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Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

Haedar Al-Safi | Jorge Munilla | Cevat Rahebi

A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the ...Daha fazlası

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An Advantageous Donor Site Alternative for Preparing Crushed Cartilage Graft: The Postero-inferior Part of the Septal Cartilage

Mehmet Cüneyt Öngüt

Crushed cartilage is used in rhinoplasties and crushing carry the risk of devitalization. The most infero-posterior part of the septal cartilage has a rough surface compared with the smooth surface of the remaining parts. This cartilage may be more convenient for crushing with lesser pressure requirements, increasing the viability. Twenty-six patients underwent septorhinoplasty and the infero-posterior part of the septal cartilage was harvested. The rough cartilage was utilized in nine patients (excluded from the study). Seventeen patients were included in the study. The mean age of the patien ...Daha fazlası

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Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Cevat Rahebi

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The imple ...Daha fazlası

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Comparison of inflammation markers and severity of illness among patients with COVID-19, acute psychiatric disorders and comorbidity

Aslı Enez Darçın

dimer, fibrinogen, and comorbid illness are associated with the course and prognosis of COVID- 19. However, the course of acute severe psychiatric disorders overlapping with COVID-19 infec- tion was not investigated and remained as an unclarified research area. This study aimed to dem- onstrate inflammatory markers and the course of patients suffering from both conditions. Methods: Thirty-eight inpatients with COVID-19 and comorbid acute psychiatric disorders (COVID-19+PD), 31 inpatients with COVID-19, and 38 inpatients with an acute psychiatric disor- der (PD) were included in the study. Neut ...Daha fazlası

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An Intelligent Anomaly Detection Approach for Accurate and Reliable Weather Forecasting at IoT Edges: A Case Study

Buket İşler

Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away ...Daha fazlası

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An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

Cevat Rahebi

Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of t ...Daha fazlası

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Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

Hakan Aydın

Objective: To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. Methods: The “College of Wisconsin Breast Cancer Dataset”, which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the ...Daha fazlası

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Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

Sima Etaner Uyar

An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter s ...Daha fazlası

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Richards’s curve induced Banach space valued ordinary and fractional neural network approximation

Seda Karateke

Here we perform the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional deriva- tives. Our operators are defined by using a density function generated by the Richards curve, which is generalized logistic function. The approximations a ...Daha fazlası

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Draft Genome Sequence of Virgibacillus sp. Strain AGTR, Isolated from Hypersaline Lake Acigol in Turkey

Meryem Menekşe Kılıç | Nevin Gül Karagüler

Virgibacillus sp. strain AGTR, which is a haloalkaliphilic microorganism, was isolated from a sediment sample collected in hypersaline Lake Acıgöl in Turkey. It has the potential to produce biotechnologically essential proteases. Here, the whole-genome sequence and its annotations are reported.

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Association of BDNF Gene Val66Met Polymorphism with Suicide Attempts, Focused Attention and Response Inhibition in Patients with Schizophrenia Şizofreni Hastalarında BDNF Geni Val66Met Polimorfizminin İntihar Girişimi, Odaklanmış Dikkat ve Yanıt İnhibisyonu ile İlişkisi

Aslı Enez Darçın

Introduction: The relationship between BDNF gene Val/Met polymorphism and clinical symptoms, attention and executive functions in patients with schizophrenia was investigated in this study. Also, BDNF Val66Met gene polymorphism was compared between patients and healthy controls. Thus, genetic factors that may affect both the etiology and cognitive functions in schizophrenia were evaluated. Methods: BDNF Val66Met gene polymorphism was investigated in 102 patients with schizophrenia and 98 healthy controls. Cognitive functions were evaluated by the Wisconsin Card Sorting Test (WCST) and Stroop T ...Daha fazlası

Süresiz Ambargo

The effect of gestational diabetes mellitus on occurrence of the pelvic girdlepain and symptom severity in pregnant women

Merve Can

The primary objective of this study was to examine the effect of gestational diabetes mellitus (GDM) on pelvic girdle pain (PGP) occurrence and symptom severity. Pregnant women who were with/without GDM, 20–40 years of age, and also in the second and third trimesters of pregnancy were included in the study. PGP provocation tests were administered to 187 pregnant women to determine the presence and severity of PGP. Based on the test results, the study subjects were divided into two groups; Group 1 (GDM+, PGP+; n:32) and Group 2 (GDM−, PGP+; n:35). Both groups were asked to fill in the Pelvic Gi ...Daha fazlası

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