Error Rate Research Articles

Estimation of Source Range and Location Using Ship-Radiated Noise Measured by Two Vertical Line Arrays with a Feed-Forward Neural Network

Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation using ship-radiated noise recorded by two vertical line arrays (VLAs) during the Shallow-water Acoustic Variability Experiment (SAVEX-15), employing the Sample Covariance Matrix (SCM) and the Generalized Cross Correlation (GCC) as input features. A feed-forward neural network (FNN) was used to train the model on the acoustic characteristics of the source at various distances, and the range estimation results indicated that the SCM outperformed the GCC with lower error rates. Additionally, array tilt correction using the array invariant-based method improved range estimation accuracy. The impact of the training data composition corresponding to the bottom depth variation between the source and receivers on range estimation performance was also discussed. Furthermore, the estimated ranges from the two VLA locations were applied to localization using trilateration. Our results confirm that the SCM is the more appropriate feature for the FNN-based source range estimation model compared with the GCC and imply that ocean environment variability should be considered in developing a general-purpose machine learning model for underwater acoustics.

Artificial Intelligence-Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study.

This feasibility trial describes changes in rhotic production in residual speech sound disorder following ten 40-min sessions including artificial intelligence (AI)-assisted motor-based intervention with ChainingAI, a version of Speech Motor Chaining that predicts clinician perceptual judgment using the PERCEPT-R Classifier (Perceptual Error Rating for the Clinical Evaluation of Phonetic Targets). The primary purpose is to evaluate /ɹ/ productions directly after practice with ChainingAI versus directly before ChainingAI and to evaluate how the overall AI-assisted treatment package may lead to perceptual improvement in /ɹ/ productions compared to a no-treatment baseline phase. Five participants ages 10;7-19;3 (years;months) who were stimulable for /ɹ/ participated in a multiple (no-treatment)-baseline ABA single-case experiment. Prepractice activities were led by a human clinician, and drill-based motor learning practice was automated by ChainingAI. Study outcomes were derived from masked expert listener perceptual ratings of /ɹ/ from treated and untreated utterances recorded during baseline, treatment, and posttreatment sessions. Listeners perceived significantly more rhoticity in practiced utterances after 30 min of ChainingAI, without a clinician, than directly before ChainingAI. Three of five participants showed significant generalization of /ɹ/ to untreated words during the treatment phase compared to the no-treatment baseline. All five participants demonstrated statistically significant generalization of /ɹ/ to untreated words from pretreatment to posttreatment. PERCEPT-clinician rater agreement (i.e., F1 score) was largely within the range of human-human agreement for four of five participants. Survey data indicated that parents and participants felt hybrid computerized-clinician service delivery could facilitate at-home practice. This study provides evidence of participant improvement for /ɹ/ in untreated words in response to an AI-assisted treatment package. The continued development of AI-assisted treatments may someday mitigate barriers precluding access to sufficiently intense speech therapy for individuals with speech sound disorders. https://doi.org/10.23641/asha.26662807.

Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

Improving Threshold for Fault-Tolerant Color-Code Quantum Computing by Flagged Weight Optimization

Color codes are promising quantum error-correction (QEC) codes because they have an advantage over surface codes in that all Clifford gates can be implemented transversally. However, the thresholds of color codes under circuit-level noise are relatively low, mainly because measurements of their high-weight stabilizer generators cause an increase in the circuit depth and, thus, substantial errors are introduced. This makes color codes not the best candidate for fault-tolerant quantum computing. Here, we propose a method to suppress the impact of such errors by optimizing weights of decoders using conditional error probabilities conditioned on the measurement outcomes of flag qubits. In numerical simulations, we improve the threshold of the (4.8.8) color code under circuit-level noise from 0.14% to around 0.27%, which is calculated by using an integer programming decoder. Furthermore, in the (6.6.6) color code, we achieve a circuit-level threshold of around 0.36%, which is almost the same value as the highest value in the previous studies employing the same noise model. In both cases, the effective code distance is also improved compared to a conventional method that uses a single ancilla qubit for each stabilizer measurement. Thereby, the achieved logical error rates at low physical error rates are almost one order of magnitude lower than those of the conventional method with the same code distance. Even when compared to the single-ancilla method with a higher code distance, considering the increased number of qubits used in our method, we achieve lower logical error rates in most cases. This method can also be applied to other weight-based decoders, making the color codes more promising as candidates for the experimental implementation of QEC. Furthermore, one can utilize this approach to improve a threshold of wider classes of QEC codes, such as high-rate quantum low-density parity-check codes. Published by the American Physical Society 2024

Large‐Scale High‐Density Multimode Optical Switch Matrix

AbstractOptical switching technology is emerging as a solution to the limitations of traditional electronic switching. Combining optical switching with mode‐division multiplexing effectively overcomes the limitations of expanding switching capacity solely by increasing the number of ports. This paper introduces a compact four‐mode 2 × 2 optical switch and a four‐mode reconfigurable non‐blocking 4 × 4 optical switch matrix based on the Beneš architecture. The multimode 2 × 2 switch achieves an extinction ratio larger than 15 dB for all modes in the wavelength range of 1530–1590 nm. The multimode 4 × 4 switch matrix has an extinction ratio exceeding 9 dB for all modes and states in the C‐band, with a compact size (1.46 × 0.23 mm2) and low power consumption (0–154 mW). It supports high‐integrity 50 Gbaud PAM4 signal transmission with a bit error rate below the forward error correction limit. These devices pave the way for enhanced integration density and capacity in optical switching systems.

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289.

Evaluating the Modified American Academy Pediatrics Screening Algorithm for Critical Congenital Heart Disease.

In 2018, an expert panel recommended two key modifications to the most used algorithm for screening neonates for critical congenital heart disease (CCHD). Our aim was to evaluate the outcomes of the modified algorithm compared to those of the original algorithm in a real-world setting. We compared the performance characteristics of an original CCHD algorithm used to screen term neonates at a large hospital system between October 26, 2018 and the recommended modified algorithm used between October 15, 2020 and June 30, 2022. We calculated sensitivity, specificity, false positive rate, proportion of false positives with non-CCHD illness, and error rates of test administration and interpretation for each algorithm. Sensitivity was not significantly different betweenthe modified algorithm compared to the original algorithm (40.00% vs 12.50%, p-1.00), but specificity was lower (and hence false positive rate higher) in the modified algorithm (99.91% vs. 99.98%, p<0.001). Despite a higher false positive rate in the modified algorithm, the proportion of false positives with significant non-CCHD illness was similar (36.47% vs. 28.57%, p=1.00), a finding that translated to an increase in number of cases of significant non-CCHD illness detected (11 cases out of 32,178 screens vs. 2 cases out of 32,984 screens). Error rates of test administration and interpretation were similar between the two algorithms. In this limited study, the modified algorithm for CCHD screening using pulse oximetry had a higher false positive rate than that of the original AAP algorithm.However, this higher rate led to an increased overall number of cases detected of significant non-CCHD illness.

Anti Wind‐Up and Robust Data‐Driven Model‐Free Adaptive Control for MIMO Nonlinear Discrete‐Time Systems

ABSTRACTThis article addresses a solution to one of the main challenges of online data‐driven control (DDC) methods: reducing the sensitivity of the model‐free adaptive control (MFAC) method to initial conditions and control parameters with the new control cost function and added the output error rate and integral along with a new anti‐wind up strategy for multi‐input multi‐output (MIMO) systems. The parameters introduced to the new control law have been validated using the boundary‐input boundary‐output (BIBO) approach to design and converge the controller. The simulation findings on a nonlinear auto‐regressive moving average model with exogenous inputs (NARMAX) system with triangular control input demonstrate that the proposed control rule will outperform to prototype MFAC. Furthermore, to analyze the sensitivity of the controller to the initial conditions and the uncertainties of the control parameters, 30 Monte Carlo simulations were performed with random initial conditions in the presence of disturbance in the control input, and output noise, and the results were compared with the prototype MFAC and conventional PID controller using standard criteria such as integral time absolute error, standard deviation, steady‐state error, and mean maximum error, which shows a noticeable superiority of proposed controller relative to the prototype MFAC.

Practical twin-field quantum key distribution parameter optimization based on quantum annealing algorithm

Abstract Twin-field quantum key distribution (TF-QKD) is widely studied since it can surpass the key capacity of repeaterless QKD, whereas electromagnetic interference (EMI) is one of the main challenges in its practical applications. This study is based on the Faraday-Michaelson TF-QKD. Analyze the effect of EMI on the rotation angle of the Faraday mirror causing an additional quantum bit error rate (QBER). Moreover, the quantum annealing algorithm (QA) based on quantum tunneling mechanism is applied to the optimization of practical TF-QKD. Mapping the secure key rate of TF-QKD into the evaluation function of QA and the transverse magnetic field is introduced to construct the kinetic energy term, which can realize the quantum tunneling effect. Meanwhile, the QA is improved in terms of chaotic optimization to obtain the dynamic initial value of the algorithm, the design of a perturbation method to skip the locally optimal solution, and the use of suitable temperature and magnetic field decay functions. Optimizing TF-QKD with QA, the standard deviation of QBER fluctuation caused by EMI is reduced to 0.206, the mean square error of the signal-state pulse intensity is only 3.38 ×10-4, and the optimization accuracy of the secure key rate can reach 99.8%.

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction

ABSTRACT Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are several approaches that are utilized in practice to calculate the secure key rate. In this manuscript, QKD and error rate optimization based on optimized multi-head self-attention and gated-dilated convolutional neural network (QKD-ERO-MSGCNN) is proposed. Initially, the input signals are gathered from 6G wireless networks which face obstacles to channel. For extending maximum transmission distances and improving secret key rates, the signals are fed to the variable velocity strategy particle swarm optimization algorithm, then the signals are fed to MSGCNN for analysing the quantum bit error rate reduction. The MSGCNN is optimized by intensified sand cat swarm optimization. The performance of the QKD-ERO-MSGCNN approach attains 15.57%, 23.89%, and 31.75% higher accuracy when analysed with existing techniques, like device-independent QKD utilizing random quantum states, practical continuous-variable QKD and feasible optimization parameters, entanglement and teleportation in QKD for secure wireless systems, and QKD for large scale networks methods, respectively.

Neyman-Pearson Multi-class Classification via Cost-sensitive Learning

Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying consequences. To address this asymmetry issue, two popular paradigms have been developed: the Neyman-Pearson (NP) paradigm and the cost-sensitive (CS) paradigm. Previous studies on the NP paradigm have primarily focused on the binary case, while the multi-class NP problem poses a greater challenge due to its unknown feasibility. In this work, we tackle the multi-class NP problem by establishing a connection with the CS problem via strong duality and propose two algorithms. We extend the concept of NP oracle inequalities, crucial in binary classifications, to NP oracle properties in the multi-class context. Our algorithms satisfy these NP oracle properties under certain conditions. Furthermore, we develop practical algorithms to assess the feasibility and strong duality in multi-class NP problems, which can offer practitioners the landscape of a multi-class NP problem with various target error levels. Simulations and real data studies validate the effectiveness of our algorithms. To our knowledge, this is the first study to address the multi-class NP problem with theoretical guarantees. The proposed algorithms have been implemented in the R package npcs, which is available on CRAN.

The Impact of an Automation System Built with Jenkins on the Efficiency of Container-Based System Deployment

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems.

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.

Demonstration of a low-complexity real-time FBMC transmitter for a spectral-efficiency IMDD system.

Filter bank multi-carriers (FBMC) have higher spectral efficiency, lower out-of-band spectrum leakage, and stronger ability to resist synchronization errors compared to orthogonal frequency-division multiplexing (OFDM), which has been widely considered as a promising solution for short-reach applications, such as passive optical networks. However, the traditional fast Fourier transform (FFT)-based FBMC scheme has quite high implementation complexity for high-speed optical communications. In this work, we proposed a low-complexity real-time FBMC transmitter scheme enabled by a single fully parallel pruned inverse FFT and simplified short prototype filter-based polyphase networks for a low-cost intensity-modulation and direct-detection (IMDD) system. Compared to the OFDM transmitter, the proposed FBMC one occupies a similar number of look-up tables and registers but saves 21% of real multipliers, reduces latency by 16.2%, and increases the data rate by 12.5%. The real-time FBMC/OFDM signals are generated with a field programmable gate array chip and 6-bit 30-GSa/s digital-to-analog converter and are experimentally demonstrated in an IMDD system. After a 20-km standard single-mode fiber transmission, the negligible power penalty can be achieved with the proposed FBMC transmitter scheme at the bit error rate of 3.8e-3 compared to the OFDM counterpart.

PSIX-5 Performance of black soldier fly larvae meal as secondary protein sources in extruded dog diets

Abstract Black soldier fly larvae meal (BSFL) has gained market exposure with ties to sustainability and novelty. Graded inclusions of BSFL were assessed for nutrient digestibility, stool quality, and palatability in adult dogs. The BSFL was included at 0%, 5% and 10% in chicken-based extruded dog diets (formulated to 22% crude protein, 13% crude fat, 3% crude fiber). The study was approved by the facility’s Institutional Animal Care and Use Committee. A standard 2-bowl palatability test over a 2-d period was executed (n = 30 dogs each) to determine intake ratios between diets (0% vs. 5%, 0% vs. 10%, and 5% vs. 10%). Total tract nutrient digestibility was evaluated (n = 6 dogs per diet) with 5 d acclimation followed by 5 d total fecal collection. Stool quality was evaluated on a 1 to 5 scale. Palatability data were analyzed using paired t-test. To test for mean differences in digestibility and stool quality outcomes between 0%, 5%, and 10% BSFL dogs, we applied the Kruskal-Wallis test. Statistical significance was set at 5% type 1 error rate. Dry matter, protein, fat, and energy digestibility, and stool quality were similar between all diets (P &amp;gt; 0.05). Palatability was improved (P &amp;lt; 0.05) with inclusion of BSFL (i.e., preference for 5% and 10% vs. 0% inclusion) and improved (P &amp;lt; 0.05) at 10% vs. 5% inclusion. The BSFL tested showed merit as a palatable ingredient source that maintains stool quality and digestibility in adult dogs.

Strategy for Simple Control of High Performance PQ/PMMA Holographic Media.

Holographic data storage technology is a cost-effective solution for long-term archival data storage. However, the development of suitable holographic recording materials remains a challenge. Among these materials, phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) stands out due to its low cost and controllable thickness. Nevertheless, its limited photosensitivity and diffraction efficiency hinder its widespread application. In order to solve these problems, we put forward a kind of convenient and simple, low cost strategy, by adding plasticizer N,N-dimethylformamide (DMF) for preparation of DMF-PQ/PMMA photopolymer, avoid the use of complex compounds. The addition of DMF not only influences the thermal polymerization stage but also forms weak interactions with PQ during the photoreaction process, thereby enhancing the holographic performance of DMF-PQ/PMMA. Consequently, we achieved a remarkable 9.1-fold increase in photosensitivity (from ∼0.35 to 3.18 cm J-1), improved diffraction efficiency by 20% (from 65% to 80%), and reduced volume shrinkage by a factor of 8 (from 0.4% to 0.05%). Furthermore, utilizing a collinear holographic storage system with multiplexing shift at a scale of 5 μm resulted in an impressively low minimum bit error rate (BER) of only 0.36% (with an average BER of 1.4%), highlighting the fast processing capability and potential for low BER applications in holographic information storage using DMF-PQ/PMMA.

A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation

This paper is a study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation. It aims to develop a prediction model for the energy efficiency of electric vehicles and compare it with the results of chassis dynamometer tests. Driving tests were conducted on a chassis dynamometer, which included various test cycles of the 5-cycle test. Using MATLAB Simulink, a vehicle dynamics analysis model for electric vehicles was constructed, and analytical research was conducted focusing on light-duty electric vehicles. By comparing and analyzing the experimental data obtained from the chassis dynamometer with the data calculated through simulation, it is possible to verify the accuracy of the electric vehicle energy efficiency prediction model. And the reliability of the simulation model was secured by comparing the experimental data obtained through chassis dynamometer test with the simulation results of the developed electric vehicle energy efficiency prediction model. There is good agreement between the experimental and simulation results. In most areas, the error rate remains below 3 %, indicating that the simulation model closely follows the experimental results from the chassis dynamometer. Based on these results, it is expected that the developed simulation model can be utilized for measuring the energy efficiency of electric vehicles.

A CNN-based approach for anomaly detection in smart grid systems

Power grids are always a major center of attraction for attackers due to their large coverage area, complex technological infrastructure, remote access facilities, connectivity, and many more. The power grids that have bidirectional power flow, information interchange, and advanced communication technologies to improve the quality of power transmission and distribution are called smart grids. DoS attacks and false data injection attacks are the two major attacks on smart grids. This paper considers a false data injection attack considering noise reduction, state estimation, system parameters, and control system generation. Firstly, we measure the noise and mitigate it using the data matrix of the devices (sensors, generators, substations, and breaker systems). Secondly, state estimation uses the CNN to track the injected attack types. Finally, a control system is designed to provide anomaly patterns to the devices as input to prevent attacks in the future. The experimental results show that the proposed method achieves a higher anomaly detection rate with a lower error rate (approximately . 1.43%). The network performance parameters are providing significant results; throughput (12 Mbps), PDR (0.005%), execution time (1.43 s), and energy consumption are reduced by 12%–15% as compared to the existing research findings.

Detection of mecA-mediated methicillin resistance and evaluation of disk-diffusion antimicrobial susceptibility characteristics of Staphylococcus saprophyticus isolates from geographically diverse locations: Staphylococcus saprophyticus antibiotic susceptibility testing

Staphylococcus saprophyticus, a common uropathogen, is usually susceptible to urine-concentrating antimicrobials, so routine AST is not recommended by CLSI. Our study evaluated the antimicrobial resistance profiles of 277 S. saprophyticus isolates from North America and a globally diverse cohort. Notably, 24% (67/277) of our isolates come from non-urinary sources. AST was performed against 12 antimicrobials using standard disk diffusion, PCR for mecA and mecC, PBP2a production assays, and cefinase. 5% (13/277) of isolates were mecA positive and cefinase positive, 63% (176/277) were mecA negative but cefinase positive, 4% (11/277) were mecA positive but cefinase negative, and 28% (77/277) were mecA and cefinase negative. All (277/277) isolates were susceptible to delafloxacin, ciprofloxacin, rifampin, linezolid, and nitrofurantoin and 95% (262/277) were susceptible to trimethoprim-sulfamethoxazole. Our results showed that regardless of using CLSI or EUCAST breakpoints oxacillin had low categorical agreement for mecA presence, making it unsuitable for surrogate testing, while cefoxitin disk diffusion had high very major error rate. If possible, PBP2a or mecA testing is recommended for guiding therapy for non-urinary infections. Our work supports CLSI guidelines on routine susceptibility to urinary tract antibiotics.

Can GPT-3.5 generate and code discharge summaries?

The aim of this study was to investigate GPT-3.5 in generating and coding medical documents with International Classification of Diseases (ICD)-10 codes for data augmentation on low-resource labels. Employing GPT-3.5 we generated and coded 9606 discharge summaries based on lists of ICD-10 code descriptions of patients with infrequent (or generation) codes within the MIMIC-IV dataset. Combined with the baseline training set, this formed an augmented training set. Neural coding models were trained on baseline and augmented data and evaluated on an MIMIC-IV test set. We report micro- and macro-F1 scores on the full codeset, generation codes, and their families. Weak Hierarchical Confusion Matrices determined within-family and outside-of-family coding errors in the latter codesets. The coding performance of GPT-3.5 was evaluated on prompt-guided self-generated data and real MIMIC-IV data. Clinicians evaluated the clinical acceptability of the generated documents. Data augmentation results in slightly lower overall model performance but improves performance for the generation candidate codes and their families, including 1 absent from the baseline training data. Augmented models display lower out-of-family error rates. GPT-3.5 identifies ICD-10 codes by their prompted descriptions but underperforms on real data. Evaluators highlight the correctness of generated concepts while suffering in variety, supporting information, and narrative. While GPT-3.5 alone given our prompt setting is unsuitable for ICD-10 coding, it supports data augmentation for training neural models. Augmentation positively affects generation code families but mainly benefits codes with existing examples. Augmentation reduces out-of-family errors. Documents generated by GPT-3.5 state prompted concepts correctly but lack variety, and authenticity in narratives.