The development of Network Intrusion Detection Systems (NIDS) requires labeled network traffic, especially to train and evaluate machine learning approaches. Besides the recording of traffic, the ...generation of traffic via generative models is a promising approach to obtain vast amounts of labeled data. There exist various machine learning approaches for data generation, but the assessment of the data quality is complex and not standardized. The lack of common quality criteria complicates the comparison of synthetic data generation approaches and synthetic data.
Our work addresses this gap in multiple steps. Firstly, we review and categorize existing approaches for evaluating synthetic data in the network traffic domain and other data domains as well. Secondly, based on our review, we compile a setup of metrics that are suitable for the NetFlow domain, which we aggregate into two metrics Data Dissimilarity Score and Domain Dissimilarity Score. Thirdly, we evaluate the proposed metrics on real world data sets, to demonstrate their ability to distinguish between samples from different data sets. As a final step, we conduct a case study to demonstrate the application of the metrics for the evaluation of synthetic data. We calculate the metrics on samples from real NetFlow data sets to define an upper and lower bound for inter- and intra-data set similarity scores. Afterward, we generate synthetic data via Generative Adversarial Network (GAN) and Generative Pre-trained Transformer 2 (GPT-2) and apply the metrics to these synthetic data and incorporate these lower bound baseline results to obtain an objective benchmark. The application of the benchmarking process is demonstrated on three NetFlow benchmark data sets, NF-CSE-CIC-IDS2018, NF-ToN-IoT and NF-UNSW-NB15. Our demonstration indicates that this benchmark framework captures the differences in similarity between real world data and synthetic data of varying quality well, and can therefore be used to assess the quality of generated synthetic data.
Psoriasis is an immune-mediated skin disease affecting approximately 3% of the global population. Proper management of this condition necessitates the assessment of the Body Surface Area (BSA) and ...the involvement of nails and joints. Recently, the integration of Natural Language Processing (NLP) with Electronic Medical Records (EMRs) has shown promise in advancing disease classification and research. This study evaluates the performance of ChatGPT-4, a commercial AI platform, in analyzing unstructured EMR data of psoriasis patients, particularly in identifying affected body areas.
The study analyzed EMR data from 94 patients treated at the Dermatology Department and Psoriasis Outpatient Clinic of Sheba Medical Center between 2008 and 2022. The data were processed using the ChatGPT-4 interface to identify and report the body areas affected by psoriasis. These identified areas were then categorized, and the accuracy of ChatGPT-4′s analysis was compared with that of a senior dermatologist.
The results revealed that the dermatologist identified 477 psoriasis-affected body areas. ChatGPT-4 accurately recognized 443 (92.8%) of these areas, missed 34, and incorrectly identified 30 areas as affected. From 94 cases, nail involvement was detected in 32 cases (34.0%), with ChatGPT-4 correctly identifying 29 cases. Joint involvement was noted in 25 cases (26.6%), with 24 correctly identified by ChatGPT-4. Complete accuracy was achieved in 54 cases (57.4%), while inaccuracies were observed in 40 cases (42.6%). We found that cases with more characters, words, or identified body areas were more prone to errors, suggesting that increased data complexity heightens the likelihood of inaccuracies in AI analysis.
ChatGPT-4 demonstrated a high performance in analyzing detailed and complex unstructured EMR data from psoriasis patients, effectively identifying involved body areas, including nails and joints. This highlights the potential of NLP algorithms in enhancing the analysis of unstructured EMR data for both clinical follow-up and research purposes.
•The use of generative pre-trained transformer (GPT)-based models has the potential to revolutionize radiology at large, offering new possibilities for improving accuracy, efficiency, and patient ...outcomes.•Current applications of GPT-based models in radiology include report generation, educational support, clinical decision support, patient communication, and data analysis.•Innovative applications of GPT-based models will be developed in the field of radiology, further enhancing the role of technology in the diagnostic process.•Similar to other applications of artificial intelligence in the field of imaging, further formal validation of ChatGP is required.
Artificial intelligence has demonstrated utility and is increasingly being used in the field of radiology. The use of generative pre-trained transformer (GPT)-based models has the potential to revolutionize the field of radiology, offering new possibilities for improving accuracy, efficiency, and patient outcome. Current applications of GPT-based models in radiology include report generation, educational support, clinical decision support, patient communication, and data analysis. As these models continue to advance and improve, it is likely that more innovative uses for GPT-based models in the field of radiology at large will be developed, further enhancing the role of technology in the diagnostic process. ChatGPT is a variant of GPT that is specifically fine-tuned for conversational language understanding and generation. This article reports some answers provided by ChatGPT to various questions that radiologists may have regarding ChatGPT and identifies the potential benefits ChatGPT may offer in their daily practice but also current limitations. Similar to other applications of artificial intelligence in the field of imaging, further formal validation of ChatGPT is required.
Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input.
This study aimed to ...evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability.
We used 2 sets of multiple-choice questions to evaluate ChatGPT's performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT's performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question.
Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT's answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001) and NBME-Free-Step2 (P=.001) data sets, respectively.
ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT's capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning.
This study was aimed to study the effect of thyroid hormones on some biochemical tests of liver function in Iraqi male patients and to study the relationship between them. A controlled study included ...135 samples from patients and controls, group B, 45 patients with a liver disorder, and group C: 45 patients with a thyroid disorder, and group A: 45 healthy people (as the controls group). The study concluded that there were significant statistically significant differences for patients with liver disease, as well as for patients who suffer from abnormalities in the functions of the thyroid gland. For triiodothyronine (T3) and thyroxine (T4), there was clear importance and a slight impact for patients with liver disease. Because of the defect in the liver enzymes, this led to an increase in the TSB percentage, which increased significantly. Alkaline and Albumin levels indicate statistical significance within the results of our study. Serum protein levels had no significant changes in our study.
The GlutoPeak®-Test (GPT) as a rapid small-scale technique was optimized to evaluate the gluten aggregation properties and to predict the loaf volume, on the basis of a multiyear and multilocation ...analysis of wheat samples, using different solvents. 5 % lactic acid and 1 % sodium chloride displayed significant GPT responses. Relationships between protein content, sedimentation value, GPT parameters and loaf volume were investigated. With 1 % sodium chloride, the torque 15 s before maximum torque (AM) presented the highest correlation with loaf volume of samples from 2013 to 2014 (r = 0.77, r = 0.63, p < 0.001, respectively). A multiple regression analysis indicated that the best prediction of loaf volume was a linear function of protein content and AM, explaining the variation in loaf volume by 63 % and providing an uncertainty of ±39 ml. The accuracy of the validation of the linear function leads to 64 % correct and to 36 % incorrect predictions of the loaf volume. This emphasizes that the application of the linear function of protein content and AM cannot replace the actual measurement of loaf volume, but it could be a useful rapid screening test in breeding for improved baking quality in bread wheat.
•GlutoPeak®-Test (GPT) displays in relation to loaf volume a significant response.•The optimized method exhibits stability over different environments.•The influence of years is of more importance than locations.•The best prediction of loaf volume is a linear function of protein content and AM.•GPT is a screening alternative to the labor-intensive quantitation of loaf volume.
Giardiasis is a well-known gastroenteritis that causes small intestine malabsorption and fatty diarrhea. The current study was performed from the start of September 2021 to the end of March 2022. ...Fifty blood samples were collected from suspected infected patients with G. lamblia when they returned to the laboratory to receive the result of microscopic analysis and were proved to be infected with the parasite, and (40) blood samples were collected from healthy people as a control group. This study reveals a significant increase in the level of interleukin-1B, being (2.807) in the infected patients. Higher than in the control group (0.454). The study of biochemical parameters of patients infected with parasites showed an increase in the concentration of liver enzymes (GOT and GPT) in patients and compared to the healthy group
