Fractional Differential Equations

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Deep Learning in Medical Image Analysis

In this chapter, fractional calculus is introduced. After a brief overview about this topic, some basic definitions and properties are presented. Moreover, some geometrical and physical interpretations of fractional operators are described, both in time and frequency domain.

Introduction to Fractional Calculus

Context Software defect prediction is important to ensure the quality of software. Nowadays, many supervised learning techniques have been applied to identify defective instances (e.g., methods, classes, and modules). Objective However, the performance of these supervised learning techniques are still far from satisfactory, and it will be important to design more advanced techniques to improve the performance of defect prediction models. Method We propose a new deep forest model to build the defect prediction model (DPDF). This model can identify more important defect features by using a new cascade strategy, which transforms random forest classifiers into a layer-by-layer structure. This design takes full advantage of ensemble learning and deep learning. Results We evaluate our approach on 25 open source projects from four public datasets (i.e., NASA, PROMISE, AEEEM and Relink). Experimental results show that our approach increases AUC value by 5% compared with the best traditional machine learning algorithms. Conclusion The deep strategy in DPDF is effective for software defect prediction.

Improving defect prediction with deep forest

There are many recent investigations on chaotic systems with self-excited and hidden attractors. In this paper we introduce a chaotic system in which the attractor can change between hidden and self-excited attractor depending on the value of parameters. Dynamical properties of the proposed attractor are investigated. The attractor is then realized using off the shelf components. Also, for this new system electronic circuit is implemented and FPGA-based chaotic oscillator is designed. In the end, the fractional- order form is examined through bifurcation and stability analysis.

Chaotic chameleon: Dynamic analyses, circuit implementation, FPGA design and fractional-order form with basic analyses

Mfrlmo: Model-Free Reinforcement Learning for Multi-Objective Optimization of Apache Spark

Although there exist various machine learning and text mining techniques to identify the programming language of complete code files, multi-label code snippet prediction was not considered by the research community. This work aims at devising a tuner for multi-label programming language prediction of stack overflow posts. To that end, a Hyper Source Code Classifier (HyperSCC) is devised along with rule-based automatic labeling by considering the bottlenecks of multi-label classification. The proposed method is evaluated on seven multi-label predictors to conduct an extensive analysis. The method is further compared with the three competitive alternatives in terms of one-label programming language prediction. HyperSCC outperformed the other methods in terms of the F1 score. Preprocessing results in a high reduction (50%) of training time when ensemble multi-label predictors are employed. In one-label programming language prediction, Gradient Boosting Machine (gbm) yields the highest accuracy (0.99) in predicting R posts that have a lot of distinctive words determining labels. The findings support the hypothesis that multi-label predictors can be strengthened with sophisticated feature selection and labeling approaches.

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Developing a hyperparameter optimization method for classification of code snippets and questions of stack overflow: HyperSCC

In this article, which has an important role in the regulation of the flow of traffic in computer networks is discussed queue management. Queue management algorithms are widely used today introduced the working principles are described briefly. NS-3 network simulator program was used for queue management algorithms comparison of performance. On NS-3 network simulator, DROP TAIL and RED queue management algorithms have been tested on various topologies and performance evaluations were made. Furthermore, the studies are compared with results obtained previously. The data obtained from these tests and studies those evaluated, RED algorithms that use probabilistic methods and threshold values have been found to be more successful.

https://www.saujs.sakarya.edu.tr/tr/download/article-file/192647

NS-3 Ağ Simülatöründe, Kuyruk Yönetim Algoritmalarının Performans Analizi

Deep learning algorithms vary depending on the underlying connection mechanism of nodes of them. They have various hyperparameters that are either set via specific algorithms or randomly chosen. Meanwhile, hyperparameters of deep learning algorithms have the potential to help enhance the performance of the machine learning tasks. In this paper, a tuning guideline is provided for researchers who cope with issues originated from hyperparameters of deep learning models. To that end, four types of deep learning algorithms are investigated in terms of tuning and data mining perspective. Further, common search methods of hyperparameters are evaluated on four deep learning algorithms. Normalization helps increase the performance of classification, according to the results of this study. The number of features has not contributed to the decline in the accuracy of deep learning algorithms. Even though high sparsity results in low accuracy, a uniform distribution is much more crucial to reach reliable results in terms of data mining.

Muhammed Maruf Öztürk

Papers

Mfrlmo: Model-Free Reinforcement Learning for Multi-Objective Optimization of Apache Spark

Developing a hyperparameter optimization method for classification of code snippets and questions of stack overflow: HyperSCC

NS-3 Ağ Simülatöründe, Kuyruk Yönetim Algoritmalarının Performans Analizi

On tuning deep learning models: a data mining perspective.