Accuracy Of System Research Articles

Diagnostic Accuracy of Artificial Intelligence-Based Systems for Detecting Diabetic Retinopathy: A Systematic Review

Diabetic retinopathy (DR) represents a leading cause of blindness worldwide, early detection is critical to prevent vision loss. However, traditional screening methods, which rely on human experts, prove to be costly and time-consuming. The systematic review aims to assess the validity of artificial intelligence (AI) as a screening tool for detecting DR among diabetic patients. A systematic literature search was performed of the following databases: PubMed, Scopus, CINAHL, and Web of Science. The last date of our search was January 31, 2024. We included all observational studies, including cohort, case–control and cross-sectional studies and evaluated their quality using the Joanna Briggs Institute tool. We included diagnostic test accuracy studies evaluating the use of AI algorithms for DR screening in patients with diabetes. Studies were excluded if they exclusively assessed diagnostic accuracy for DR that did not use AI algorithms as a diagnostic tool and studies with incomplete or inaccessible data. Thirteen studies with sample sizes ranging from 69 to 1378 participants, reported good sensitivity of AI for detecting visually threatening DR (VTDR). The lowest sensitivity was 89.2%, and the highest was 100%. In terms of specificity, Any DR exhibited higher specificity compared to RDR and VTDR, ranging from 80.2% to 100%. The sensitivity and specificity of the Artificial Intelligence (AI)-based tools available for DR screening was considered acceptable, especially in detecting VTDR and Any DR, was regarded as good. These results implied the potential usefulness of these tools for DR screening in settings with limited resources. However, further high-quality comparative studies were deemed necessary to evaluate their effectiveness in real-world clinical settings.

Improved Sliding Mode Model Reference Adaptive System Observer

In order to improve the accuracy and stability of sensorless control system of permanent magnet synchronous motor, considering the influence of external load torque on system performance, the symbol function of sliding mode control system of vehicle-mounted permanent magnet synchronous motor is prone to chattering and the robustness of traditional PI control is insufficient. In this paper, a method of combining Non-singular Fast Terminal Sliding Mode (NFTSM) and Model Reference Adaptive System (MRAS) is proposed. A Non-singular Fast Terminal Sliding Mode Controller (NFTSMC) is designed. A control strategy combining NFTSMC and Model Reference Adaptive System Observer (MRASO) (NFTSMC-MRASO) converts the observed torque value into torque current feedforward compensation to the current loop input. The large sliding mode gain is avoided, and the chattering problem of the system is overcome when the external disturbance is suddenly added. At the same time, the robustness of the system is improved under the premise of suppressing the chattering.

A Study of New Indexing Techniques for Multimodal Identification Using Iris, Fingerprint, and Face Biometrics

This paper explores innovative indexing techniques for multimodal biometric identification systems, focusing on iris, fingerprint, and facial recognition technologies. With the proliferation of digital identity verification needs, traditional single-modal biometric systems often fall short in terms of accuracy, speed, and security. Multimodal biometric systems, which integrate multiple biological characteristics, are becoming crucial for enhancing identification performance. This study aims to present a comprehensive analysis of new indexing methods that can significantly improve the efficiency, accuracy, and security of multimodal biometric identification systems.

Design and validation of a high-speed hyperspectral laparoscopic imaging system.

Minimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS. We present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system's accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera. Standard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues. Multiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600nm), red/NIR (RNIR) (610 to 850nm), and NIR (665 to 950nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of , , and 3.47% were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques. We developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery.

Inheriting the glory of PāṆinian grammar into computer science

the integration of Pāṇinian grammar principles into computer science represents a significant advancement in computational linguistics and natural language processing. Pāṇini, an ancient Indian grammarian, formulated an elaborate system of grammar known as Aṣṭādhyāyī around the 4th century BCE, which has been revered for its systematic and concise rules governing Sanskrit language structure. In recent years, there has been a growing interest in adapting Pāṇinian principles to enhance computational frameworks for understanding human languages. This paper explores the theoretical foundations of Pāṇinian grammar and its applicability in the context of modern computational linguistics. The integration of Paninian grammar into computer science enhances the accuracy and efficiency of natural language processing system and contributes to preservation and dissemination of ancient linguistic knowledge. Through this the researchers aim to bridge gap between traditional linguistic scholarship and modern computational methodologies. By embracing the systematic and rigorous framework laid out by Pāṇini millennia ago, researchers are poised to achieve new breakthroughs in understanding and processing human language through innovative computational techniques. The journey from ancient Sanskrit grammar to innovative computer science exemplifies a harmonious blend of tradition and innovation in the quest for linguistic knowledge and technological advancement. Keywords - Pāṇini, Aṣṭādhyāyī, Computational linguistics, Machine translation, semantic analysis, syntactic rules.

Evaluation of a continuous glucose monitoring system in healthy dairy calves and adult goats.

To determine the accuracy of a continuous glucose monitoring system (CGMS) device by comparing glucose concentrations measured over time as determined by the CGMS to those of the chemistry analyzer (reference method). 7 healthy goats and 7 dairy calves. A randomized, crossover design with 3 treatments: control, hypoglycemia, and hyperglycemia. The CGMS device was applied to the neck. Hypoglycemia and hyperglycemia were induced by insulin and xylazine, respectively. Glucose concentrations were measured by the chemistry analyzer CGMS, point-of-care glucometer, and intensive care unit machine at 0 (before treatment), 2, 4, 6, 8, 10, and 12 hours. Agreement between the CGMS and the chemistry analyzer was determined by Bland-Altman plots. The analytical and clinical accuracy of the CGMS was determined using the International Organization for Standardization (ISO) 15197:2013 criteria and the Parkes error grid analysis. In goats, the CGMS overestimated glucose concentrations during the hypoglycemic, normoglycemia, and hyperglycemia treatments. In calves, the CGMS underestimated glucose concentrations during the hypoglycemic treatment but overestimated glucose concentrations in normoglycemia and hyperglycemic treatments. The CGMS met the ISO clinical accuracy criteria for goats and calves, with > 99% of the glucose measurements in zones A and B of the Parkes grid. However, the CGMS did not meet the ISO 15197:2013 criteria for analytical accuracy. The CGMS evaluated in our study only met the ISO 15197:2013 clinical accuracy criteria, not the analytical accuracy. Therefore, the device might be considered for clinical use.

Sufficient condition for reliable logic operations in an over damped bistable system driven by Gaussian white noise.

In this paper, we investigate an overdamped bistable system subject to Gaussian white noise and logical inputs. By solving and estimating the steady distribution of the corresponding Fokker-Planck equation and considering the two essential features of the reliable logic operations (RLOs)-initial value independence and sign invariance-we establish the sufficient condition for RLO occurrence and identify parametric resonance phenomena in the system. Our numerical simulations confirm the reliability and accuracy of the theoretical results. This work offers insights into enhancing the accuracy of stochastic systems, particularly in the realm of logical stochastic resonance, thereby contributing to advancements in understanding and controlling stochastic dynamical systems.

Advancing neural network-based data assimilation for large-scale spatiotemporal systems with sparse observations

Data assimilation (DA) is a powerful technique for improving the forecast accuracy of dynamic systems by optimally integrating model forecasts with observations. Traditional DA approaches, however, encounter significant challenges when applied to complex, large-scale, highly nonlinear systems with sparse and noisy observations. To overcome these challenges, this study presents a new Neural Network-based Data Assimilation (DANet) model, specifically employing a Convolutional Long Short-Term Memory architecture. By leveraging the strengths of neural networks, DANet establishes the relationship among model forecasts, observations, and ground truth, facilitating efficient DA in large-scale spatiotemporal forecasting with sparse observations. The effectiveness of the DANet model is demonstrated through an initial case study of wind-driven oceanic flow forecasting, as described by a Quasi-Geostrophic (QG) model. Compared to the traditional Ensemble Kalman Filter (EnKF), DANet exhibits superior performance in cases involving both structured and unstructured sparse observations. This is evidenced by reduced Root Mean Square Errors (RMSEs) and improved correlation coefficients (R) and Structural Similarity Index. Moreover, DANet is seamlessly integrated with the QG model to operationally forecast vorticity and stream function in the long term, further confirming the accuracy and reliability of the DANet model. DANet achieves operational forecasting 60 times faster than EnKF, underscoring its efficiency and potential in DA advancement.

Deep Feature Extraction with SVM Classifier for Brain Tumor Classification

Recently, methods for classifying brain tumors have used Deep Learning (DL), in particular, Convolutional Neural Networks (CNNs), to obtain promising results. Unlike conventional Machine Learning (ML) algorithms, CNNs can automatically extract features from images; they do not, however, need the extraction regarding hand-crafted features. Many obstacles reduce the accuracy of brain tumor classification systems; to address such obstacles, we propose a DL-based feature extraction method for brain tumor classification. A total of two pretrained CNNs, VggNet-16 and ResNet-18, are applied as deep feature extractors in the scheme to accomplish deep feature extraction. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique has been first used for enhancing the images. Second, each separately pretrained CNN, VggNet-16 and ResNet-18, is used for extracting the deep features from Magnetic Resonance Imaging (MRI). Lastly, a Multiclass Support Vector Machines (Multiclass -SVM) classifier is used to complete a classification task. Tests conducted on the aforementioned datasets: the effectiveness of the suggested approach is demonstrated by the CE-MRI Figshare, the REMBRANDT, and the Brain Tumor Classification, where the pre-trained CNN models and SVM classifier enhance performance. Specifically, the pretrained CNN with SVM approach produces accuracy ranging from 95.28% to 98.62% across all data-sets.

Accuracy of Dental Implant Placement using Robotics. A Systematic Review of in vitro Studies.

Robotic-assisted techniques have the potential to revolutionize dental implantology by offering enhanced precision, accuracy, and clinical outcomes compared to computer-assisted implant placement techniques. This study aimed to evaluate the accuracy of dental implant placement using robot-assisted implant surgery in vitro settings. An unrestricted search of indexed databases along with a manual search was performed up to March 2024. In vitro, studies comparing the positioning accuracy of robotic systems in dental implant placement of planned pre-operative coordinates and postoperative outcomes in phantom and simulated models were included. QUIN Tool was used to assess the quality of the included studies. A total of 13 in vitro studies were included. All studies except one used entry, exit, or angle deviation as parameters to assess the accuracy of implants placed on phantom models or simulated virtual implant placement. Overall, pooled entry deviations were 0.72 ± 0.68 mm, exit deviations were 0.86 ± 0.92 mm, and angular deviations were 1.47 ± 1.610 favoring robot-assisted implant surgery. Based on the current evidence, robotic-placed implants have the potential to revolutionize dental implantology by offering enhanced precision, accuracy, and clinical outcomes compared to dynamic and static computer-assisted implant surgery techniques.

Temperature control technology for PCR

AbstractIn order to solve the obvious nonlinear problem of temperature and complex structure of PCR instrument during nucleic acid amplification. In this paper, a new nucleic acid amplification device and temperature control algorithm were proposed. In the device, in order to improve the rise and fall rate and make the whole reaction device smaller and simpler, this paper uses a microfluidic chip for nucleic acid reaction. At the same time, in the warming and cooling module, the temperature is controlled by the semiconductor chilling plate, the air‐cooled cooling device and the heat sink structure, which greatly improves the speed of nucleic acid amplification. In the algorithm, a hybrid algorithm is designed, using Particle Swarm Optimization (PSO) to optimize PID algorithm parameters, and then based on fuzzy theory, according to the temperature control requirements of nucleic acid amplification, fuzzy rules are analyzed and fuzzy reasoning is carried out, and then combined with PID to achieve rapid response and overshooting control of temperature control. Finally, the measurement noise is filtered by Kalman filter. Finally, COMSOL and MATLAB software are used to simulate and compare, and it is proved that the device has a certain heat dissipation effect in the process of nucleic acid amplification. This algorithm can improve the accuracy and robustness of the control system, improve the response speed, reduce the overshoot, shorten the adjustment time, and restrain the interference.

The prediction of thermal sensation in building using support vector machine and extreme gradient boosting

The building has great potential for energy savings as one of the locations that humans often occupy. In addition to energy efficiency, humans must consider environmental sustainability and the comfort of the building's occupants. Conditioning of indoor air quality, including those related to thermal comfort, continues to be pursued to be more economical, one of which is to utilize the prediction of occupants' thermal sensations. The prediction results can be utilized to adjust room air conditions more economically. This paper proposes using extreme gradient boosting (XGBoost) and support vector machine (SVM) to predict the thermal sensation in the building. The built environment parameters are preprocessed, and the thermal sensation is predicted by intelligent systems. The ten variables that most influence the level of accuracy of this thermal sensation prediction system are thermal preference vote, indoor operative temperature, Griffith's neutral temperature, indoor globe temperature, mean radiant temperature, Indoor air temperature, predicted mean vote, and outdoor mean temperature. SVM with four features, XGBoost and XGBoost with hyperparameter tuning, achieve an accuracy of 99.45%, 97.81%, and 98.08%, respectively. Regarding computational complexity, training an SVM system with the same number of features requires a shorter time than XGBoost training. The same thing also happened with the test of the SVM system, which required a shorter time compared to the time for the examination of the XGBoost system.

Face Recognition Attendance Management System using LBPH and Haar Cascade

This research presents the implementation and design of a facial recognition attendance management system by using Local Binary Pattern Histogram (LBPH) and Haar Cascade machine learning algorithms. This system aims to automate the attendance process, providing an efficient and accurate alternative to traditional methods. The Haar Cascade algorithm is employed for face detection due to its rapid processing and high detection rate, while the LBPH algorithm is utilized for face recognition because it is simple and effective in handling changes in facial expressions, lighting, occlusions, distance from the camera, and camera resolution. The integration of these algorithms results in a robust system capable of real-time attendance tracking. Experimental results demonstrate the system's high accuracy and reliability in face detection and recognition under different conditions of lighting, distance from the camera, face expressions, occlusions, and camera resolution, making it suitable for deployment in educational institutions and corporate environments.

Automatic Detection of Anomalies in Post-Processed Data Applied to UTC Time Transfer Links.

In this article, we present a method for identifying anomalies, particularly time steps, which can affect data. Recognition of these anomalies is essential for understanding the intrinsic nature of problems that may occasionally affect the data, and for guaranteeing system reliability and accuracy. The tool presented, based on the Kalman filter, is optimized to work with post-processed data which means that the dataset is available at the time the algorithm is run. The main aim is to retain as much data as possible, while detecting anomalies, and avoid deleting valuable data. The originality of this tool with respect to the already existing Kalman-filter-based tools for detecting anomalies is substantial, because its objective is not only to enable the system to run but also to avoid unnecessary deletion of valuable data. This tool is designed to accurately determine the date of occurrence and magnitude of these anomalies, focusing on time steps. The tool presented will be applied, by way of example, to the time links used in Coordinated Universal Time (UTC) as calculated by Bureau International des Poids et Measures (BIPM), Paris, France. In addition, the algorithm developed will enable the BIPM's Time Department to be rapidly alerted to unexpected behavior that may compromise UTC performance. To guarantee the reliability and accuracy of UTC, rigorous data validation and rapid problem identification are essential.

Iris Identification Based on SVM-CNN Method

A person is automatically recognized in a biometric system by analyzing their distinct traits. Most people agree that iris recognition is the most accurate and dependable biometric identification method currently in use. The goal is to accurately and efficiently identify a person in real time by analyzing the sporadic patterns seen in the iris if an eye from some distance, by extracting strong features using deep learning technique. The extracting of significant features is important step that effect the overall accuracy of iris recognition system. In order to extract the iris characteristics, this research suggests a robust convolution neural network (CNN) structure. Then, an identity of the person is determine based on extracted features from his iris to support vector machine (SVM). The proposed system is examined on CASIA V. (1). Several parameters such as accuracy, precision, recall, and F-score are computed to evaluate the performance of the proposed system. The obtained result of the accuracy is about (99%). The proposed system results are compared with several previous methods and prove its effectiveness. A contemporary approach and the suggested method are contrasted and use deep learning for features extraction and the results depict our method outperforms other methods.

Accuracy of digital image correlation system with telecentric lens for compression tests of wood

Accuracy of digital image correlation system with telecentric lens for compression tests of wood

Improved State-Space Approach Based on Lumped Mass Matrix for Transient Analysis of Large-Scale Locally Nonlinear Structures

Due to the assumption of acceleration variation in traditional step-by-step integration methods such as Newmark, sufficiently small time steps are required to ensure numerical stability and accuracy in dynamic systems. In contrast, the state-space approach, based on piecewise interpolation of discrete load functions, does not rely on predetermined acceleration assumptions and has demonstrated high efficiency in terms of stability and accuracy. The original state-space method requires the calculation of the inverse of the structural mass in the transition matrix. However, when a lumped mass matrix is used, this computation renders the entire mass matrix singular, resulting in an invalid solution expression. To address this issue, this study proposes an improved state-space approach for the transient analysis of large-scale structural systems with local nonlinearities. In this approach, a nonlinear force corrector is introduced as an external force term applied to the linear elastic system to account for the nonlinear behavior of locally yielding components. Consequently, the original nonlinear dynamic system can be transformed into an equivalent linear elastic transient system. Furthermore, based on the lumped mass matrix, a first-order ordinary differential state-space equation for such an equivalent linear elastic transient system is derived. Simulation results from three transient system examples show that the state-space approach outperforms the Newmark method in terms of accuracy and stability for dynamic systems characterized by high frequency and low damping. The prediction results show that the state-space approach appears to be insignificantly affected by the choice of the consistent or lumped mass matrix. The numerical results show that the root-mean-square errors between the consistent and lumped matrices in the top displacement time histories of a 15-storey plane frame under various seismic intensities are all less than 1%, and in the base reaction time histories responses the discrepancies are only about 0.5%, indicating that the use of lumped mass matrices is quite reliable. When many nodes or degrees of freedom have no assigned mass, the dimensionality of the state-space equation can be significantly reduced using the lumped mass approach. Therefore, the simulation of large-scale systems can be simplified by employing the improved state-space approach with lumped mass matrices, yielding results nearly identical to those obtained using traditional methods. In conclusion, the improved state-space approach has great potential for the simulation of transient behavior in large-scale systems with local nonlinearities.

Development and application of a comprehensive evaluation index system for groundwater quality evolution patterns

In recent years, driven by rapid socio-economic development and intensified human activities, the groundwater quality has exhibited a concerning trend of degradation. The challenge lies in integrating the impacts of both natural and anthropogenic factors to establish a scientific evaluation framework for the evolution of groundwater quality. This study adopts the model of driving forces – pressures – state – impacts – responses (DPSIR) proposed by the European Environment Agency, in conjunction with the Analytic Hierarchy Process (AHP) and Information Entropy Theory (IET), and the Water Quality Index (WQI) evaluation methods, to construct an evaluation index system for groundwater quality evolution that encompasses driving forces, state, and response systems. Initially, twelve indicators relevant to groundwater quality are quantified by screening across three systems, and a functional relationship between the categorization and scoring of each indicator is established. Subsequently, the weights for each system and indicator are obtained through the AHP, and the objective weights of the indicators are determined using the IET. The scores of each indicator are then comprehensively calculated. Finally, based on the defined types of groundwater quality evolution patterns, an integrated assessment of the evolution of groundwater quality over various time periods is conducted. Taking the Shijiazhuang region as a case study and analyzing the hydrochemical data of groundwater from 1985 to 2015, the results indicate a shift in the groundwater quality evolution pattern from one dominated by natural factors to one primarily influenced by human activities (The comprehensive score of the evaluation index system has increased from 1.84 to 3.25). Among these, the application of fertilizers emerges as the most important driving factors affecting groundwater quality. Particularly, nitrate and total hardness (TH) have emerged as the most salient indicators of quality degradation, with a significant escalation in their composite scores. At the outset, nitrate registered a score of 0.408, while TH scored 0.326; yet, these values have sharply ascended to 0.716 and 0.467, respectively, by the advanced stage. The study concludes with a discussion on the accuracy, strengths, limitations, and applicability of the evaluation index system. The establishment of this evaluation framework provides a scientific basis for the management and protection of groundwater resources and serves as a reference for identifying groundwater quality evolution patterns in other regions.

Assessing the accuracy of a wireless sensor system for estimating lumbar moments during manual lifting tasks considering the effects of load weight, asymmetry, and height

This study assessed the accuracy of L5/S1 moment estimates calculated with an Inertial Motion Capture (IMC) system during an asymmetrical and variable height lifting task. The effects of load weight, asymmetry, and lifting height on estimates of lumbar moment have not been comprehensively considered in studies using IMC systems. Thirty-six participants engaged in tasks involving three loads, lifting heights, and trunk rotation angles. Lumbar moments were calculated using bottom-up and top-down biomechanical models. Gold-standard Optical Motion Capture (OMC) and Force Plates (FP) were used as the reference. A randomized block partially confounded design was used to compare the root mean square errors (RMSE) between the IMC and OMC-based reference estimates. The IMC system's estimated peak moments were 12%–13% lower than those estimated using the gold standard OMC-BU inverse dynamics, while the RMSE varied between 19 and 21 Nm. A Load*Height interaction was found; a trend was identified where the RMSE values increased as both the load and height levels increased. The angle did not show a significant effect on any of the tested scenarios. A close correspondence between the IMC and OMC-based moment estimates was established, with the load being the main factor affecting the differences between systems. The IMC system shows potential for use in occupational settings to capture data on the lumbar moments of workers, which could be utilized to assess ergonomic risk.

Recommendation Systems for the Healthcare Domain: A Comprehensive Survey of Evaluation Datasets

Healthcare Recommendation Systems (HRSs) primarily aim to offer advice, recommendations, or suggestions related to human healthcare. Similar to other information systems, datasets affect HRSs’ efficiency. The larger datasets can make the information more diverse and complete. Therefore, the recommendation systems can be more accurate and deliver better performance. In addition, several recent studies have revealed that to enhance the accuracy of systems, there should be a switch from a model-centered method to data-centricity. Therefore, several datasets have been introduced over the past few years to assess the effectiveness of HRSs. However, building new datasets and selecting suitable datasets to evaluate HRSs is an issue. Another problem is that there are presently no articles surveying the datasets that have been published to offer the requirements that a dataset uses to judge the HRSs needs to have. These are the reasons that motivated us to conduct this research. To address these problems, we systematically focus on a comprehensive survey of the datasets used to evaluate HRSs published in recent years. Some of the primary contributions of this study are as follows: (i) Surveying 34 datasets commonly used in methods for building and developing HRSs. (ii) Extracting 10 common characteristics and comparing datasets with those 10 common characteristics. (iii) Providing the 13 requirements regarding a dataset used in HRSs (iv) Comparing surveyed datasets and state-of-the-art HRSs based on 13 constructed requirements (v) Discussing the five main challenges and set out some directions for building new datasets in the future. The results of this research analysis are valuable to many new researchers in choosing or constructing suitable datasets to evaluate how they build their HRSs for the present and future.