The neoclassical growth model is tested by use of panel DATA procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel DATA method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

In this paper, a new histogram-based method is introduced to make object detectors resistant to hostile attacks. In the following, this method was applied to two object detector models, YOLOV5 and FRCNN, and in this way, two models resistant to attacks were introduced. In order to verify the performance of the mentioned models, we performed the adversarial training process of these models with three targeted attacks TOG-vanishing, TOG-mislabeling, and TOG-fabrication and one untargeted attack, DAG. We have checked the efficiency of the introduced models on two DATA sets MSCOCO and PASCAL VOC, which are among the most famous DATA sets in the field of object recognition. The results show that this method, in addition to improving the adversarial ACCURACY, also improves the clean ACCURACY of the object detector models to some extent. The average clean ACCURACY of the YOLOv5-n model for the PASCAL VOC DATAset, if adversarial attacks are applied to it, in the case where no defense method is applied, is 85.5%, and in the case where the histogram method is applied, the average ACCURACY is equal to with 87.36%. In the YOLOv5-n model, according to the results, the best adversarial ACCURACY of this model, which has increased compared to other models, is in TOG-vanishing and TOG-fabrication attacks, which are 48% and 52.36%, respectively.

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process DATA. All sensor DATA from initial methods was sent to the central server for processing, raising concerns about sensitive DATA disclosure. The main concern was that all DATA from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize DATA transfer and convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model ACCURACY in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing DATA transfer and convergence time, leading to improved performance in face recognition ACCURACY. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.

DATA collection and storage has been facilitated by the growth in electronic services, and has led to recording vast amounts of personal information in public and private organizations DATAbases. These records often include sensitive personal information (such as income and diseases) and must be covered from others access. But in some cases, mining the DATA and extraction of knowledge from these valuable sources, creates the need for sharing them with other organizations. This would bring security challenges in user’ s privacy. The concept of privacy is described as sharing of information in a controlled way. In other words, it decides what type of personal information should be shared and which group or person can access and use it. “ Privacy preserving DATA publishing” is a solution to ensure secrecy of sensitive information in a DATA set, after publishing it in a hostile environment. This process aimed to hide sensitive information and keep published DATA suitable for knowledge discovery techniques. Grouping DATA set records is a broad approach to DATA anonymization. This technique prevents access to sensitive attributes of a specific record by eliminating the distinction between a number of DATA set records. So far a large number of DATA publishing models and techniques have been proposed but their utility is of concern when a high privacy requirement is needed. The main goal of this paper to present a technique to improve the privacy and performance DATA publishing techniques. In this work first we review previous techniques of privacy preserving DATA publishing and then we present an efficient anonymization method which its goal is to conserve ACCURACY of classification on anonymized DATA. The attack model of this work is based on an adversary inferring a sensitive value in a published DATA set to as high as that of an inference based on public knowledge. Our privacy model and technique uses a decision tree to prevent publishing of information that removing them provides privacy and has little effect on utility of output DATA. The presented idea of this paper is an extension of the work presented in [20]. Experimental results show that classifiers trained on the transformed DATA set achieving similar ACCURACY as the ones trained on the original DATA set.

Background: We aimed to evaluate completeness and ACCURACY of the Golestan Death Registry (GDR) to identify cancer-related causes of death (CCoD). Methods: The GDR DATA (2004-2015) were compared with cancer DATA collected from clinical/pathological sources (the considered gold standard) by the Golestan Population-Based Cancer Registry (GPCR). Using a linkage method, matched cases, including subjects with CCoD and those with ill-defined cause of death (ICoD) (garbage codes), were identified and entered into the final analysis as study subjects. The completeness (percentage of study subjects with CCoD) and ACCURACY (number of subjects with correct CoD from the total number of study subjects) of the GDR were calculated. Results: In total, 3, 766 matched cases were enrolled. Overall, the completeness and ACCURACY of the GDR for identifying CCoD were 92. 7% and 53. 2%, respectively. There were variations by cancer site and age group, with completeness and ACCURACY highest for brain cancer (96. 3%) and leukaemia (79. 8%) while the lowest ACCURACY was observed for colorectal cancer (29. 9%). The completeness and ACCURACY of GDR was higher in patients aged under 60 years (95. 7% and 53. 6%, respectively). We also found higher completeness (93. 7%) and ACCURACY (55. 8%) in residents of rural areas. Conclusion: Linkage of death registry DATA with cancer registry DATA can be a significant resource for evaluating quality of the death registry DATA. Our findings suggested that completeness of the GDR for identifying CCoD is reasonable, but ACCURACY is relatively low. Access to clinical and pathological DATA from other sources and enhanced training of death certifiers can improve the present situation.

Transformers are critical components in power systems. Faults within these devices can lead to substantial repair costs and prolonged service interruptions. Dissolved Gas Analysis (DGA) of transformer oil is widely used for monitoring transformer health. This research leverages DATA-driven algorithms, employing the Duval-Pentagon (DP) method and hyperparameter optimization, to enhance fault diagnosis ACCURACY in power transformers. After preprocessing the DGA DATAset, it was split into training and testing sets in an 80: 20 ratio. Subsequently, several DATA-driven algorithms, including Support Vector Machines Algorithm (SVMA), Decision Trees Algorithm (DTA), Logistic Regression Algorithm (LRA), and Naive Bayes Algorithm (NBA), were employed on the DATAset. To further improve fault diagnosis ACCURACY, a hyperparameter optimization technique was implemented by leveraging random search. Evaluation metrics such as ACCURACY, F1-measure, recall, precision, and Matthews Correlation Coefficient (MCC) were used to assess impact of hyperparameter optimization. The findings demonstrate that hyperparameter optimization consistently enhances the performance of DATA-driven algorithms. Among the algorithms proposed in this research, DTA with hyperparameter optimization achieved the highest ACCURACY with an ACCURACY rate of 93. 37% in transformer fault diagnosis. The algorithms were implemented based on Python.

The acquisition of reliable flow velocity and streamflow estimates is vital and essential in aquatic studies. Acoustic Tomography Technology is a branch of remote sensing science which innovatively developed for continuous monitoring of surface water currents in oceans, seas, and in recent years in rivers and is a promising method to measure Flow characteristics such as velocity & discharge with high ACCURACY and continuously in time. The output of this system impressed by the influence of unknown factors and after the initial processing of raw DATA, some spikes appear in the DATA. Although the developers of this system have stated that a source of spurious DATA can be a complex salinity distribution in estuarine regions, failure to identify these outliers will cause errors in measurements and increase the error of DATA mining and time series forecasting algorithms. In the previous studies, the spikes removed using the standard deviation method without any replacement. In this study, Phase-Space Thresholding (PST) is proposed to detect and remove the spikes, which was developed for despiking output of Acoustic Doppler Velocimeter (ADV) DATA. This method combines three concepts: 1) the differentiation enhances the high-frequency portion of a signal, 2) the expected maximum of a normal, random series is given by the universal threshold, and 3) the good DATA cluster in a dense cloud in phase space. These concepts are used to construct an ellipsoid in a three-dimensional phase-space, then points which lie outside the threshold ellipsoid are designated as spikes. An important advantage of this method in comparison with various other methods is that it requires no parameters. Furthermore, another advantage of this method against the standard deviation method is the replacement of detected spikes with a reliable value. for replacement of detected spike’s values, we used the mean value of two adjacent DATA points on either side of the detected spike. After 6 iterations of implementing the PST method on the input DATAset a total of 8017 DATA, which is 32% of 25031 DATA were identified as spikes and replaced with a correct value. Moreover, the standard deviation value before despiking was 0/206 and after applying the PST method improves to 0/119. This change in standard deviation value shows that the DATA dispersion around signal mean reduces due to despiking process. The results show that the PST method has higher ACCURACY in comparison with the standard deviation approach. Finally, it was observed that the comparison of the relative discharge error between the output of the PST method and the rating curve DATA (as a reference) is almost less than 20%. While this value exceeds 50% in the comparison between the standard deviation and the rating curve DATA.

Due to the spread of the Internet and its pervasiveness, ``big DATA" is created daily. Processing this amount of DATA requires a system with high processing power. In fact, the production and collection of DATA from a wide range of different equipment and tools lead to the creation of large-scale DATAbases. In dealing with large and unstructured DATAbases and their management, there are always challenges. This study aims to present a model to increase the clustering ACCURACY of big DATA using a fuzzy clustering system based on DATA mining in a MatLab programming environment. For this purpose, first, the importance of each variable in the decision tree models in SPSSModeler software is determined, then with the help of these results, fuzzy rules are explained and a fuzzy inference system is formed in MATLAB software. This study uses DATA mining techniques such as C\&R Tree, Chaid and C5.0 to study the development of the FCM method to increase clustering ACCURACY in high volume DATA and related factors such as DATA preparation indicators, DATA type DATA quality, DATA dimensions, DATA volume and number of clusters were evaluated as inputs and clustering ACCURACY index was evaluated as output. Then, with the help of these results, the rules of forming a fuzzy inference system were determined and by explaining the membership functions of the decision model, it showed what effect each input index has on the output index.