Error Analysis Research Articles

AN ERROR ANALYSIS ON COMPARISON AND CONTRAST PARAGRAPH WRITING OF THE SECOND SEMESTER STUDENTS’ OF ENGLISH EDUCATIONAL DEPARTMENT AT PGRI UNIVERSITY OF BANYUWANGI

This research is error analysis research on students’ comparison and contrast paragraph writing of the second semester students’ of English Educational Department at PGRI University of Banyuwangi. The aims of this research was to identify the types of errors in writing comparison and contrast paragraph made by second semester students’ at PGRI University of Banyuwangi. Using a purposive sample, students in the second-semester of the 2021/2022 academic year were selected as samples in this study. The research study is classified into a descriptive qualitative study. The data source were collected from the students’ writing of second semester. The instrument used in this research is the document. Corder theory becomes the main key in the data analysis. Based on the analysis of the grammatical errors, four types of errors were found. These four errors are as follows. In class A, there are 18 total error sentences with 4 types of errors (six errors in omission, five in addition, two in misformation, and three in misordering). Meanwhile, in class B, there are 7 of total errors sentence with 4 types of errors, they are two errors in omission, two in addition, two in misformation, and one in misordering.

Superconvergent scheme for a system of Green nonlinear Fredholm integral equations

This study investigates a system of second-kind nonlinear Fredholm integral equations (SSNFIEs) featuring a Green's type kernel function. To solve this integral equations (IEs), we introduce Galerkin and iterated Galerkin (IG) methods based on piecewise polynomials. Convergence and error analysis are conducted for these methods, and we demonstrate superconvergence for the IG method. Numerical tests are employed to validate the theoretical findings.

Problem-solving difficulties, performance, and differences among preservice teachers in Western Philippines University

The ability to solve problems is a prerequisite in preparing mathematics preservice teachers. This study assessed preservice teachers’ problem-solving difficulties and performance, particularly in worded problems on number sense, measurement, geometry, algebra, and probability. Also, academic profile differences in the preservice teacher’s problem-solving performance and common errors were determined. A descriptive-comparative research design was employed with 158 random respondents. Data were gathered face-to-face during the first semester of the school year 2022-2023, and data were analyzed with the aid of jamovi software, ensuring ethical measures. Overall findings revealed that the preservice teachers experienced average difficulty in solving problems. The low performance of the preservice teachers on the given problems was also demonstrated. Further analysis revealed a significant difference between the preservice teachers’ problem-solving performance based on their subject preference and program. Moreover, the error analysis revealed that the preservice teachers incurred comprehension errors in misrepresentation, misinterpretation, and miscalculation. These results will serve as a measure for policymakers and curriculum developers of the teacher education institution concerned to make relevant enhancements to the math courses offered in the elementary and secondary education programs.

An adaptive grid method for a two-parameter singularly perturbed problem with non-smooth data

In this paper, a two-parameter singularly perturbed problem with discontinuous source and convection coefficient is studied. This problem is discretized by using a first-order upwind finite difference scheme for which a posteriori error analysis in the maximum norm is derived. Then, based on this a posteriori error estimation, a grid iteration algorithm is designed to generate an adaptive nonuniform mesh. Finally, Numerical experiments are proposed to confirm that our proposed method is first-order uniformly convergent.

Advanced Image Processing Techniques for Automated Detection of Healthy and Infected Leaves in Agricultural Systems

Advances in computer vision and machine learning have transformed leaf disease detection by enabling efficient and accurate identification of subtle disease signs in leaves. Leveraging high-resolution imaging, pattern recognition algorithms, and deep learning models, researchers and farmers can now conduct automated detection across various plant species. The development focuses on sophisticated image processing techniques applied to diverse datasets captured under controlled conditions, ensuring comprehensive coverage of lighting, time, and weather variations. Expert annotation of infection stages and types enhances dataset reliability, while pre-processing stages such as resizing and normalization optimize image consistency for robust model training. Data augmentation techniques enrich dataset diversity, complemented by feature extraction methods like RGB color analysis, GLCM texture analysis, and shape descriptors to discern healthy and infected leaves with precision Validation through K-fold cross-validation ensures model reliability across diverse datasets, culminating in a deployable application for real-time leaf health monitoring. Results demonstrate significant advancements, with the proposed model achieving 92% accuracy, surpassing Logistic Regression (87%), Decision Tree (82%), and Support Vector Machine (79%). Over 10 epochs, the model achieves steady improvements to 95% training accuracy and 85% validation accuracy, underscoring its effectiveness. Implementing data augmentation boosts accuracy from 85% to 89%, while analysis of prediction errors refines model performance for enhanced automated plant health monitoring and precision agriculture applications. These advancements highlight the transformative impact of technology in safeguarding crop resilience and optimizing agricultural practices.

Error analysis of machine learning methods as the educational background for its use skills formation

Introduction. Machine learning methods and elements of artificial intelligence are used to analyze random data, processes and signals. The study of relevant tools is already included in the various levels curricula. The purpose of the study is to demonstrate, using examples available to students of various specialties, that the error analysis of machine learning methods in solving specific tasks can be the basis in the educational process for the skills formation of using artificial intelligence elements.Materials and Methods. For processing random signals and data, widely available software is used: Microsoft Excel for preparing training and test samples, the Deductor analytical platform for implementing machine learning algorithms. As an example, quasi-harmonic signals with random parameters are processed for technical specialties, and the results of psycho diagnostics are used to process multidimensional random data.Results. As a typical solution of approximation technical problems, direct propagation neural network errors in using to determine random signal parameters are analyzed. As a solution of classification problems, multidimensional random data with different dimensions were processed using neural networks and the "decision tree" method. The advantages of the combined use of these two machine learning methods are analyzed. These examples and their analysis were tested in classes with university students in the disciplines of "Digital Signal Processing" and "Fundamentals of Statistics".Discussion and Conclusions. The statistical features of the obtained results, the possibilities of reducing the training sample and selective analysis of multidimensional random data are discussed. It is shown that an adequate assessment of the machine learning methods errors can significantly expand the possibilities of their application, and can be the basis for the formation of skills for their use.

Оптимизация контроля заболевания у ребенка с бронхиальной астмой

The achievement of good control of bronchial asthma in pediatric patients is the cornerstone of the treatment of this disease. The analysis of certain factors of partial and uncontrolled course of bronchial asthma in children is presented. As a result of the analysis, it was revealed that the reason that prevents the achievement of control in bronchial asthma in children is food sensitization, as well as a violation of the technique of inhalation of drugs. The analysis of the practical errors presented in the article will contribute to timely and competent therapy, and in general – an improvement in the quality of life of the patient.

Analysis of Learners' Errors to Solve Problems on Three-Variable Linear Equation System Material Based on Newman Stages

The purpose of this research is to describe the errors made by students in solving problems on the topic of the Three-Variable Linear Equation System (SPLTV). The problems are presented in the form of story problems. This research is a descriptive study with a qualitative approach. The subjects in this study were students from class X PPLG at SMK Muhammadiyah 2. A total of 6 subjects were selected from 24 students, consisting of 2 students from the upper group, two from the middle group, and two from the lower group. The selection of 6 subjects was intended to provide a diverse representation of student abilities, allowing for a more in-depth analysis of the common types of errors at each ability level. Data collection techniques were conducted using test instruments. Meanwhile, the data analysis technique in this study is a qualitative analysis based on Newman's framework to identify and classify the errors made by students in solving story problems in the SPLTV material. The results showed that most students still made mistakes in solving story problems based on the types of Newman stage errors, namely 1) Two students were wrong at the reading stage, 2) Two students were wrong at the comprehension stage, 3) Two students were wrong at the transformation stage, 4) Two students were wrong at the process skill stage, 5) One student was wrong at the encoding stage.

Analysis of Learners' Errors in Working on Formative Tests on the Topic of Quadratic Equations and Functions

The purpose of this study was to determine and analyze the errors of students in working on formative tests on the topic of Equations and Quadratic Functions by completing the perfect square root method and the ABC formula. This type of research is descriptive research with a qualitative approach. In this study, the subjects were taken from 12 students of class XI IT SMKS Dar El Hikmah Pekanbaru, each of which consisted of 2 students from the upper, middle, and lower groups. In this study, data collection techniques based on observation, written formative test questions, and interviews were used to determine students' errors in solving the topic of Quadratic Equations and Functions. In this study, it can be concluded that students' errors in solving the topic of Quadratic Equations and Functions with the method of completing the perfect square root and the ABC formula are the lack of understanding of the concept of the method, the lack of assignment of the topic, and the lack of students' mathematics learning so that students make many mistakes in solving problems.

A High-Speed Multichannel Electrochemical Impedance Spectroscopy System Using Broadband Multi-Sine Binary Perturbation for Retired Li-Ion Batteries of Electric Vehicles

Retired electric vehicle (EV) batteries are reused in second-life energy storage applications. However, the overall performance of repurposed energy storage systems (ESSs) is limited by the variability in the individual batteries used. Therefore, battery grading is required for the optimal performance of ESSs. Electrochemical impedance spectroscopy (EIS)-based evaluation of battery aging is a promising way to grade lithium-ion batteries. However, it is not practical to measure the impedance of mass-retired batteries due to their high complexity and slowness. In this paper, a broadband multi-sine binary signal (MSBS) perturbation integrated with a multichannel EIS system is presented to measure the impedance spectra for the high-speed aging evaluation of lithium-ion batteries or modules. The measurement speed is multiple times higher than that of the conventional EIS. The broadband MSBS is validated with a reference sinusoidal sweep perturbation, and the corresponding root-mean-square error (RMSE) analysis is performed. Moreover, the accuracy of the presented multichannel EIS system is validated by impedance spectra measurements of Samsung INR18650-29E batteries and compared with those measured using a commercial EIS instrument. A chi-squared error under 0.641% is obtained for all channels. The non-linearity of batteries has a significant impact on the quality of impedance spectra. Therefore, Kronig–Kramer (KK) transform validation is also performed.

Multimodal deep learning for dementia classification using text and audio

Dementia is a progressive neurological disorder that affects the daily lives of older adults, impacting their verbal communication and cognitive function. Early diagnosis is important to enhance the lifespan and quality of life for affected individuals. Despite its importance, diagnosing dementia is a complex process. Automated machine learning solutions involving multiple types of data have the potential to improve the process of automated dementia screening. In this study, we build deep learning models to classify dementia cases from controls using the Pitt Cookie Theft dataset from DementiaBank, a database of short participant responses to the structured task of describing a picture of a cookie theft. We fine-tune Wav2vec and Word2vec baseline models to make binary predictions of dementia from audio recordings and text transcripts, respectively. We conduct experiments with four versions of the dataset: (1) the original data, (2) the data with short sentences removed, (3) text-based augmentation of the original data, and (4) text-based augmentation of the data with short sentences removed. Our results indicate that synonym-based text data augmentation generally enhances the performance of models that incorporate the text modality. Without data augmentation, models using the text modality achieve around 60% accuracy and 70% AUROC scores, and with data augmentation, the models achieve around 80% accuracy and 90% AUROC scores. We do not observe significant improvements in performance with the addition of audio or timestamp information into the model. We include a qualitative error analysis of the sentences that are misclassified under each study condition. This study provides preliminary insights into the effects of both text-based data augmentation and multimodal deep learning for automated dementia classification.

Research on Calculation Method of On-Orbit Instrumental Line Shape Function for the Greenhouse Gases Monitoring Instrument on the GaoFen-5B Satellite

The Greenhouse Gases Monitoring Instrument is based on the spectroscopic principle of spatial heterodyne spectroscopy technology and has the characteristics of no moving parts, a hyperspectral resolution, and a large luminous flux. The instrumental line shape function is one of the most important parameters characterizing the features of the instrument, and it plays a vital role in the system error analysis of the instrument’s measurements. To accurately obtain the instrumental line shape function of a spatial heterodyne spectrometer during the on-orbit period and improve the accuracy of gas concentration retrieval, this study develops a method to model and characterize the characteristics of the instrumental line shape function, including modulation loss and phase error. This study employs the solar calibration spectrum in the 1.568–1.583 μm bands to conduct iterative calculations of the instrumental line shape function error model. After the instrumental line function is updated, the average relative deviation is reduced from 1.83% to 0.756% between the theoretical and measured solar spectra. Additionally, the average relative deviation is reduced from 7.049% to 2.106% between the GMI nadir and theoretical nadir spectra. The findings demonstrate that updating the instrumental line shape function mitigates the impact of variations in the spectrometer’s instrumental line shape due to alterations in the orbital environment. This study offers a dependable reference for both the enhancement and oversight of a spectrometer’s instrumental line shape function, along with an investigation of shifts in instrument parameters.

Accuracy of FreeStyle Libre flash glucose monitoring in patients with type 2 diabetes who migrated from highlands to plains.

The FreeStyle Libre flash glucose monitoring (FGM) system entered the Chinese market in 2017 to complement the self-monitoring of blood glucose. Due to its increased usage in clinics, the number of studies investigating its accuracy has increased. However, its accuracy has not been investigated in highland popu-lations in China. To evaluate measurements recorded using the FreeStyle Libre FGM system compared with capillary blood glucose measured using the enzyme electrode method in patients with type 2 diabetes (T2D) who had migrated within 3 mo from highlands to plains. Overall, 68 patients with T2D, selected from those who had recently migrated from highlands to plains (within 3 mo), were hospitalized at the Department of Endocrinology from August to October 2017 and underwent continuous glucose monitoring (CGM) with the FreeStyle Libre FGM system for 14 d. Throughout the study period, fingertip capillary blood glucose was measured daily using the enzyme electrode method (Super GL, China), and blood glucose levels were read from the scanning probe during fasting and 2 h after all three meals. Moreover, the time interval between reading the data from the scanning probe and collecting fingertip capillary blood was controlled to < 5 min. The accuracy of the FGM system was evaluated according to the CGM guidelines. Subsequently, the factors influencing the mean absolute relative difference (MARD) of this system were analyzed by a multiple linear regression method. Pearson's correlation analysis showed that the fingertip and scanned glucose levels were positively correlated (R = 0.86, P = 0.00). The aggregated MARD of scanned glucose was 14.28 ± 13.40%. Parker's error analysis showed that 99.30% of the data pairs were located in areas A and B. According to the probe wear time of the FreeStyle Libre FGM system, MARD1 d and MARD2-14 d were 16.55% and 14.35%, respectively (t = 1.23, P = 0.22). Multiple stepwise regression analysis showed that MARD did not correlate with blood glucose when the largest amplitude of glycemic excursion (LAGE) was < 5.80 mmol/L but negatively correlated with blood glucose when the LAGE was ≥ 5.80 mmol/L. The FreeStyle Libre FGM system has good accuracy in patients with T2D who had recently migrated from highlands to plains. This system might be ideal for avoiding the effects of high hematocrit on blood glucose monitoring in populations that recently migrated to plains. MARD is mainly influenced by glucose levels and fluctuations, and the accuracy of the system is higher when the blood glucose fluctuation is small. In case of higher blood glucose level fluctuations, deviation in the scanned glucose levels is the highest at extremely low blood glucose levels.

Analiza porównawcza wybranych metod kalibracji kamery termowizyjnej z chłodzonym detektorem pracującym w zakresie MWIR

For accurate remote temperature measurement with a thermal imaging camera, it is necessary to perform radiometric calibration. This allows the radiative parameters of an object, and therefore its temperature, to be related to the response of the infrared detector observing it. The article presents the basic concepts related to radiometric calibration and various factors influencing its effectiveness. The most common calibration methods were also reviewed, and then two of them were implemented and compared on a specially constructed laboratory station, using proprietary software and equipped with a cooled infrared camera, operating in the MWIR range. The camera was calibrated in the temperature range from 20 °C to 50 ° C. The comparison of methods was carried out on the basis of a series of temperature measurements, analysis of absolute and relative errors, as well as analysis of the dispersion of the measured temperature values.

Analysis of the nonlinear Fitzhugh–Nagumo equation and its derivative based on the Rabotnov fractional exponential function

The Fitzhugh–Nagumo equation is an essential governing equation that explains the process of impulse transmission from the nerves. This work suggests a novel generalized arbitrary-order Fitzhugh–Nagumo equation that is based on the recently developed nonsingular fractional operator. The modified Fitzhugh–Nagumo equation has been generalized using fractional Yang–Abdel–Cattani operator to provide a more accurate representation of the equation with memory effects and non-local behavior. Further, we discussed some interesting and new properties of the proposed fractional operator and the Rabotnov exponential fractional kernel with a modified Sumudu integral transform. In order to solve the proposed nonlinear differential equation, the homotopy perturbation method coupled with the modified Sumudu integral transform technique is implemented to examine the result of a newly proposed Yang–Abdel–Cattani fractional Fitzhugh–Nagumo equation, which converges to the exact solution. The nonlinear terms of this equation are handled in terms of He’s polynomials. The existence and uniqueness of the solution have been demonstrated using the Banach space fixed point theorem. The convergence and error analysis of the suggested technique are also discussed. The findings are expressed in terms of generalized Mittag-Leffler functions. The obtained results are plotted with the help of MATLAB R2016a mathematical software. The results in this work are more accurate, and it is proposed that the new Yang–Abdel–Cattani fractional derivative operator is an effective tool for finding the results of any other nonlinear problems arising in science and engineering. All three problems have been computed for different orders to confirm the significance of the fractionalizing concept in the proposed equation. The increase of the fractional order value ℘ to 1 confirms that our proposed time-fractional Fitzhugh–Nagumo equation in the Yang–Abdel–Cattani sense gets close to the analytic solution.

A Priori Error Analysis and Finite Element Approximations for a Coupled Model Under Nonlinear Slip Boundary Conditions

A Priori Error Analysis and Finite Element Approximations for a Coupled Model Under Nonlinear Slip Boundary Conditions

Analysis of Syntactic Errors in Writing of Third Semester Students of the Department of English and Literature

Purpose of the study: This research presents the results of research on syntactic errors in writing produced by third semester students of the Department of English and Literature, Alauddin State Islamic University, Makassar. The aim of this research is to identify the dominant types of syntactic errors and provide in-depth insight into the error patterns that frequently occur. Methodology: The research method used is descriptive qualitative by collecting data from students' final written exam sheets. Data collection involved obtaining final writing exam sheets from third semester AG 1&amp;2 students enrolled in the Department of English and Literature at Alauddin State Islamic University Makassar. Main Findings:The analysis results show that the most common syntax errors include omissions, additions, information errors, and ordering errors. Novelty/Originality of this study:The novelty of this research lies in the focus of analyzing syntactic errors during the third semester students studying English and Literature. with a more comprehensive and detailed approach than previous research.

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

Kinematic information such as position, velocity, and acceleration is critical to determine the three-dimensional state of the robot in space. In this study, it is aimed to estimate as visual the linear and angular velocity of a mobile robot. Additionally, another aim of this study is to determine the suitability of the concatenation or subtraction layer in the Convolutional Neural Network (CNN) that will make this estimate. For these purposes, first, a simulation environment was created. 9000 pairs of images and necessary velocity information were collected from this simulation environment for training. Similarly, 1000 pairs of images and velocity information were gathered for validation. Four different CNN models were designed and these models were trained and tested using these datasets. As a result of the test, the lowest average error for linear velocity estimation was calculated as 0.93e-3m/s and angular velocity estimation was measured as 4.37e-3rad/s. It was observed that the results were sufficient for linear and angular velocity prediction according to statistical analysis of errors. In addition, it was observed that the subtraction layer can be used instead of the concatenation layer in the CNN architectures for hardware-limited systems. As a result, visual velocity estimation of mobile robots has been achieved with this study and the framework of CNN models has been drawn for this problem.

Guessability of Indian picture symbols for communication (IPSC) and picture communication symbols (PCS) among Malayalam-speaking typical adults

The purpose of this study was to investigate the perceived symbol referent relationship for selected symbols in typical adults for two different symbol sets: Indian Picture Symbols for Communication (IPSC) and Picture Communication Symbols (PCS) to identify cultural influences on symbol recognition. A total of 240 typical adults, including 120 nonprofessionals, and 120 health professionals, from Kerala, a southern state in India, participated in the study. A guessability task involving IPSC and PCS symbols for 30 target referents was utilized. Guessability scores for each participant as well as each symbol stimuli across two symbol sets were determined. The guessability scores for IPSC were significantly (p <.001) higher than PCS. For both symbol sets, nouns were the most accurately guessed, followed by verbs, adjectives, and prepositions. The results also revealed that prompting and previous exposure to symbols tend to influence the transparency of symbols. Error analyses revealed that visual and conceptual cues within a symbol tend to influence guessability. The results indicate that cultural and linguistic factors play a role in symbol guessability. The study highlights the importance of investigating the iconicity of IPSC and PCS symbols across different populations within India, before using symbols for educational/clinical purposes.

Intersession reliability of center of pressure measurement during bipedal standing with different count-back orders

IntroductionDual-task assessments can identify changes in postural control during balance assessments. Static standing with backward counting is frequently used to evaluate postural control while dual-tasking. The most reliable countdown method for standing postural stability has not yet been defined. Research objectiveto investigate postural stability's intra- and inter-day reliability while backward counting in different steps. MethodThirty-nine healthy adults (20 females, 26.94 ± 7.55 years) completed 70 s trials of stability tests with no dual-task, counting backward under five conditions (in ones, tows, threes, fours, and fives) while standing on a force-plate in three sessions: two sessions were on the same day, and the third session was one-week apart. The repeatability of measurements was tested using repeated-measure analysis of variance, interclass correlation, and standard error of measurements. ResultsThe interclass correlation scores ranged from 0.67 to 0.92, and the standard error of measurements ranged from 2.9% to 13.4%. No significant systematic changes (p < 0.05) occurred between the testing sessions for any backward counting. DiscussionThe backward counting showed higher reliability when performed in condition 5 (counting backward in five's). The inter-day reliability scores were greater than intera-day reliability. ConclusionsDual-tasking with most backward counting (in ones to fives) is reliable, and a quantitative assessment of the center of pressure could be used to monitor the changes in postural stability between sessions.