Accuracy Of System Research Articles

An NHS Trust comparison of reporting radiographers and consultant radiologists ability to identify cancer on chest radiographs with confirmation through a Fast track CT thorax referral system

IntroductionThe National Institute for Health and Care Excellence (NICE) recommends that GPs initially refer patients with suspected lung cancer for a chest X-ray (CXR). The Radiology department has a ‘fast track system’ to identify those patients who may have lung cancer on CXR and are referred for a CT thorax with contrast to help determine a cancer diagnosis. This fast track system was put in place to ensure the NICE guidelines and NHS England's standards on a faster cancer diagnosis are being met. This audit studied the ability of radiologists and reporting radiographers to identify lung cancer on CXRs and the accuracy of the fast-track system. Methods846 cases with lung alerts were analysed and 545 CXRs were audited. The CXRs were split into images reported by radiologists (168) and those reported by reporting radiographers (377). CT thorax results were collected through PACS and Cerner computer systems to identify if the ‘fast track’ system had yielded a “positive”, “negative”, or “other findings” result for lung cancer. Results32.8% (179) of CXRs flagged for lung cancer were positive, 40.6% (221) were negative, and 26.6% (145) had other findings. Chi square statistical test showed no significant difference (p = 0.14) between the two reporting groups in their ability to identify lung cancer on CXRs. 27% (38) of CXRs flagged by radiologists and 35% (125) by reporting radiographers were positive for lung cancer. ConclusionThis clinical audit indicates, reporting radiographers and radiologists are not statistically significantly different regarding their ability to identify lung cancer on CXRs, when supported by the fast track system. The fast-track system had a 59.4 % accuracy rate, detected by the number of imaging of reports that identified a serious pathology. This concludes that the system is performing well, yet could still be improved. Implications for practiceThis audit provides further evidence for the value of developing and deploying reporting radiographers for projection radiography reporting.

Attention-enhanced conditional-diffusion-based data synthesis for data augmentation in machine fault diagnosis

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis, impeding the development and broad adaptation of accurate and reliable deep-learning-based fault detection systems. To address these issues, we propose a novel attention-enhanced conditioning-guided diffusion-based approach for synthesizing additional training data. By generating noisy motor-current signals and employing a diffusion process to reduce noise levels, we obtain synthetic data that closely resembles the statistical properties and patterns of real-world data. The quality of the synthesized samples is assessed using a four-layer, pre-trained convolutional neural network, achieving an impressive classification rate of 96.39%. This demonstrates the effectiveness of our attention-enhanced conditioned diffusion-based model in synthesizing critical features for fault detection, making it well-suited for the introduced fault detection task. To further investigate the quality of the synthesized data samples, we implement a specific training strategy that uses the generated samples as training data. The trained diffusion model generates samples per class, which are then used to train a simple four-layer convolutional neural network for fault classification. Remarkably, the model achieves an accuracy of 98.96% when evaluated with real inverter signals, indicating that the generated samples effectively capture the distribution of real data. For further evaluation, multiple scenarios are created confirming the value of the additionally synthesized samples for real-world bearing fault detection. Our approach presents a well-suited solution to the data scarcity and class imbalance problem in machine fault diagnosis. The proposed method enhances the accuracy and reliability of fault diagnosis systems, offering valuable insights for various real-world condition monitoring applications.

Defect Detection of Tomato Fruit Using Image Dataset

Abstract: The tomato crop is more susceptible to disease than any other vegetable, and it can be infected with over 200 diseases caused by different pathogens worldwide. Tomato plant diseases have become a challenge to food security globally. Currently, diagnosing and preventing tomato plant diseases is a challenge due to the lack of essential methods or tools.The traditional techniques of detecting plant disease are arduous and error-prone. Utilizing precise or automatic detection methods in spotting early plant disease can improve the quality of food production and reduce adverse effects. Deep learning has significantly increased the recognition accuracy of image classification and object detection systems in recent years. In this study, a 15-layer convolutional neural network is proposed as the backbone for single shot detector (SSD) to improve the detection of healthy, and three classes of tomato fruit diseases. The proposed model performance is compared with ResNet-50, AlexNet, VGG 16, and VGG19 as the backbone for Single shot detector. The findings of the experiment showed that the proposed CNN-SDD achieved 98.87% higher detection accuracy, which outperformed state-of-the-art models.

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems’ overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass inaccuracy once the ellipsoid fitting method has corrected manufacturing and installation errors in the digital compass system. Second, the digital compass’s direction angle data is utilized to offset the combined navigation system’s mistake. The final objective is to create a high-performing portable TMR digital compass system that will enhance the accuracy and stability of the combined navigation system (abbreviated as CNS). According to the experimental results, the digital compass’s azimuth accuracy was 4.1824° before error compensation and 0.4580° after it was applied. The combined navigation system’s path is now more accurate overall and is closer to the reference route than it was before the digital compass was added. Furthermore, compared to the combined navigation route without the digital compass, the combined navigation route with the digital compass included is more stable while traveling through the tunnel. It is evident that the digital compass system’s design can raise the integrated navigation system’s accuracy and stability. The integrated navigation system’s overall performance may be somewhat enhanced by this approach.

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. We used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as the time series 60s before and 30s after an abnormal pattern was detected. Normal segments (90s continuous monitoring) were randomly sampled and filtered to discard sequences with missing values. Five ML models were trained on extracted features and optimized towards an Fβ score with β = 2. The results show a high inter-rater agreement (> 87%), allowing 7,858 sequences (2,944 abnormal) to be used for model development. Data were divided into 80% training and 20% test sequences. The XGBoost model had the highest Fβ score of 0.94 (with β = 2), showcasing an impressive recall of 0.98 against a precision of 0.83. This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts.

A Queue Monitoring System in OpenFlow Software Defined Networks

Real-time trac characteristic is dierent and it is very sensitive to delay. To meet trac specications in real time, monitoring systems are used as an important part of networking. Many monitoring systems are deployed to have an update view of the network QoS parameters and performance. Most of these systems are implemented to measure QoS parameters in links. Here, in this paper, a system for monitoring queues in each link by means of Software Defined Networks is proposed. The monitoring system is implemented by extending Floodlight controller, which uses OpenFlow as southbound protocol. The controller has a centralized view of the network. By the help of OpenFlow it also can provide flow level statistics. Using these advantages, the proposed system can monitor delay and available bandwidth of a queue on a link or path. Despite of monitoring systems in traditional networks, the proposed monitoring system makes a low overhead in network thanks to OpenFlow protocol messages. It is also integrated into the network controller, which enables QoS and trac engineering applications to use the system's reports for automatic trac management and QoS setup. The experimental results show a 99% accuracy of the proposed system for monitoring of both bandwidth and delay.

Determination of phenytoin at trace levels in domestic wastewater and synthetic urine samples by gas chromatography-mass spectrometry after its preconcentration by simple liquid-phase microextraction.

This work presents a sensitive and accurate analytical method for the determination of phenytoin at trace levels in domestic wastewater and synthetic urine samples by gas chromatography-mass spectrometry (GC-MS) after the metal sieve-linked double syringe liquid-phase microextraction (MSLDS-LPME) method. A metal sieve was produced in our laboratory in order to disperse water-immiscible extraction solvents into aqueous media. Univariate optimization studies for the selection of proper extraction solvent, extraction solvent volume, mixing cycle, and initial sample volume were carried out. Under the optimum MSLDS-LPME conditions, mass-based dynamic range, limit of quantitation (LOQ), limit of detection (LOD), and percent relative standard deviation (%RSD) for the lowest concentration in calibration plot were figured out to be 100.5-10964.2 μg kg-1, 150.6 μg kg-1, 45.2 μg kg-1, and 9.4%, respectively. Detection power was improved as 187.7-folds by the developed MSLDS-LPME-GC-MS system while enhancement in calibration sensitivity was recorded as 188.0-folds. In the final step of this study, the accuracy and applicability of the proposed system were tested by matrix matching calibration strategy. Percent recovery results for domestic wastewater and synthetic urine samples were calculated as 95.6-110.3% and 91.7-106.6%, respectively. These results proved the accuracy and applicability of the proposed preconcentration method, and the obtained analytical results showed the efficiency of the lab-made metal sieve apparatus.

A high-accuracy system model and accuracy evaluation method for transfer alignment

Abstract With the increasing demand for high-accuracy navigation, distributed payloads put forward higher requirements for the initial alignment accuracy of their equipped slave inertial navigation systems (INSs). However, the existing transfer alignment models have poor compensation effect on flexible deformation, resulting in low alignment accuracy. Moreover, these models cannot accurately evaluate the accuracy of transfer alignment due to ignoring the errors of the master INS. To address these two problems, a new high-accuracy transfer alignment system model is established by comprehensively considering errors of the master and slave INSs. Further, the propagation characteristics of the master and slave inertial sensor errors in transfer alignment are analyzed, and the relationship expression between the master and slave inertial sensor accuracy and the transfer alignment accuracy is derived. Thus, the accuracy of transfer alignment can be pre-evaluated according to the master and slave inertial sensor accuracy. The experiment results show that the alignment accuracy of the proposed method is improved by more than 71.56% compared with the traditional methods, and the relative errors between the proposed accuracy evaluation method and the Monte Carlo simulations are less than 9.14%. Thus, the correctness, effectiveness and superiority of the proposed method are verified.

First Accuracy and User-Experience Evaluation of New Continuous Glucose Monitoring System for Hypoglycemia Due to Hyperinsulinism.

Patients with congenital hyperinsulinism (HI) require constant glucose monitoring to detect and treat recurrent and severe hypoglycemia. Historically, this has been achieved with intermittent self-monitoring blood glucose (SMBG), but patients are increasingly using continuous glucose monitoring (CGM). Given the rapidity of CGM device development, and increasing calls for CGM use from HI families, it is vital that new devices are evaluated early. We provided two months of supplies for the new Dexcom G7 CGM device to 10 patients with HI who had recently finished using the Dexcom G6. Self-monitoring blood glucose was performed concurrently with paired readings providing accuracy calculations. Patients and families completed questionnaires about device use at the end of the two-month study period. Compared to the G6, the G7 showed a significant reduction in mean absolute relative difference (25%-18%, P < .001) and in the over-read error (Bland Altman +1.96 SD; 3.54 mmol/L to 2.95 mmol/L). This resulted in an improvement in hypoglycemia detection from 42% to 62% (P < .001). Families reported an overall preference for the G7 but highlighted concerns about high sensor failure rates. The reduction in mean absolute relative difference and over-read error and the improvement in hypoglycemia detection implies that the G7 is a safer and more useful device in the management of hypoglycemia for patients with HI. Accuracy, while improved from previous devices, remains suboptimal with 40% of hypoglycemia episodes not detected.

SignSense: AI Framework for Sign Language Recognition

Sign Language recognition is a pioneering framework designed to advance the field of Sign Language Recognition (SLR) through the innovative application of ensemble deep learning models. The primary goal of this research is to significantly improve the accuracy, resilience and interpretability of SLR systems. Leveraging the unique features of ResNet within an ensemble learning paradigm. The key component of InceptionResNetv2 architecture is its deep and effective feature extraction capabilities. The utilization of InceptionResNet model enhances the model ability to capture intricate details crucial for accurate sign language recognition. This framework is also to scale seamlessly, accommodating an expanding vocabulary of signs, diverse users and dynamic environmental conditions without compromising performance.

Improvement Model for Speaker Recognition using MFCC-CNN and Online Triplet Mining

Various biometric security systems, such as face recognition, fingerprint, voice, hand geometry, and iris, have been developed. Apart from being a communication medium, the human voice is also a form of biometrics that can be used for identification. Voice has unique characteristics that can be used as a differentiator between one person and another. A sound speaker recognition system must be able to pick up the features that characterize a person's voice. This study aims to develop a human speaker recognition system using the Convolutional Neural Network (CNN) method. This research proposes improvements in the fine-tuning layer in CNN architecture to improve the Accuracy. The recognition system combines the CNN method with Mel Frequency Cepstral Coefficients (MFCC) to perform feature extraction on raw audio and K Nearest Neighbor (KNN) to classify the embedding output. In general, this system extracts voice data features using MFCC. The process is continued with feature extraction using CNN with triplet loss to obtain the 128-dimensional embedding output. The classification of the CNN embedding output uses the KNN method. This research was conducted on 50 speakers from the TIMIT dataset, which contained eight utterances for each speaker and 60 speakers from live recording using a smartphone. The accuracy of this speaker recognition system achieves high-performance accuracy. Further research can be developed by combining different biometrics objects, commonly known as multimodal, to improve recognition accuracy further.

Precise Positioning of Primary System of Geodetic Points by GNSS Technology in Railway Operating Conditions

This article deals with the analysis of the accuracy of the geodetic real-time GNSS measurement procedure used in railway operating conditions in the Czech Republic. The purpose was to determine to what extent the operating conditions affect the accuracy of the measurement result and whether an accuracy of standard deviation σx,y = 5 mm in the horizontal plane could be achieved. The use of geodetic GNSS equipment with an IMU unit was also tested. The accuracy obtained in operational conditions is compared with the accuracy obtained on a calibration base using the same measurement procedure. The consistency between the accuracy of the primary system (satellite-based) and the secondary system (terrestrially measured by the traverse method) is also discussed. The analysis includes the issue of residual inhomogeneities of the uniform transformation key when converted to the Czech national coordinate system S-JTSK. It is shown that a homogeneous accuracy in coordinate standard deviation better than σx,y = 5 mm can be achieved. The results indicate that the accuracy under operational conditions is two–three times worse than the accuracy achieved by the same procedure under ideal conditions on a calibration base. This is due to the non-ideal observing conditions, i.e., horizon occlusion by overlays, surrounding vegetation and multipath effects. It has been shown that the effect of multipath can be reduced by repeating short observations 3–4 h apart. Older GNSS instruments using an IMU unit in combination with an electronic compass (eCompass) are at risk of a systematic bias of up to several tens of millimeters, which can be detected by rotating the antenna by 180°. The current uniform transformation key used in the Czech Republic for the conversion of GNSS coordinates into the national system has residual geometric inhomogeneities (p = 0.90 to 10 mm/km, sporadically up to 20 mm/km), which metrologically deteriorate the results of the calculation of the terrestrially measured secondary system inserted into the GNSS measured primary system. Achieving homogeneous accuracy in coordinate standard deviation in a horizontal plane better than σx,y = 5 mm has been demonstrated in non-ideal railway operating conditions with increased risk of multipath. The innovative aspect of the approach used is that it simplifies and thus increases the efficiency of the measurement with respect to the availability of GPS, GLONASS, Galileo and BeiDou satellites, as well as reducing the effect of multipath on the noise by repeating the measurement procedure.

On the Regularization of Recursive Least-Squares Adaptive Algorithms Using Line Search Methods

Stereophonic acoustic echo cancellation (SAEC) requires the identification of four unknown impulse responses corresponding to four loudspeaker-to-microphone pairs. Recent developments in the field of adaptive filters for SAEC setups have allowed for the handling of a single complex-valued adaptive impulse response, instead of the four classical real-valued adaptive filters. With the simplified framework provided by the widely linear (WL) model, more advanced versions of recursive least-squares (RLS) were employed in order to take advantage of their superior tracking speeds when working with highly correlated input signals (such as speech). Despite the performances and numerical stability provided by using exponentially weighted versions of the RLS family in combination with line search methods (LSMs), the SAEC configurations have limited capabilities in mitigating the negative effects caused by high-level disturbances affecting the two microphone signals. Such is the case of double-talk scenarios, which considerably reduce the update accuracy of the adaptive system. This paper analyzes a regularization technique for the named WL-RLS-LSM adaptive filters by adjusting the correlation matrix associated with the input signals and creating a reaction in the update process. The proposed method is designed to considerably slow (or even freeze) the adaptation process while the disturbance is manifested. Simulation results are discussed in order to validate the theoretical content.

The Influence of the Setup on the Result of Measuring the Roundness of an Anuloid Surface

In engineering practice, anuloid surfaces are produced that are subject to strict requirements in terms of accuracy (e.g., in the bearing industry). Roundness is also often measured. The accuracy of the roundness measurement does not only depend on the accuracy of a particular metrological system. The setting of the component on the measuring device can also have a significant influence on the accuracy of the measurement. The axis of the measured surface must be perpendicular to the section in which the roundness is measured. Tilting the measured surface causes a systematic measurement error, which depends on the size of the tilt angle and the parameters of the measured surface. This study presents mathematical relations for the calculation of this error when measuring roundness on an anuloid surface. Mathematical relations for the internal and external surfaces formed by the inner and outer parts of the anuloid are derived herein. There is also a theoretical analysis of the influence of inclination and the parameters of the measured surface on the size of this error. Theoretically calculated values were compared with practically measured values of roundness. The error had a negative effect on the accuracy of the roundness measurement. It affected the roundness value, the second harmonic component, and also the roundness profile itself.

Automatic Hydrotest Unit for Heat Exchanger

Hydrostatic test most formally known as hydrotest is a type of test that is performed to check leakage in weld joints and assembled parts of different parts of pressure vessel and pipe lines. Hydrotest involves the filling of water or liquid in the pressure vessel and pressured it and check whether there is any leakage by the of pressure gauge or transmitters. The automatic hydrostatic test bed system is the system to eliminate manual intervention and make the system automatic. This test bed uses proportional control technology to realize the automatic load and automatic control of hydraulic motor, greatly improved stability accuracy and strong controllability of the automatic test bed system.

Exploration on Wireless Network-Based Monitoring and Early Warning Algorithms for Power Grounding Faults

Electricity is an indispensable resource in daily life. While it brings great convenience to people, it also brings some safety hazards, especially in the event of power failure, which may cause significant harm. Therefore, monitoring and warning of power faults are very important. Traditional power grounding fault monitoring and warning systems have problems such as untimely monitoring, inaccurate warning, and high error rates in fault location. In order to solve the above problems, this paper uses wireless networks to construct a power grounding fault monitoring and early warning system. The wireless network collected fault data based on the complexity of power grounding fault data, and used threshold monitoring method to analyze the collected data. The wireless network was used to construct a prediction model to monitor grounding faults. Through experiments, it can be found that the accuracy of the wireless network-based power fault monitoring system for predicting grounding faults was over 92.57%, and the average warning accuracy of 20 experiments was 93.856%. This paper studied a wireless network-based algorithm for monitoring and warning of power grounding faults, which can effectively improve the monitoring and warning capabilities of power systems, and reduce the risk of power equipment faults and the probability of power accidents.

Empirical Enhancement of Intrusion Detection Systems: A Comprehensive Approach with Genetic Algorithm-based Hyperparameter Tuning and Hybrid Feature Selection

AbstractMachine learning-based IDSs have demonstrated promising outcomes in identifying and mitigating security threats within IoT networks. However, the efficacy of such systems is contingent on various hyperparameters, necessitating optimization to elevate their performance. This paper introduces a comprehensive empirical and quantitative exploration aimed at enhancing intrusion detection systems (IDSs). The study capitalizes on a genetic algorithm-based hyperparameter tuning mechanism and a pioneering hybrid feature selection approach to systematically investigate incremental performance improvements in IDS. Specifically, our work proposes a machine learning-based IDS approach tailored for detecting attacks in IoT environments. To achieve this, we introduce a hybrid feature selection method designed to identify the most salient features for the task. Additionally, we employed the genetic algorithm (GA) to fine-tune hyperparameters of multiple machine learning models, ensuring their accuracy in detecting attacks. We commence by evaluating the default hyperparameters of these models on the CICIDS2017 dataset, followed by rigorous testing of the same algorithms post-optimization through GA. Through a series of experiments, we scrutinize the impact of combining feature selection methods with hyperparameter tuning approaches. The outcomes unequivocally demonstrate the potential of hyperparameter optimization in enhancing the accuracy and efficiency of machine learning-based IDS systems for IoT networks. The empirical nature of our research method provides a meticulous analysis of the efficacy of the proposed techniques through systematic experimentation and quantitative evaluation. Consolidated in a unified manner, the results underscore the step-by-step enhancement of IDS performance, especially in terms of detection time, substantiating the efficacy of our approach in real-world scenarios.

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Road potholes have a well-known impact on driving quality and safety. Therefore, timely mitigation of potholes is critical for the safety of road users. However, efficient and timely maintenance relies on the presence of an effective process for pothole detection. Currently, transportation agencies primarily rely on manual inspection and road user reporting. These methods are subjective, prone to inaccuracy, and some are also laborious and time-consuming. An ideal pothole detection system would be accurate, objective, automated, and relatively inexpensive. In this context, accuracy encompasses three distinct performance areas: detection, localization, and size estimation. This study explores the potential of utilizing a mobile light detection and ranging (LiDAR) for accurate detection and size estimation, along with a global navigation satellite system (GNSS) receiver for localization, to develop an effective pothole surveillance system. To achieve this objective, the study proposes a four-step framework. Firstly, the LiDAR data are processed to generate ring-wise cross-sectional images. Secondly, a deep learning object detection network is trained to predict the presence and size of potholes. Thirdly, the ring-wise inferences are aggregated to produce a final decision. Lastly, the aggregated inferences are synchronized with GNSS locations to generate inspection maps. The system’s performance was validated using multiple road strips, never seen by the model, containing potholes of different sizes and shapes. The results demonstrated the effectiveness and accuracy of the proposed system. Overall, this research contributes to the research on LiDAR-based pothole inspection by proposing a novel four-step framework and incorporating it into an end-to-end pothole detection system, which can greatly improve the efficiency of pothole maintenance and enhance the safety of road users.

E-Commerce Data Mining Analysis based on User Preferences and Assiciation Rules

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems.

DMS-yolov8 A gesture recognition algorithm based on improved yolov81

Aiming at the problems of slow speed and low accuracy of traditional neural network systems for real-time gesture recognition in complex backgrounds., this paper proposes DMS-yolov8-a gesture recognition method to improve yolov8. This algorithm replaces the Bottleneck convolution module in the backbone network of yolov8 with variable row convolution DCNV2, and increases the feature convolution range without increasing the computation amount through a more flexible feeling field. in addition, the self-developed MPCA attention module is added after the feature output layer of the backbone layer, which improves the problem of recognizing the accuracy of difference gestures in complex backgrounds by effectively combining the feature information of the contextual framework, taking into account the multi-scale problem of the gestures in the image, this paper introduces the SPPFCSPS module, which realizes multi-feature fusion and improves real-time accuracy of detection. Finally, the model proposed in this paper is compared with other models, and the proposed DMS-yolov8 model achieves good results on both publicly available datasets and homemade datasets, with the average accuracy up to 97.4% and the average mAP value up to 96.3%, The improvements proposed in this paper are effectively validated.