جستجوی مقالات مرتبط با کلیدواژه "machinelearning" در نشریات گروه "فنی و مهندسی"

In this paper, the problem of finding profitable pairs by automatically limiting the search space of pairs using machine learning techniques and integrating an unsupervised learning algorithm, OPTICS, to pair identification and selection in pair trading is discussed. In addition, to optimize the portfolio consisting of pairs of assets and allocate optimal capital to them, a genetic-based algorithm to increase the Sharpe ratio is used. The proposed technique for automatic clustering is better than the conventional methods of searching for pairs of assets used by investors and leads to a higher average rate of return on investment and a higher Sharpe ratio for portfolios in trading using selected pairs of clusters. These calculated evaluation criteria for the portfolio were improved after using a bi-objective optimization genetic algorithm. This study was simulated using intraday price data of a group of stocks in the Tehran Stock Exchange between the years 2015 to 2020 and taking into account the transaction costs.

Today, maintenance and repair have become very important in the manufacturing industry. An ecient solution to prevent downtime is to predict equipment failure. Therefore, accurate and correct prediction of breakdown events in the eld of predictive maintenance can be very useful. In general, each prediction will be accompanied by a certain amount of error, which in various ways tries to control this error or limit it to a reasonable amount. In this thesis, a framework has been proposed that species when the system under review will need maintenance and repairs to prevent downtime as much as possible. Therefore, the main purpose of this thesis is to design and implement an ecient combination structure to accurately predict failure events using both standard statistical standard models and machine learning in predictive maintenance. The literature review results indicate that the use of these methods in recent years has led to extensive advances in the eld of providing accurate forecasts and subsequently improved the level of decisions made by managers and decisionmakers. The proposed model is used to predict failure events in benchmark data related to the truck air pressure system. Finally, the performance of the proposed model is compared with other data-driven techniques individually and in combination, which includes logit models, support vector machines, and multilayer perceptron neural networks. According to the numerical values obtained from the nal analysis, the results indicate that the backup vector machine model has higher prediction accuracy than other single models, and also the results indicate the eciency and eectiveness of the proposed parallel combination structure compared to the use of models individually and in series combination in modeling and forecasting issues. The parallel hybrid model improved the accuracy of predictions by an average of 11% in test data and 7% in training data. Therefore, due to the greater accuracy in combining classical statistical models and machine learning in parallel, the use of this combined method to improve the accuracy of predictions in the eld of predictive maintenance is recommended for future studies.

Although the lack of data is one of the important challenges for research in the field of natural language processing, but this challenge is more acute in the Persian language, so finding a high-quality and comprehensive dataset in the Persian language is a difficult task. In addition to that, having some problems such as the ability to categorize and not complying with the storage standard are among the problems of the existing datasets, each of which can affect the learning rate of the model, the results, and the error rate in the experiments. For this reason, all these reasons made us seek to collect and prepare a dataset that covers all such problems and reduces the amount of error when using data in different models. In this research, we have designed and used a crawler to collect textual data. By crawling on one of the news bases, it has been able to collect data sets in five columns: title, summary, text, tag, and publication date. The textual data has been normalized with the help of one of the Persian language libraries in the Python programming language and stored in csv and xml formats and made available to fellow researchers. The tags in this dataset include 13 main tags of sports, art and media, culture, science and progress, political, foreign policy, life, family, society, education and training, international, economic and provinces. Among the tasks that can be done on this data set are text classification, text extraction, text summarization and title recognition. Also, one of the prominent features of this data set is its comprehensiveness, the amount of suitable data, the existence of useful features, having unique features, as well as storage in a standard format. This dataset is a product of the Language Processing Department of Imam Hossein Comprehensive University and can be downloaded and used through the link mentioned in the footnote of the next page and with respect to copyright.

Wireless telecommunication network process mining, key enablers, emerging machine learning schemes, communication technologies, networking technologies and computing technologies are presented in this paper to optimize the performance of telecommunication operators. Process mining is a combination of technology and methods whose implementation leads to optimal process management in communication and telecommunication networks. The main purpose of this article is process mining and analysis of the current state of processes and possible problems in the fifth and sixth generation wireless networks. The next generation telecommunication networking process consists of two main stages: the first stage is process prioritization and selection, in which the telecommunication network goals for improvement are identified and the desired part to create business value is identified and the effect of macro processes on Value creation is examined and the second step is to obtain and record the information of the process that is intended to be improved and then to create the desired process model in the telecom operators.

In recent years, the tendency for ransomware-based cyberattacks has increased dramatically. One of the defensive methods is the behavioral detection of the ransomware by system functions. Literature review and related studies and investigations in this field show that these researches are not optimum concerning the accuracy and speed of ransomware detection. Because all datasets used in these studies are limited in scope, they have shortcomings such as high false positive or false negative rates and even high indiscriminate rates. Another drawback of these schemes is the failure to expedite the debate on extortion ransom. Therefore, in this study, the first step is to generate an initial dataset with 126 attributes containing all types of ransomware families. Then, by performing 4-step experiments and tests and applying a feature selection algorithm, this initial set is processed and optimized and reduced to a dataset with 67 attributes without loss of detection precision. In the final step, by providing an optimal and so-called lightweight dataset, the best classification model for the detection of ransomware is obtained being capable of identifying ransomwares with an optimum precision rate of 95.11 in 0.21 seconds, a false positive rate of 0.047 and a true positive rate of 0.951 by using a random forest classification algorithm (using 10-part cross-validation method)