SÜRÜ ZEKASI YÖNTEMLERİYLE AŞIRI ÖĞRENME MAKİNESİ’NİN ÖĞRENME PARAMETRELERİ OPTİMİZASYONU

SÜRÜ ZEKASI YÖNTEMLERİYLE AŞIRI ÖĞRENME MAKİNESİ’NİN ÖĞRENME PARAMETRELERİ OPTİMİZASYONUÖzetSinir ağları algoritmalarından olan Aşırı Öğrenme Makinesi (AÖM)’de giriş ağırlığı ve gizli eşik değeri parametrelerinin rastgele seçilmekte ve çıktı katman ağırlıkları analitik olarak hesaplanmaktadır. Bundan dolayı ağın öğrenme işlemi hızlı bir şekilde gerçekleşmektedir. Ayrıca AÖM’nin gradyan temelli algoritmalara göre gizli katmanda ihtiyaç duyduğu nöron sayısı daha fazla olmaktadır. Bu nedenle giriş ağırlıkları ve gizli nöron eşik değerlerinin optimum değerlerinin bulunması AÖM'nin performansına etki etmektedir. Bu çalışmada bu optimum değerlerin belirlenmesinde sürü zekası algoritmalarından Parçacık Sürü Optimizasyonu (PSO) ve Rekabetçi Sürü İyileştirici (RSİ) kullanılmıştır. Optimum giriş ağırlıkları ve gizli eşik değerlerinin belirlenerek çıkış ağırlıkları Moore-Penrose genelleştirilmiş tersiyle analitik olarak hesaplanmıştır. AÖM, RSİ-AÖM ve PSO-AÖM modellerinin çok sınıflı tiroit veri setine uyarlanarak öğrenme parametrelerinin optimizasyonu ile en iyi doğruluk oranları sırasıyla %94.74, %94.86, %95.42 olarak elde edilmiştir. Optimizasyon metotlarının AÖM modellerinin sınıflandırma performansını artırdığı görülmüştür.Anahtar Kelimeler: Aşırı Öğrenme Makinesi (AÖM), Metasezgisel, Parçacık Sürü Optimizasyonu (PSO), Rekabetçi Sürü İyileştirici (RSİ)OPTIMIZATION OF LEARNING PARAMETERS OF EXTREME LEARNING MACHINE WITH SWARM INTELLIGENCE METHODSAbstractIn the Extreme Learning Machine (ELM), which is one of the neural networks algorithms, the input weight and hidden bias value parameters are randomly selected and the output layer weights are calculated analytically. Therefore, the learning process of the network takes place quickly. In addition, the number of neurons needed by the hidden layer is higher than the gradient-based algorithms. Finding optimum values of entry weights and hidden neuron bias values affects the performance of the ELM. In this study, Particle Swarm Optimization (PSO) and Competitive Swarm Optimizer (CSO) were used to determine these optimum values. By determining the optimum input weights and hidden bias values, the output weights were analytically calculated by Moore-Penrose generalized inverse. By adapting the multi-class thyroid data set of ELM, CSO-ELM and PSO-ELM models, the best accuracy rates were obtained as 94.74%, 94.86%, 95.42% respectively. It has been seen that optimization methods increase the classification performance of the ELM models.Keywords: Extreme Learning Machine (ELM), Metaheuristic, Particle Swarm Optimization (PSO), Competitive Swarm Optimizer (CSO

MANYETİK FİLTRELER İÇİN FPGA TABANLI BULANIK KONTROLÖR TASARIMI

FPGA'ler işlemleri yüksek hızda ve paralel olarak gerçekleştirebilen, veri depolama kapasiteli, taşınabilir, pratik, az miktarda enerji harcayan, programlanabilen lojik cihazlardır. Bu özellikleri FGPA'leri güçlü bir dijital kontrol aracı haline de getirmektedir. Bu çalışmada Manyetik Filtre (MF) performansını en yüksek seviyede tutacak bir bulanık mantık kontrolör tasarımı VHDL kullanılarak FPGA üzerinde gerçekleştirilmiştir. Gerçekleştirilen modelin test sonuçları ile MATLABFuzzy Logic Toolbox'dan elde edilen test sonuçları karşılaştırılarak kontrol sisteminin doğrulaması yapılmıştır. Diğer sistemlere göre önerilen sistemin dikkate değer şekilde başarılı olduğu görülmüştür

Densification of TiB2 with metallic additive by using vacuum arc melting: A preliminary study

In this study, densification of TiB2 with the addition of 316L grade stainless steel powder by using the vacuum arc melting (VAM) process was investigated. 316L and TiB2 powders were mixed in predetermined amounts and pressed to form pellet shaped samples which were later heated in the VAM. Microstructures of the samples were examined by SEM and an optical microscope. It was observed that TiB2 grains in the samples were wetted by the molten stainless steel phase. Furthermore, it was noted that the sample composition and arc current in the VAM affected the densification behavior of the samples. This kind of a material processing technique can be used to build up a composite layer over different substrates as well as over selected surfaces of machine parts

A CNN-SVM study based on selected deep features for grapevine leaves classification

WOS:000742844600001The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. The species of grapevine leaves are important in terms of price and taste. In this study, deep learning-based classification is conducted by using images of grapevine leaves. For this purpose, images of 500 vine leaves belonging to 5 species were taken with a special self-illuminating system. Later, this number was increased to 2500 with data augmentation methods. The classification was conducted with a state-of-art CNN model fine-tuned MobileNetv2. As the second approach, features were extracted from pre-trained MobileNetv2′s Logits layer and classification was made using various SVM kernels. As the third approach, 1000 features extracted from MobileNetv2′s Logits layer were selected by the Chi-Squares method and reduced to 250. Then, classification was made with various SVM kernels using the selected features. The most successful method was obtained by extracting features from the Logits layer and reducing the feature with the Chi-Squares method. The most successful SVM kernel was Cubic. The classification success of the system has been determined as 97.60%. It was observed that feature selection increased the classification success although the number of features used in classification decreased

Colistin nephrotoxicity increases with age

WOS: 000342202800002PubMed ID: 25073536Background: Colistin (COL) has become the backbone of the treatment of infections due to extensively drug-resistant (XDR) Gram-negative bacteria. The most common restriction to its use is acute kidney injury (AKI). Methods: We conducted a retrospective cohort study to evaluate risk factors for new-onset AKI in patients receiving COL. The cohort consisted of 198 adults admitted to 9 referral hospitals between January 2010 and October 2012 and treated with intravenous COL for >= 72 h. Patients with no pre-existing kidney dysfunction were compared in terms of risk factors and outcomes of AKI graded according to the RIFLE criteria. Logistic regression analysis was used to identify associated risk factors. Results: A total of 198 patients met the inclusion criteria, of whom 167 had no pre-existing kidney dysfunction; the mean patient age was 58.77 (+/- 18.98) y. Bloodstream infections (34.8%) and ventilator-associated pneumonia (32.3%) were the 2 most common indications for COL use. New-onset AKI developed in 46.1% of the patients, graded as risk (10%), injury (15%), and failure (21%). Patients with high Charlson co-morbidity index (CCI) scores (p = 0.001) and comparatively low initial glomerular filtration rate (GFR) estimations (p < 0.001) were more likely to develop AKI, but older age (p = 0.001; odds ratio 5.199, 95% confidence interval 2.684-10.072) was the major predictor in the multivariate analysis. In-hospital recovery from AKI occurred in 58.1%, within a median of 7 days. Conclusions: COL-induced nephrotoxicity occurred significantly more often in patients older than 60 y of age and was related to low initial GFR estimations and high CCI scores, which were basically determined by age

Suboptimal use of non-vitamin K antagonist oral anticoagulants: Results from the RAMSES study

WOS: 000384041400052PubMed ID: 27583892This study aimed to investigate the potential misuse of novel oral anticoagulants (NOACs) and the physicians' adherence to current European guideline recommendations in real-world using a large dataset from Real-life Multicenter Survey Evaluating Stroke Prevention Strategies in Turkey (RAMSES Study).RAMSES study is a prospective, multicenter, nationwide registry (ClinicalTrials.gov identifier NCT02344901). In this subgroup analysis of RAMSES study, patients who were on NOACs were classified as appropriately treated (AT), undertreated (UT), and overtreated (OT) according to the European Society of Cardiology (ESC) guidelines. The independent predictors of UT and OT were determined by multivariate logistic regression.Of the 2086 eligible patients, 1247 (59.8%) received adequate treatment. However, off-label use was detected in 839 (40.2%) patients; 634 (30.4%) patients received UT and 205 (9.8%) received OT. Independent predictors of UT included >65 years of age, creatinine clearance 50mL/min, urban living, existing dabigatran treatment, and HAS-BLED score of <3, whereas that of OT were creatinine clearance <50mL/min, ongoing rivaroxaban treatment, and HAS-BLED score of 3.The suboptimal use of NOACs is common because of physicians' poor compliance to the guideline recommendations in patients with nonvalvular atrial fibrillation (NVAF). Older patients who were on dabigatran treatment with good renal functions and low risk of bleeding were at risk of UT, whereas patients who were on rivaroxaban treatment with renal impairment and high risk of bleeding were at risk of OT. Therefore, a greater emphasis should be given to prescribe the recommended dose for the specified patients