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Copyright of Journal of Sports Sciences Research / Spor Bilimleri Araştırmaları Dergisi is the property of Journal of Sport Sciences Researches / Spor Bilimleri Arastirmalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Social Economic Research / Sosyal Ekonomik Araştırmalar Dergisi is the property of Sosyal Ekonomik Arastirmalar Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In agricultural activities, it is very important to get high-yield harvests at low costs. To get a high yield from the harvest, it is necessary to choose products and fertilizers suitable for the agricultural land. Getting high yields with low costs in agriculture is also possible with smart agriculture. With smart agriculture, agricultural activity stages can be controlled; Precautions can also be taken against negativities that may arise from external factors. To control agricultural lands remotely; Internet of Things (IoT) based sensors require hardware systems to receive data from these sensors and send them to the server. The data sent to the server is evaluated with artificial intelligence algorithms and the need for the land is determined according to the result and the need for fertilizer, irrigation need, etc. suitable for the agricultural stage is determined. Processing is carried out according to needs. For this purpose, in this study, hardware products, including field and server modules, were developed to capture sensor data from agricultural land with IoT and send it to the server. Data sets taken from open-access websites were used to train models with machine learning methods, one of the fields of artificial intelligence. The data taken from the field is evaluated with the created machine learning models, allowing the selection of products and fertilizers suitable for the land. [ABSTRACT FROM AUTHOR]

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Industry 4.0, the fourth industrial revolution, focuses on the increasing digitization and interconnection of goods, products, value chains, and business models. It aims to transform traditional industries into smart industries by incorporating innovative technologies, connecting people, things, and machines globally. This revolution creates smart factories and manufacturing environments by integrating physical assets into interconnected digital and mechanical processes. A growing domain the Internet of Things (IoT), has changed the way people communicate and interact with things around them, turning common objects into connected objects, providing a universal connection to the Internet. The Internet of Things is a rapidly growing technology that greatly contributes to the realization of Industry 4.0. By connecting the physical world to the digital world, IoT seeks to penetrate our daily environment of people and things "anytime, anywhere, anything and anyone", ideally using any network and service. IoT healthcare, quality control, logistics, energy, agriculture and manufacturing Industrial Internet of Things (IIoT) provides efficient and sustainable production by breaking new ground for a better understanding of the production process. The article explains the concepts of IoT, IIoT, and Industry 4.0, while also delving into the opportunities, threats, and challenges associated with their implementation in a detailed manner. [ABSTRACT FROM AUTHOR]

In today's rapidly changing and transforming world, the fourth industrial revolution, called Industry 4.0, heralds a radical transformation in production processes. This concept refers to a modern system in which machines exchange data with each other through automation systems, with almost no need for human intervention. Industry 4.0 consists of advanced technology-based components such as Smart Factories, the Internet of Things (IoT), Internet Services, Artificial Intelligence, Learning Robots, Big Data, Virtual Reality, 3D Printers, Cloud Technologies, and Cyber-Physical Systems. This new production model represents the most advanced technological level in human history to date. Industry 4.0 has begun to radically transform human life and business life. While in the past the main power of production processes was the physical labor of human beings, today this power has been replaced by the control of technological systems. People now undertake the planning and management of these systems instead of spending long hours at the machine. With Industry 4.0, production processes have accelerated and productivity has increased significantly. People are freed from the physical workload and have the opportunity to move into more strategic and creative fields. While this transformation has eliminated many traditional professions, new ones have emerged. In particular, skills related to data analysis, software development, and artificial intelligence have gained great importance. Industry 4.0 is a revolutionary development that changes not only production processes but also the basic dynamics of human life. Instead of carrying the physical burden of production, people are now in a strategic position to control and develop these advanced technological systems. In this study, the main features of Industry 4.0 and its effects on human life and business life are discussed in detail. In this study, a literature review was conducted and a document analysis method was used. [ABSTRACT FROM AUTHOR]

This paper aims to examine the effects of digital technologies on academic knowledge production in the field of urban research. It provides a comprehensive overview of the evolution of technology-based urban research literature, delineates the knowledge structure, and investigates prevalent trends. The study employs bibliometric analysis to analyze bibliographical and textual data extracted from scientific documents, which allows for the discovery of the existing epistemological structure of the discipline or research domain. The research was conducted by conducting a comprehensive and up-to-date review of the literature available in the Web of Science Core Collection regarding how concepts such as big data, artificial intelligence (AI), and the Internet of Things (IoT) are discussed in academic papers in the field of urban studies. A total of 2055 academic papers that met the established criteria were identified and analyzed using bibliometric analysis software called Bibliometrix. The results of the research aid in examining the integration of technological advancements into urban research, and reveal the temporal, spatial, and disciplinary distribution of scientific articles, as well as their reflections on new research areas and both frequently studied and yet unexplored topics. [ABSTRACT FROM AUTHOR]

Copyright of Itobiad: Journal of the Human & Social Science Researches / İnsan ve Toplum Bilimleri Araştırmaları Dergisi is the property of Itobiad: Journal of the Human & Social Science Researches and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Internet of things (IoT) is a collection of various types of devices that has sensors and connected to each other over a network. This technology was first implemented in 1982 and today, especially with the spread of the internet, it is frequently used in many areas such as identification and monitoring technologies, wired and wireless sensors and distributed information systems. As its usage areas have expanded, this technology has started to be used for data collection purposes. On the other hand, in an era where workable data has gained significant value, collecting data via the IoT has become an important endeavor not only for researchers in the field of data, but also for those who want to gain commercial benefits from this data. This situation has revealed the need to market such data in a reliable and healthy way. Due to its features such as transparency, immutability of records and decentralization, blockchain technology stands out as an effective and safe method to meet this need. In this work, with the help of SysML diagrams, it is examined that how an ideal blockchain-based data trading platform should be. In this direction, first the components of the platform were determined and the relations between these components were analyzed. In addition, the requirements needed for this system to work safely and effectively have been defined. Moreover, how these requirements can be met is demonstrated through an example scenario. [ABSTRACT FROM AUTHOR]

Indoor localization involves pinpointing the location of an object in an interior space and has several applications, including navigation, asset tracking, and shift management. However, this technology has not yet been perfected, and many methods, such as triangulation, Kalman filters, and machine learning models have been proposed to address indoor localization problems. Unfortunately, these methods still have a large degree of error that makes them ill-suited for difficult cases in real-time. In this study, we propose a hybrid model for Bluetooth low energy-based indoor localization. In this model, the triangulation method is combined with several machine learning methods (naïve Bayes, k-nearest neighbor, logistic regression, support vector machines, and artificial neural networks) that are optimized and tested in three different environments. In the experiment, the proposed model performed similarly to the solo triangulation model in easy and medium cases; however, the proposed model obtained a much smaller degree of error for hard cases than either solo triangulation or machine learning models alone. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Turkish Operations Management (JTOM) is the property of Ankara Yildirim Beyazit University Journal of Turkish Operations Management (JTOM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Beykoz Akademi Dergisi is the property of Beykoz University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mersin University Journal of the Faculty of Education / Mersin Üniversitesi Eğitim Fakültesi Dergisi is the property of Mersin University Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, smart cities solutions have been becoming popular day by day. The problem of finding a parking place is one of these implementations. Our study appeals to users who want to park their vehicle quickly and who want to find out whether the parking place is available to park before searching for a parking place. This application aims to find a solution to the problem of searching for a place to park. We have found a novel solution to this problem by using image processing methods and utilizing photographs of streetside parking places in the most effective way. In addition, an Android software has been developed through which we can deliver the results obtained with the image processing module to users via a mobile application. As a result, a parking IoT determination system has been implemented in this study with 92% accuracy value. Thanks to this system, vehicle owners who want to park their vehicles on the side of the street will be able to learn whether the parking places in a street are available in advance and they will be able to save their time by preferring more available streets.

Copyright of Journal of Defense Sciences / Savunma Bilmleri Dergisi is the property of Turkish Military Academy Defense Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fire is a natural disaster that causes ecological, social and economic damage. With global warming and the widespread use of explosive/flammable chemicals, fires have become one of the most important problems for humanity. Early fire detection is critical to minimize destruction. For this reason, studies on fire detection through images have begun to be carried out in order to detect fires early. In recent studies for fire detection from images deep learning algorithms are generally used. These studies focus on the analysis of images taken from cameras with models running on powerful servers. With the developments in mobile devices and internet of things, images can analyzed on edge devices. In the study fire and smoke detection model which requires low processing power has been developed to enable the images to be analyzed on mobile device without transferring them to server. The MobileNet convolutional neural network was revised, the last 3 layers were removed and replaced with a flattening layer and a dense layer consisting of two nodes. Fire and smoke detection model, the method with the highest accuracy was determined among the models developed using revised MobileNet, original MobileNet, MobileNetV2, EfficientNetB0, ShuffleNet, NASNetMobile and PeleeNet convolutional neural networks. To train and test the models, 80% of the dataset consisting of 43,355 images was used for training and 20% for testing. According to test results, highest accuracy rate of 98.37% was achieved in the revised MobileNet. [ABSTRACT FROM AUTHOR]

In this study, a non-invasive diagnosis that can be used to diagnose Sleep Apnea, which may pose a risk for cardiovascular diseases, obstruct airways and are the most common diseases related to breathing method has been developed. For this application, the Deneyap Card, which was developed with local and national facilities, was used. Three-axis IMU accelerometer sensor (LSM6DSM), which is built into the Deneyap card, is used as a detector. A symbolic but realistic environment has been created in which test measurements of sleep apnea disease will be carried out. In this context, a plastic doll model was used to make the measurements. As a result of the study, many parameters of the patient such as the patient's position, how long he lay in which position, how many times he changed positions during sleep, and in which position he entered apnea were successfully measured and recorded on the SD card. By developing an Internet of Things (IoT)-based system that will allow the remote monitoring of the measured parameters, the data can be successfully monitored by transmitting it to different environments. With this study, a subjective study that can shed light on future studies has been brought to the literature by using our local-national card, the Deneyap Card. [ABSTRACT FROM AUTHOR]

The increase in the number of vehicles brings parking needs in city centers. Many studies are being carried out to solve the problem and new parking lots are constructed. With the developing technology, smart systems are effectively used to increase the capacity of existing car parks. Unfortunately, both putting new car parks into service and equipping existing car parks with technological facilities cannot solve the problem. This highlights that public transportation is used effectively instead of vehicles. On the other hand, the idea of enabling people to reduce parking time is a partial solution. Thus, new smart parking mobile application called “Let's Share a Single Vehicle Park-TAPP was developed conceptually. In the system, utilization period must be specified by the user. Users who specify the duration of utilization are rewarded as a fee when they comply. If they do not, they are faced with overpayment and parking utilization restriction. The parking capacity will be planned instantly and an effective operating performance will be achieved. The factors affecting the proposed system were determined by SWOT analysis and the weighting of each factor using the AHP method was obtained by pairwise comparisons. With the A'WOT analysis, it has been determined that the increase in the need for parking spaces with the increase in the number of vehicles. The need for parking, which is the most important factor due to the increasing number of vehicles, shows the need for such innovative systems when all general factor weights of factors are evaluated together. [ABSTRACT FROM AUTHOR]

Smart meters are among the fundamental components of smart grids due to their features such as high-accuracy energy measurement, remote reading, and control. They are also utilized to reduce the rate of illegal electrical energy use in some distribution areas. This paper presents the development of a Wi-Fi smart meter and designing a smart plug-in device for customer-side energy management. In this way, a method that does not require additional infrastructure and extra investment for remotely reading the energy consumption in environments with common Wi-Fi networks, such as large shopping malls, business centers, community buildings, villa cities, and university campuses has been proposed. The Wi-Fi smart meter and smart energy metering module (SEMM) support various user-friendly applications in accordance with the infrastructure of the smart grid and smart building applications. The SEMM is capable of measuring, monitoring, and remotely managing each electrical device. Thanks to the developed mobile application, customers can monitor their Wi-Fi smart meter data and also the consumption of each electrical device through the SEMMs as well as remotely manage these devices. The application examples presented in this study are also aimed at increasing awareness of energy saving for customers. [ABSTRACT FROM AUTHOR]

Industry 4.0 is transforming and reshaping the production methods in all sectors, and significant-changes are also anticipated in television broadcasting in the near future. Artificial intelligence and robotic systems, virtual reality applications, big data, the Internet of Things, and cloud computing, among other components of Industry 4.0, are expected to enable automation in television broadcasting, diversify and expand broadcast content. However, it is also foreseen that employment issues will arise. The aim of this research is to elucidate the views of television professionals on contemporary television broadcasting, their perspectives on the evolving form of television broadcasting in the emerging world of Industry 4.0, and whether automation in broadcasting creates employment concerns. To achieve this objective, data was obtained through in-depth interviews using qualitative research techniques. Eight media professionals from different fields of television broadcasting, which could be affected by the nature of their work by developments in Industry 4.0, were selected using purposive sampling for the interviews. According to the findings, television workers in Turkey find contemporary television broadcasting technically successful but consider it lacking in terms of content quality. Participants believe that media quickly adapt to new technologies, and while some job sectors may disappear in Industry 4.0, new areas of expertise will emerge. The results show that most participants believe that television will be different from its current form in the near future as we enter Industry 4.0, and it will continue to exist in the future. [ABSTRACT FROM AUTHOR]

Information and communication technology is developing rapidly and many disruptive technologies such as cloud computing, Internet of Things, big data analytics and artificial intelligence are emerging. These technologies permeate the manufacturing industry and enable the fusion of the physical and virtual worlds through cyber-physical systems (CPS), which marks the advent of the fourth stage of industrial production (i.e. Industry 4.0). Industry 4.0 encompasses a large number of digital technologies affecting manufacturing businesses. The widespread application of CPS in production environments is making production systems increasingly intelligent. However, there is still a lack of comprehensive research on the applications of technologies enabling Industry 4.0 in manufacturing lifecycle processes. In order to advance research on the application of Industry 4.0 in the manufacturing industry, firstly, a conceptual framework for Industry 4.0 is presented in this study. Secondly, sample scenarios related to front-end technologies presented in this framework are explained. In addition, Industry 4.0 core technologies and their possible applications for Industry 4.0 smart manufacturing systems are reviewed. Finally, challenges and future perspectives are identified and discussed. [ABSTRACT FROM AUTHOR]

In today's smart homes, the infrastructure of IoT (Internet of Things) technology is used. As the use of smart homes increases, cyber attacks in this area are also increasing. It is very important to detect and prevent cyber attacks on smart homes as early as possible. In this study, a machine learning-based method is proposed to detect and prevent cyber attacks against smart homes. First of all, a smart home platform was created using the “Home Assistant” technology. Smart homes make extensive use of “Home Assistant” technology. The created smart home platform makes use of sensors and cameras. People can monitor and manage their homes remotely using sensors and cameras. Seven attacks, namely “brute force ftp”, “brute force ssh”, “dos http flood”, “dos icmp flood”, “dos syn flood”, “syn scan” and “udp scan” were carried out on the developed smart home platform. The collected dataset consists of eight classes with “normal” packages. A total of 435815 sample data were collected for eight classes. XGBOOST algorithm was used on this obtained dataset and attack types were classified. For Hold-out 80:20 and Hold-out 70:30 training test data, 92.55% and 92.49% accuracy were calculated, respectively. The results of the proposed XGBOOST algorithm were compared with the results of other machine learning algorithms and the results were found to be successful. [ABSTRACT FROM AUTHOR]

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of TYB Akademi Dil Edebiyat & Sosyal Bilimler Dergisi is the property of Turkiye Yazarlar Birligi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Intelligent Transportation Systems & Applications is the property of Journal of Intelligent Transportation Systems & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Uludag University Journal of the Faculty of Engineering (UUJFE) is the property of Uludag Universitesi, Muhendislik Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Çankırı Karatekin University Journal of the Faculty of Economics & Administrative Sciences is the property of Cankiri Karatekin University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Evrensel Tak ve Çalıştır (Universal Plug and Play, UPnP) ve IoT iletişim protokolleri sayesinde cihazların birbirleriyle ve ağ ile bağlantıları çok daha kolay ve hızlı yapılabildiğinden ev ağındaki bağlantı sayısı da artmıştır. Akıllı televizyonlar ve temizlik robotları gibi akıllı cihazlar, yaşam konforumuzu artırmakta ve ev ağı üzerinden tüm dünyaya bağlantı sağlar hale gelmiştir. Bu nedenle, ev ağının internete bağlı olduğu gerçeği ağdaki akıllı cihazların güvenlik durumlarının sorgulanması ihtiyacını ortaya çıkarmıştır. Bu çalışmada, ev ağı içerisindeki popüler cihazların güvenlik seviyelerinin analiz edilmesi sağlanmıştır. Ev Ağı içerisinde UPnP zafiyetine sahip cihazların varlığını tespit etmek için Python yazılım dili kullanılarak uygulama geliştirilmiştir. Geliştirilen uygulama kullanılarak ev ağı içerisindeki 15 adet cihazdan 3 adet cihazın UPnP açıklığına sahip olduğu görülmüştür. Bir senaryo içerisinde UpNP açıklığı kullanılarak saldırı uygulaması gerçekleştirilmiştir. Bu çalışma ile evdeki ağ ve iletişim yöntemleri güvenliğinin yanında her bir IoT cihazın güvenliğinin sağlanmasının gerekliliği ayrıntılı olarak sunulmuştur.

Dijital dönüşüm sürecinin başlamasıyla beraber, alışılagelen geleneksel bilgi hizmetlerinin mevcut yapısı ve sunum biçimleri farklı bir boyuta taşınmıştır. Bilgi taşıyıcılarının çeşitlilik göstermesi ve insanların bu tür cihazlara rahatlıkla erişebilmesi geleneksel bilgi hizmetlerini yetersiz kılmış, hizmet sağlayıcılarını farklı çözümler üretmeye zorlamıştır. Bu durum, insan-robot iş birliği ile var edilmeye çalışılan süper akıllı toplumlarda bilgi hizmetlerini mecburi bir değişime neden olmuştur. Toplum 5.0 kavramı teknolojinin sunduğu imkânlardan faydalanarak toplumun refah seviyesini artırmayı ve toplumsal sorunlara teknoloji tabanlı çözümler üretebilmeyi amaçlamaktadır. Toplum 5.0 süper akıllı toplum ifadesini karşılayan eşdeğer bir kelime olup, çalışma kapsamında bu anlamıyla kullanılmıştır. Çalışmada, raf ömrü çabuk tükenen teknolojik gelişmeleri küresel gündemle birlikte takip edebilmek ve söz konusu gelişmeler doğrultusunda bilgi merkezlerinde sunulan hizmetlerin yenilenmesi için farkındalık oluşturmak amaçlanmıştır. Çalışma, Toplum 5.0 projesi, dijital dönüşüm sürecinin etkileri ve süper akıllı toplumlarda bilgi hizmetleri konularını içermektedir.

Futbol sahalarının aydınlatılması yeni bir uygulama olmamakla birlikte ilerleyen teknoloji sayesinde yeni geliştirmelere ihtiyaç duyulmaktadır. Özellikle nesnelerin interneti kavramı gün geçtikçe hayatımızda daha fazla yer edinirken aydınlatma ekipmanlarının da bu kapsamda daha teknolojik bir hale getirilmesi kaçınılmazdır. Futbol sahasının aydınlatma seviyesinin değiştirilmesi gereken durumlarda kontrol sinyalleri kablolu veya kablosuz olarak iletilebilir. Ancak futbol sahası aydınlatma projektörlerinde kullanılan LED sürücülerin kontrolü için sinyal kablosu kullanılmasının bazı dezavantajları vardır. Saha aydınlatmasında kullanılan LED sürücülerin sinyal kablolarıyla kontrol edilmesi durumunda, kablo maliyeti, işçilik ve ortamdaki elektriksel gürültülerden etkilenme açısından değerlendirilirse, güvenli ve düşük maliyetli kablosuz sinyal taşımanın önemi net olarak ortaya çıkmaktadır. Bu çalışmada LoRa modülasyon tabanlı kontrol kartları geliştirilmiş ve Diyarbakır ilinde bulunan bir futbol sahasının aydınlatma sistemine uygulanmıştır. Bu sayede kontrol sinyallerinin kablo ile taşındığı sistemlere göre daha düşük maliyetli ve güvenli kablosuz bir sistem kurulmuştur.

The recent growth in Internet of Things (IoT) deployment has increased the rapidness of integration and extended the reach of the internet from computers, tablets, and phones to countless devices in our physical world. This growth makes our life more convenient and industries more efficient. However, at the same time, it brought numerous challenges in terms of security and expanded the area of cyber-attacks, especially the DoS and DDoS attacks. Moreover, since many IoT devices run custom or outdated operating systems, and most do not have enough resources to run typical intrusion detection systems, it was necessary to search for alternative solutions. Therefore, many researchers have joined the race to develop new lightweight intrusion detection methods. In this study, we have investigated the detection of different DoS attacks on the IoT network using machine learning techniques. The studied attacks are TCP Syn-Flood Attack, UDP Flood Attack, HTTP Slowloris GET Attack, Apache Range Header DoS, and Port Scan attack. We have proposed a new dataset, namely HEIoT21, which was generated in a real smart home environment using a collective of IoT devices and non-IoT devices connected to a wireless network. The proposed dataset included normal and anomaly data, and using the CiCflowmeter application, we extracted 82 network features from the proposed dataset. The dataset was labeled and categorized into binary-class and multi-class. Our dataset underwent multiple feature selection methods to keep only enough features to produce a good detection accuracy; for that, we have used Anova F-value Feature Selection, Random Forest importance feature selection, and Sequential Forward Feature Selection. The feature selection techniques produced three new sub-datasets, which were evaluated using multiple machine learning algorithms like Logistic Regression (LR), J48 Decision Tree (DT), Naïve Bayes, and Artificial Neural Network (ANN). A comparison study was conducted on the result obtained from applying the different machine learning algorithms on the derived sub-datasets, which led to the finding that the most suitable feature selection technique for the proposed dataset was Anova Fvalue and the best-fit machine learning algorithm for the proposed dataset was The Decision Tree which produced an accuracy result of 99.92% for binary classification and 99.94% for multi-class classification. In the end, our study was compared with other studies in the field of IoT intrusion detection, and we found that the result obtained through this study was higher than most others. Therefore, the proposed dataset could be of great use to those who want to work on the analysis and detection of the existing network security threats. Also, this study can be considered a cornerstone for a proper lightweight intrusion detection system, where the datasets can be expanded to include other types of attacks, new detection rules can be added, and an alert mechanism can be integrated to become a complete detection system. [ABSTRACT FROM AUTHOR]

This study has been carried out in order to question the ethical problems that big data-based products cause or may cause on privacy, and to reveal redictions for the future, in today's world where big data technology has developed and entered every area of our lives and brought privacy discussions with it. In this direction, 8 scenes selected from the "The Entire History of You" episode of the Black Mirror series, based on science fiction films and TV series that can reflect the future, were analyzed according to the semiotic analysis methods of Sassure and Barthes, and were examined in the context of ethical discussions on privacy in big data. According to this analysis, it was seen that the big data product with augmented reality and internet of things features in the series caused violations of personal and personal information privacy. It is thought that these violations will contribute to the ethical dimension of privacy in future big data studies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graduate School of Natural & Applied Sciences of Mehmet Akif Ersoy University / Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi is the property of Burdur Mehmet Akif Ersoy University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Inonu University Law Review / İnönü Üniversitesi Hukuk Fakültesi Dergisi is the property of Inonu University Law Review (IULR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Izmir Katip Celebi University Faculty of Health Sciences Journal / İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi is the property of Izmir Katip Celebi University, Faculty of Health Sciene Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of TYB Akademi Dil Edebiyat & Sosyal Bilimler Dergisi is the property of Turkiye Yazarlar Birligi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Disciplines Economics & Administrative Scienves Studies is the property of International Journal of Disciplines in Economics & Administrative Sciences Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Amaç: Bu makalenin amacı, Endüstri 4.0 ile önemi artan dijital ikiz kavramının yeri, önemi, verimliliği ve gelecekteki potansiyelini anlamak, diğer çalışmalar için temel oluşturmak adına literatüre katkı sağlamaktır. Yöntem: Bu çalışmanın yöntemi literatür taramasıdır. Dijital ikiz ve işletmelerde kullanımı ile ilgili en güncel makaleler, bildiriler, gazete ve internet haberlerine dair örnek uygulamalar incelenerek, konu ile ilgili derinlemesine bir derleme makale çalışması yapılmıştır. Bulgular: Değerlendirme sonuçlarına göre, dijital ikiz teknolojisi işletmeler için bir çözüm yoludur. Dijital ikiz teknolojisinin katkısı ile siber fiziksel sistemler ve nesnelerin interneti başta olmak üzere Endüstri 4.0 teknolojileri daha etkin kullanılabilmektedir. Dijital ikiz, esnek, daha hızlı, kaliteli ve kişiselleştirilmiş ürünler elde edilmesine yardımcı olmaktadır. İşletmelerin tahmin etme, güvenlik, teşhis etme ve mevcut durumu sorgulamasında önemli avantaj sağlayan dijital ikizler, karar vermede isabet gücünü, ürün ve hizmetlerde kaliteyi, operasyonel faaliyetlerde hızı artırarak, toplamda işletme verimliliğini ve örgütsel performansı iyileştirip işletmelerin rekabet gücünü artırmaktadır. Özgünlük: Dijital ikiz kavramının yeni ortaya çıkmasına bağlı olarak literatürde önemli bir boşluk bulunmaktadır. Bu nedenle makale, dijital ikiz hakkında bilgi veren, üretimde faydaları ve uygulamaları üzerine odaklanan ilk yerli çalışmalar arasındadır.

Driver drowsiness detection is an important issue to prevent traffic accidents. 40% of severe traffic accidents are due to drowsiness. Various methods are used for driver drowsiness detection. One of the driver drowsiness detection method is driver drowsiness detection based on the analysis of signals such as EEG and ECG. Another driver drowsiness detection method is driver drowsiness detection based on vehicle-driver interaction. The last driver drowsiness detection method used in the study is driver drowsiness detection from images. This method is more advantageous than the other two methods in terms of cost and usability because no driver intervention required. Classical image processing techniques and deep learning algorithms are used for driver drowsiness detection from images. Recent driver drowsiness detection studies are based on deep learning models. In addition, the model to be developed will need to be able to work on mobile devices in order to ensure widespread use. In the study, Convolutional Neural Networks were used for driver drowsiness detection on mobile devices. In order to increase the success rate of the model, the pre-trained model was reused with the transfer learning technique. The model developed for the training consists of 14 layers and 1,236,217 parameters. The dataset consists of two categories, open-eye and closed-eye images. The developed model achieved 95.65% accuracy, 95.86% precision, 94.32% recall and 95.17% f1 score which achieved better results than previous studies. A dataset of 2425 images was used to train the model. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Balkan & Near Eastern Journal of Social Sciences (BNEJSS) is the property of Balkan & Near Eastern Journal of Social Sciences (BNEJSS) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, with the increase in the number of employees in the enterprises and the proportional workload, different occupational accidents frequently occur. Examples of this are slippery floors, falling materials, harmful substances/gas leaks, improper use of protective clothing and equipment or not using them at all. Identifying these dangers and taking the necessary precautions are very important for both worker safety and employers. The most common accident among these dangerous situations is the accidents that occur as a result of slipping or falling. Such accidents are usually caused by a foreign liquid/substance on the work surface, the worker's inability to balance himself, or surface inequalities. With this study, an IoT and 1D CNN deep learning-based system has been developed to detect accidents such as falling, slipping and balance disorders to inform relevant health institutions. The developed 1D CNN-based system detected work accidents caused by falls with 100% accuracy. With the results obtained from this study, it is aimed to make improvements for the prevention of these accidents. [ABSTRACT FROM AUTHOR]