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Full-Text Articles in Physical Sciences and Mathematics
Improving Anomaly Detection In Bgp Time-Series Data By New Guide Features And Moderated Feature Selection Algorithm, Mahmoud Hashem, Ahmed Bashandy, Samir Shaheen
Improving Anomaly Detection In Bgp Time-Series Data By New Guide Features And Moderated Feature Selection Algorithm, Mahmoud Hashem, Ahmed Bashandy, Samir Shaheen
Turkish Journal of Electrical Engineering and Computer Sciences
The Internet infrastructure relies on the Border Gateway Protocol (BGP) to provide essential routing information where abnormal routing behavior impairs global Internet connectivity and stability. Hence, employing anomaly detection algorithms is important for improving the performance of BGP routing protocol. In this paper, we propose two algorithms; the first is the guide feature generator (GFG), which generates guide features from traditional features in BGP time-series data using moving regression in combination with smoothed moving average. The second is a modified random forest feature selection algorithm which is employed to automatically select the most dominant features (ASMDF). Our mechanism shows that …
Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli
Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli
Turkish Journal of Electrical Engineering and Computer Sciences
Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (GridSearch), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer …
Cloud-Supported Machine Learning System For Context-Aware Adaptive M-Learning, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq
Cloud-Supported Machine Learning System For Context-Aware Adaptive M-Learning, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq
Turkish Journal of Electrical Engineering and Computer Sciences
It is a knotty task to amicably identify the sporadically changing real-world context information of a learner during M-learning processes. Contextual information varies greatly during the learning process. Contextual information that affects the learner during a learning process includes background knowledge, learning time, learning location, and environmental situation. The computer programming skills of learners improve rapidly if they are encouraged to solve real-world programming problems. It is important to guide learners based on their contextual information in order to maximize their learning performance. In this paper, we proposed a cloud-supported machine learning system (CSMLS), which assists learners in learning practical …
Towards Wearable Blood Pressure Measurement Systems From Biosignals: A Review, Ümi̇t Şentürk, Kemal Polat, İbrahi̇m Yücedağ
Towards Wearable Blood Pressure Measurement Systems From Biosignals: A Review, Ümi̇t Şentürk, Kemal Polat, İbrahi̇m Yücedağ
Turkish Journal of Electrical Engineering and Computer Sciences
Blood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, is the cause of nearly 13 % of mortality all over the world. Blood pressure is not only measured in the medical environment, but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systems with low error rates have been developed besides the new technologies and algorithms. Blood pressure measurements are differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurement methods. Although IBP measurement provides the most accurate …
A Hybrid Feature-Selection Approach For Finding The Digital Evidence Of Web Application Attacks, Mohammed Babiker, Eni̇s Karaarslan, Yaşar Hoşcan
A Hybrid Feature-Selection Approach For Finding The Digital Evidence Of Web Application Attacks, Mohammed Babiker, Eni̇s Karaarslan, Yaşar Hoşcan
Turkish Journal of Electrical Engineering and Computer Sciences
The most critical challenge of web attack forensic investigations is the sheer amount of data and level of complexity. Machine learning technology might be an efficient solution for web attack analysis and investigation. Consequently, machine learning applications have been applied in various areas of information security and digital forensics, and have improved over time. Moreover, feature selection is a crucial step in machine learning; in fact, selecting an optimal feature subset could enhance the accuracy and performance of the predictive model. To date, there has not been an adequate approach to select optimal features for the evidence of web attack. …
Energy Saving Scheduling In A Fog-Based Iot Application By Bayesian Task Classification Approach, Gholamreza Heydari, Dadmehr Rahbari, Mohsen Nickray
Energy Saving Scheduling In A Fog-Based Iot Application By Bayesian Task Classification Approach, Gholamreza Heydari, Dadmehr Rahbari, Mohsen Nickray
Turkish Journal of Electrical Engineering and Computer Sciences
The Internet of things increases information volume in computer networks and the concept of fog will help us to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem. This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain the task requirements like the processing ones. After classification, virtual machines will be created in accordance with the predicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling (FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on …
Identifying Criminal Organizations From Their Social Network Structures, Muhammet Serkan Çi̇nar, Burkay Genç, Hayri̇ Sever
Identifying Criminal Organizations From Their Social Network Structures, Muhammet Serkan Çi̇nar, Burkay Genç, Hayri̇ Sever
Turkish Journal of Electrical Engineering and Computer Sciences
Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these …
An Improved Tree Model Based On Ensemble Feature Selection For Classification, Chandralekha M, Shenbagavadivu N
An Improved Tree Model Based On Ensemble Feature Selection For Classification, Chandralekha M, Shenbagavadivu N
Turkish Journal of Electrical Engineering and Computer Sciences
Researchers train and build specific models to classify the presence and absence of a disease and the accuracy of such classification models is continuously improved. The process of building a model and training depends on the medical data utilized. Various machine learning techniques and tools are used to handle different data with respect to disease types and their clinical conditions. Classification is the most widely used technique to classify disease and the accuracy of the classifier largely depends on the attributes. The choice of the attribute largely affects the diagnosis and performance of the classifier. Due to growing large volumes …
Dop: Discover Objects And Paths, A Model For Automated Navigation Andselection In Virtual Environments, Muhammad Raees, Sehat Ullah
Dop: Discover Objects And Paths, A Model For Automated Navigation Andselection In Virtual Environments, Muhammad Raees, Sehat Ullah
Turkish Journal of Electrical Engineering and Computer Sciences
Navigation and selection are the two interaction tasks often needed for the manipulation of an object in a synthetic world. An interface that supports automatic navigation and selection may increase the realism of a virtual reality (VR) system. Such an engrossing interface of a VR system is possible by incorporating machine learning (ML) into the realm of the virtual environment (VE). The use of intelligence in VR systems, however, is a milestone yet to be achieved to make seamless realism in a VE possible. To improve the believability of an intelligent virtual agent (IVA), this research work presents DOP (Discover …
Heart Attack Mortality Prediction: An Application Of Machine Learning Methods, Issam Salman
Heart Attack Mortality Prediction: An Application Of Machine Learning Methods, Issam Salman
Turkish Journal of Electrical Engineering and Computer Sciences
The heart is an important organ in the human body, and acute myocardial infarction (AMI) is the leading cause of death in most countries. Researchers are doing a lot of data analysis work to assist doctors in predicting the heart problem. An analysis of the data related to different health problems and its functions can help in predicting the wellness of this organ with a degree of certainty. Our research reported in this paper consists of two main parts. In the first part of the paper, we compare different predictive models of hospital mortality for patients with AMI. All results …