scholarly journals Evaluation of brain FDG PET images in temporal lobe epilepsy for lateralization of epileptogenic focus using data mining methods

2020 ◽  
Vol 50 (4) ◽  
pp. 738-748
Author(s):  
Ümit Özgür AKDEMİR ◽  
İrem YILDIRIM ◽  
Seda GÜLBAHAR ◽  
Kerim ŞEKER ◽  
Uğuray AYDOS ◽  
...  
Keyword(s):  
Data Mining ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Fdg Pet ◽  
Epileptogenic Focus ◽  
Mining Methods ◽  
Using Data

Data Mining zur Nacharbeitsdauerprognose*/Data Mining-based forecasting of rework duration - Predictive rework control and work process optimization in the automotive assembly

2017 ◽  
Vol 107 (10) ◽  
pp. 773-778
Author(s):  
S. Krzoska ◽  
M. Eickelmann ◽  
J. Schmitt ◽  
J. Prof. Deuse
Keyword(s):  
Data Mining ◽  
Process Optimization ◽  
Quality Data ◽  
Operating Process ◽  
Automotive Assembly ◽  
Related Quality ◽  
Mining Methods ◽  
Product And Process ◽  
Using Data ◽  
Manufacturing Execution

Der Fachbeitrag zeigt am Beispiel der Nacharbeitssteuerung und Arbeitsprozessoptimierung in der Automobilmontage, wie produkt- und prozessbezogene Qualitätsdaten durch den Einsatz von Data Mining-Methoden analysiert sowie effizient genutzt werden können. Dazu wurden Daten aus Manufacturing-Execution-Systemen (MES) mithilfe von Regressionsbäumen zur Entwicklung einer fahrzeugspezifischen Nacharbeitsdauerprognose ausgewertet. Das grundlegende Data Mining-Konzept sowie die Pilotierungsergebnisse werden nachfolgend dargestellt.   The article shows at the example of rework control and operating process optimization in the car assembly how recorded product- and process-related quality data can be analyzed and used efficiently by using Data Mining-methods. With data from MES-systems regression trees were built for a vehicle-specific rework duration forecast. The basic concept and validation results will be presented below.

scholarly journals A Survey of Anomaly Detection Using Data Mining Methods for Hypertext Transfer Protocol Web Services

2015 ◽  
Vol 11 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Mohsen Kakavand ◽  
Norwati Mustapha ◽  
Aida Mustapha ◽  
Mohd Taufik Abdullah ◽  
Hamed Riahi
Keyword(s):  
Data Mining ◽  
Web Services ◽  
Anomaly Detection ◽  
Hypertext Transfer Protocol ◽  
Mining Methods ◽  
Using Data ◽  
Transfer Protocol

scholarly journals Magnetic resonance spectroscopy of the brain in the diagnosis of temporal lobe epilepsy

2018 ◽  
Vol 10 (1S) ◽  
pp. 51-55
Author(s):  
E. S. Solomatova ◽  
N. A. Shnaider ◽  
A. A. Molgachev ◽  
D. V. Dmitrenko ◽  
I. G. Strotskaya
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
English Language ◽  
Russian Language ◽  
Resonance Spectroscopy ◽  
Epileptogenic Focus ◽  
Patients With Epilepsy ◽  
The Brain

The temporal lobe is the most epileptogenic region of the brain. 90% of patients with temporal ictal epileptomorphic EEG activity have a variable long history of seizures. Magnetic resonance spectroscopy  (MRS) may be useful in identifying an epileptogenic focus in patients  with epilepsy without apparent structural pathology at neuroimaging.Objective: to systematize the results of early studies on this issue.Materials and methods. An electronic search was carried out in two English-language (Medline, PubMed) and one Russian-language (eLIBRARY.RU) databases. The search queries found  18,019 citations, by which 12 full-text articles were selected.Results and discussion. The main criteria for the diagnosis of temporal lobe epilepsy by MRS is to lower the level of N-acetylaspartate (NAA), the ratio of NAA to creatinine + choline  (NAA/(Cr + Cho) in the brain region where there is neuronal death  or damage, as well as a change in the level of myo-inositol, the  elevated level of which indicates the presence of an epileptogenic  focus, while the decreased one shows the spread of pathological activity to the adjacent tissues.Conclusion. This review will contribute to a better diagnosis of temporal lobe epilepsy, as well as to the intravital noninvasive detection of metabolic changes in the brain long before the development of structural pathology.

Observation of Success Status of Employees in E-Learning Courses in Organizations with Data Mining

2017 ◽  
Vol 9 (1) ◽  
pp. 38-49
Author(s):  
Fatma Önay Koçoğlu ◽  
İlkim Ecem Emre ◽  
Çiğdem Selçukcan Erol
Keyword(s):  
Data Mining ◽  
Evaluation Criteria ◽  
Data Set ◽  
Completion Status ◽  
Completion Date ◽  
E Learning ◽  
Pharmaceutical Industries ◽  
Mining Methods ◽  
Using Data ◽  
Performance Results

The aim of this study is to analyze success in e-learning with data mining methods and find out potential patterns. In this context, 374.073 data of 2013-14 period taken from an institution serving in e-learning field in Turkey are used. Data set, which is collected from information technology, banking and pharmaceutical industries, includes success and industry of employees', trainings which they complete, whether the trainings are completed, first login and last logout dates, training completion date and duration of experience in training. Using this data set, success status of participants is observed by using data mining methods (C5.0, Random Forest and Gini). By observing using accuracy, error rate, specificity and f- score from performance evaluation criteria, C5.0 has chosen the algorithm which gives the best performance results. According to the results of the study, it has been determined that the sectors of the employees are not important, on the contrary the ones that are important are the completion status, the duration of experience and training.

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