method, was used as the control for comparison of results. Altogether, 1,250 soil samples representing rice-growing regions in Sri Lanka were used. Soils were grouped under 16 categories based on soil type. Ex-K varied widely (26 to 1376 mg kg−1 from AA-FP method). Ex-K determined using three methods were in the order of AA-FP>CC-FP >CC-ICP-MS. On average, the values obtained from CC-FP and CC-ICP-MS methods were 53% and 24%, respectively, of those from AA-FP. Soil type-dependent variation in the detection of K using three methods was evident. In the 16 soil categories, coefficient of determination (R2) ranged from 0.23 to 0.86 for AA-FP and CC-ICP-MS, and from 0.05 to 0.70 for AA-FP and CC-FP. As a result, soil ex-K determined using CC-FP and CC-ICP-MS methods cannot be related to the results generated through AA-FP method across different soil types.

A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection’s severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures.