جستجوی مقالات مرتبط با کلیدواژه « benchmarking » در نشریات گروه « ریاضی »

Data envelopment analysis is one of the best methods to evaluate the performance of decision-making units. This method is also used for benchmarking. benchmarking is a tool to evaluate organizational performance with a learning approach from others, it is also one of the practical methods in continuous improvement of the benchmarking method. The importance of benchmarking in all industries is clear. This paper considers the after-sales service network of an automobile company in Iran to evaluate the model. According to the structure of this network, a hierarchical structure is considered for benchmarking. In this paper, the purpose is to provide a model for benchmarking decision-making units with hierarchical structure and dependent parameters. In the real world, most decision-making units have a hierarchical structure and this structure needs more attention by researchers also dependent parameters can have a high impact on benchmarking. The proposed model for the after-sales service network of an automobile company in Iran was implemented and the results show the high impact of dependent variables on benchmarking and has increased modeling accuracy. The accuracy of benchmarking is very important for the success of decision-making units and the results show that paying attention to the relationships between the parameters increases the accuracy of benchmarking and according to the proposed model, more accurate benchmarking can be achieved.

Human Activity Recognition (HAR) systems used in healthcare have attracted much attention in recent years. A HAR system consists of a wearable device with sensors. HAR has been used to suggest several machine learning (ML) algorithms. However, only a few research have looked at how to evaluate HAR to identify physical activities. Nevertheless, obtaining an explanation for their performances is complicated by two factors: the lack of implementation specifics and the lack of a baseline evaluation setup that makes comparisons unfair. For establishing effective and efficient ML–HAR of computers and networks, this study uses ten common unsupervised and supervised ML algorithms. The decision tree (DT), artificial neural network (ANN), naive Bayes (NB), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and XGBoost (XGB) algorithms are among the supervised ML algorithms, while the k-means, expectation-maximization (EM), and self-organizing maps (SOM) algorithms are among the unsupervised ML algorithms. Multiple algorithms models are presented, and the turning and training parameters in ML (DT, ANN, NB, KNN, SVM, RF, XGB) of each method are investigated in order to obtain the best classifier assessment. Differ from earlier research, this research measures the true negative and positive rates, precision, accuracy, F-Score as well as recall of 81 ML-HAR models to assess their performance. Because time complexity is a significant element in HAR, the ML-HAR models training and testing time are also taken into account when evaluating their performance efficiency. The mobile health care (M\_HEALTH CARE) dataset, which includes real-world network activity, is used to test the ML-HAR models. In general, the XGB outperforms the DT-HAR, k-NN-HAR, and NB-HAR models in recognizing human activities, with recall, precision, and f-scores of 0.99, 0.99, and 0.99 for each, respectively, for health care mobile recognition.

Benchmarking is a systematic way by which organizations can measure and modify their activities based on the best industry or organization. The purpose of this study is to rank and evaluate the performance of automotive and parts manufacturing companies presented in the Tehran Stock Exchange using data envelopment analysis and also to benchmark inefficient companies-years. Practical results are also provided based on mathematical models to find the position of automotive and parts manufacturing companies compared to each other and to maximize their performance. The components of companies' intellectual capital, including human capital, structural capital, capital employed and innovative capital are considered as inputs. Also, return on assets, return on equity, return on sales and net profit per share are deemed as outputs which are extracted from the financial statements of the sample companies during the ten years 2010-2019. The performance of the units is evaluated using the output-oriented CCR model. 26 out of 280 member-year sample companies were determined as efficient. Then, the Anderson-Peterson model is used to rank them. Also, benchmarking for inefficient units are done applying the envelopment form and GAMS software, which can be used to observe the behavior of inefficient companies, in order to target to improve organizational performance.