PRECURSORS OF EARTHQUAKES: VLF SIGNALSIONOSPHERE IONOSPHERE RELATION

lot of people have died because of earthquakes every year. Therefore It is crucial to predict the time of the earthquakes reasonable time before it had happed. This paper presents recent information published in the literature about precursors of earthquakes. The relationships between earthquakes and ionosphere are targeted to guide new researches in order to study further to find novel prediction methods

A Review about Deep Learning Methods and Applications

<span style="font-size: 13px; font-family: Helvetica, Arial, sans-serif;">Makine ögrenmesi alaninda yapay sinir aglari birçok problemin çözümünde siklikla kullanilmistir. Ancak ?Yapay Zeka Kis Uykusu? olarak da adlandirilan dönemde basta donanimsal kisitlamalar ve diger problemler sebebiyle bu alandaki çalismalar durma noktasina gelmistir. 2000’lerin basinda tekrar gözde bir alan olmaya baslayan yapay sinir aglari, GPU gelismeleriyle birlikte sig aglardan derin aglara geçis yapmistir. Bu yaklasim görüntü islemeden, dogal dil islemeye, medikal uygulamalardan aktivite tanimaya kadar oldukça genis bir yelpazede basariyla kullanilmaya baslanmistir. Bu çalismada, derin ögrenmenin tarihçesi, kullanilan yöntemler ve uygulama alanlarina göre ayrilmis çalismalar anlatilmistir. Ayrica son yillarda kullanilan kütüphaneler ve derin ögrenme üzerine yogunlasan çalisma gruplari hakkinda da bilgiler verilmistir. Bu çalismanin amaci, hem arastirmacilara derin ögrenme konusundaki gelismeleri anlatmak, hem de derin ögrenme ile çalisilacak muhtemel konulari vermektir.</span><span style="font-size: 13.3333px; font-family: &quot;Times New Roman&quot;, serif;">Artificial neural networks were used in the solution of many problems in the field of machine learning. However, in the period called &quot;AI Winter&quot;, studies in this area have come to a halt due to especially hardware limitations and other problem. Artificial neural networks, which started to become a popular area at beginning of the 2000s, have switched from shallow networks to deep networks thanks to GPU developments. This approach has been successfully used in a wide range of fields from image processing to natural language processing, from medical applications to activity identification. In this study, it is described the history of the deep learning, methods and the implementations separated by the application areas. In addition, information has been given to the libraries used in recent years and working groups focused on deep learning. The aim of this study both explains the developments in deep learning to researchers and provides possible fields study with deep learning.</span

GDPR Compliance IoT Authentication Model for Smart Home Environment

The Internet of things (IoT) became quickly one of the most popular and most discussed topics in research. Studies paid attention to the Internet stuff, primarily to new products that aim to achieve greater efficiency and simplicity in life. IoT may cover several fields of the smart environment. Because of the data exposure that occurs when data is transferred via various channels, data protection issues have become a major problem as the company continues to expand. When user privacy and property are taken into consideration, the situation may become much worse. As a result, the authentication process for communicating entities has garnered considerable attention. In this paper, we proposed a secure authentication model for smart home applications, which privacy considered and complies with the General Data Protection Regulation GDPR. The proposed scheme improved the existing authentication schemes' performance and security level. This work based on the Elliptic curve cryptography ECC, one-way hash function, and XOR operation. The proposed lightweight authentication model is suitable for resource-constrained devices. This study is developing the offline direct authentication model to authenticate users and IoT devices in the local network. In addition, our scheme uses the online authentication server to authenticate all system parts

GENOTYPING AND ANALYSIS OF rs911271 POLYMORPHISM FOR INTRACRANIAL ANEURYSM

Intracranial aneurysms (IA) are balloon like dilation of the intracranial arterial wall in the brain. It affects %2-5 of the general population and arise from the action of multiple genetic and environmental risk factors. Single nucleotide polymorphisms (SNPs) are variations at a single position in a DNA sequence among individuals and they are associated with certain diseases to evaluate an individual's genetic predisposition to develop a disease. Recently, several SNPs associated with IA have been identified in genome-wide association studies. To our knowledge, the effects of these SNP's in Turkish population has not been studied and this arises great necessity to study them. In this study it is aimed to genotype and analyze rs911271 polymorphism, known to be associated with IA in different populations, to determine genetic risk predisposition of this SNP in Turkish population. Genotyping of rs911271 polymorphism was conducted by using the iPLEX assay in study group which consists of 105 intracranial aneurysm patients and 102 healty controls. Findings of this study showed that there is a lack of association between rs911271 and intracranial aneurysm in Turkish population. Despite the odds ratio was determined as 1,03 (95% CI=0,70-1,51) for carriers of any A allele, this result was not associated with IA in our population because of having a p value of 0,890