This paper addresses the issue of cell loss and its consequent effect on video quality in a packet video system, and examines possible compensative measures. In the system’s encoder, adaptive spectral separation is used to develop a two-layer Coding scheme comprising a high priority layer to carry essential video data and a low priority layer with data to enhance the video image. A two-step error detection scheme using cell numbering and variable length decoder is incorporated in the system to detect cell loss in the bit-stream without long mis-interpretation. In the system’s decoder, cell loss is compensated by a two-step recovery procedure based on temporal error concealment to maintain high picture quality. The simulated performance of the proposed two-layer system is studied and compared with that of the single layer system. It is shown that good resilience and PSNR improvement can be achieved even at quite high error rates. Statistics such as the cell-rate distribution and autocorrelation function of both high and low priority streams generated by the adaptive system are analyzed. It is observed that two different classes of gamma function can represent the cell distribution in each layer. A multiplicative - ARIMA (autoregressive integrated moving average) model is proposed to represent and forecast the number of cells per frame for the encoded video traffic and its goodness-of-fit is compared with AR(1) (autoregressive) model. The cells per frame measured from a sample video sequence compare favorably with those obtained by the ARIMA model. Simulations were performed using three five-second video sequences to demonstrate the efficiency of the two-layer enCoding algorithm for sequences with different characteristics. Statistics gathered from these prioritization schemes are claimed to be useful for network design and resource management.

 Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using error Correcting Output Coding (ECOC) ensemble method. Furthermore, the feature selection is used to reduce the costs of errors in our proposed method. ECOC is a method for decomposing a multi-way classification problem into many binary classification tasks; and then combining the results of the subtasks into a hypothesized solution to the original problem. Firstly, the image features are extracted by Principal Components Analysis (PCA). After that, ECOC is used for identification the Persian handwritten letters which it uses Support Vector Machine (SVM) as the base classifier. The empirical results of applying this ensemble method using 10 real-world data sets of Persian handwritten letters indicate that this method has better results in identifying the Persian handwritten letters than other ensemble methods and also single classifications. Moreover, by testing a number of different features, this paper found that we can reduce the additional cost in feature selection stage by using this method.

In this paper, we develop a new error-resistant secure lossless source Coding scheme for discrete sources. Recent results indicate that the same turbo principle that provides sub-optimal strategy for channel Coding, can be used to obtain efficient source Coding schemes. We extend the source turbo-Coding idea to include security for transmission of the compressed data and also to ensure the lossless recovery of information at the receiver. Compression and security are achieved by adapting the random puncturing strategy to the statistics of the source. Lossless compression is guaranteed by finely puncturing the encoded data of a parallel-concatenated turbo code while verifying the integrity of the source information at the source encoder. Simulation results show that the proposed scheme can obtain a compression rate close to the source entropy. For the same block length, this scheme yields better compression rates in comparison with the Lempel-Ziv universal source Coding. The proposed scheme has a robust and error-resistant performance over noisy channels for Eb/N0>5 dB.

Domain adaptation can transfer knowledge from a training set (source domain) to a test set (target domain), promoting the performance of the model learned from the training set. In addition, sparse Coding makes the learned model more succinct and easy to manipulate. However, the existence of the distribution mismatch across the source and target domains reduce the performance of model. In this paper, we propose an unsupervised domain adaptation model to minimize the prediction error of image classification. Sample reweighting is utilized to handle redundant and useless information of source data in the new representation. Moreover, the difference of the conditional distributions across the source and target domains is reduced along with the subspace alignment. Our proposed approach learns a sparse domain-invariant classifier in a latent subspace with preserving the structure of the input data. Extensive experiments demonstrate that our proposed approach shows 4. 49% improvement in classification accuracy on real-world datasets compared to state-of-the-art machine learning and domain adaptation methods.

Background: Coding errors lead to incorrect classification of diseases and thus severely affect health care planning and epidemiological research. Objectives: This study aimed to investigate the factors affecting clinical Coding errors. Methods: This descriptive study was conducted in 2020 in 2 stages. In the first stage, a questionnaire was developed by examining relevant resources, documents, and articles. The questionnaire’, s validity was examined by 5 health information management experts. Its reliability was determined using Cronbach,and correlation coefficient (r = 0. 84%). In the second stage, the data were collected by visiting teaching hospitals affiliated with Shahid Beheshti University of Medical Sciences, examining the medical records, and interviewing the coders. Data were analyzed using descriptive statistics. Results: Non-observance of diagnostic principles by physicians, illegibility of medical records, use of ambiguous and nonstandard abbreviations, and incomplete medical documentation were the most important causes of Coding errors. Conclusions: Considering the importance of Coding in presenting correct data as a powerful lever in health care, knowledge of the factors affecting the occurrence of Coding errors will greatly contribute to the selection of effective strategies to reduce and eliminate errors.

Background: During the COVID-19 pandemic, disease Coding significantly influenced national and international strategies for prevention, treatment, and control. This study aimed to assess the factors influencing COVID-19 records' Coding errors in teaching hospitals in South Khorasan province. Materials and Methods: In 2022, researchers conducted a cross-sectional study in South Khorasan Province, Iran. Data was gathered using a self-developed questionnaire distributed among medical coders in all hospitals. Descriptive statistics were employed in the data analysis using SPSS version 19. Results: The study's results showed that non-observance of diagnostic principles by physicians (66 out of 92), a lack of specialized medical Coding specialists (52 out of 92), and the use of ambiguous and non-standard abbreviations (51 out of 92) were the most significant factors affecting COVID-19 Coding errors. Among the three main factors affecting COVID-19 records' Coding errors, factors related to the medical coder (47.66 out of 92), factors related to healthcare providers (29.8 out of 92), and organizational and environmental factors (21.4 out of 92) were the most significant. Conclusion: This study revealed that multiple factors contribute to Coding errors in COVID-19 records, with the most critical being physicians' non-adherence to diagnostic principles, the shortage of medical Coding specialists, the use of unclear and non-standard abbreviations, and the absence of COVID-19 Coding continuing education. Therefore, we recommend conducting workshops on diagnosis and Coding for medical coders, particularly emphasizing the precise Coding of COVID-19 records.