Bilgisayar Programcılığı Bölümü Koleksiyonu
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- ItemOptimal Smart Agriculture Technologies and Solutions in the Future of Farming(Ataturk University, 2024-12) Çakmakçı, Muhammet Fatih; Günay, Faruk Baturalp; 410449Precision agriculture technologies have been developed and are still being developed to increase the efficiency of agricultural processes, optimize resource utilization, and support environmental sustainability. One of the most important ways to solve food shortages is the use of modern technology and the integration of artificial intelligence in agriculture to increase productivity. Smart farming uses technologies such as optical, mechanical, and electrochemical sensors; air flow and location tracking; drones; satellite imagery; artificial intelligence; and the Internet of Things to monitor, analyze and manage farm practices. Smart agricultural technologies are utilized in a wide range of areas, including pest management, weed control, plant monitoring, storage management, irrigation management, disease management and control, weather forecasting and monitoring, yield estimation, soil composition analysis, and agricultural machinery management. By utilizing realtime data and intelligent decision-making systems, smart agriculture aims to increase productivity, reduce resource waste, improve sustainability, and address the challenges posed by a growing global population. Another goal of precision agriculture technology is to automate data collection and analysis processes, enabling farmers to make more informed decisions while reducing the cost of agricultural inputs and increasing productivity. The use of digital technologies in agriculture and livestock is rapidly increasing. Smart monitoring systems enhance agricultural efficiency, whereas digital technologies improve productivity, sustainability, and effectiveness. Smart greenhouses, irrigation, and fertilization systems support agricultural sustainability by monitoring environmental and plant parameters. In this study, the opportunities, benefits, future trends, and effects of the use of precision agriculture technologies on sustainable agriculture and food production are discussed.
- ItemThe Future of Farming: Leveraging AI, Machine Learning, and Smart Systems for Optimal Agricultural Practices(ÇOMU Publication, 2024-10) Çakmakçı, Muhammet Fatih; Günay, Faruk Baturalp; 410449In smart agriculture (SA), key applications of intelligent technologies include pest management, weed control, monitoring agricultural products, storage management, disease management and control, weather forecasting and monitoring, irrigation management, yield prediction, soil composition and management, and machinery management. Managing the agricultural production supply chain, measuring soil variability, improving agricultural production and management, reducing resource usage, monitoring water consumption, enhancing agricultural processes, identifying agricultural risks and hazards, and optimizing decision-making are crucial application areas of agricultural technologies.The use of digital technologies in agriculture and livestock is rapidly increasing. Optical, mechanical, electrochemical sensors, air flow, and location tracking technologies provide early warnings for diseases and pests, optimizing harvest processes. Smart monitoring systems enhance agricultural efficiency, while digital technologies improve productivity, sustainability, and effectiveness. Smart greenhouses, irrigation, and fertilization systems support agricultural sustainability by monitoring environmental and plant parameters.In livestock management, environmental and body sensors improve animal health and living conditions. Machine learning algorithms are effective in detecting mating behaviors and diseases in livestock. Precision livestock systems monitor health and welfare parameters, increasing productivity and protecting animal health. Artificial intelligence (AI) and machine learning (ML) applications are effective in analyzing soil data, plant phenotyping, and carbon stock estimation. Smart irrigation systems contribute to water conservation and increased efficiency. Additionally, smart harvesting systems help achieve sustainable production with lower costs and increased productivity. These technologies enhance the sustainability of agriculture and livestock by strengthening the capacity to manage productivity and environmental impacts. This article will discuss the topics that mentioned above.
- ItemUsing web 3.0 in education: a systematic review(ISRES Publishing, 2022-12) Aydın, Sevim; Duman, Emel; Baltacı, Şehnaz; 385249; Hebebci, Mustafa Tevfik; Yılmaz, OğuzThe internet, which has become an indispensable part of our lives, is rapidly developing and changing with each passing day. In parallel with the development of the Internet, the expected development in the Web is also observed. The first web technologies that entered our lives along with the emergence of the Internet consist of simple, plain, read-only content and are called Web 1.0 (Parsa, 2009; Dominic, Francis & Pilomenraj, 2014). In Web 1.0 technologies, there are websites where the flow of information is unidirectional, the content is limited and created by a content provider, and users can only access the provided subject (Park, 2013; Thomas & Li, 2008). Due to the limited use of Web 1.0, Odabaşı et al. (2012) named Web 1.0 users as content-dependent passive readers.
- ItemInvestigation of the effect of computer-aided animations on conceptual understanding through metaphors: an example of artificial intelligence(ICETOL, 2022-06) Aydın, Sevim; Duman, Emel; Bertiz, Yasemin; Birişçi, Salih; 385249In this research, Information Technology and Software (IT) course the differences between the conceptual understanding of fifth grade students regarding the concept of artificial intelligence before and after computer-aided animation monitoring were tried to be determined through metaphors. In the 2021-2022 academic year, 39 teachers working in the field of Information Technologies and 101 fifth-grade students studying at a secondary school in the Nilufer District of Bursa participated in the study. The purposeful sampling method was used to select the sample of the study. At the first stage of the study, the “Information Technology and Software Course Concept Teaching Questionnaire” prepared by the researchers was applied to the teachers online in order to identify the concepts that had difficulty in teaching within the scope of the BTY course. As a result of the survey dec, many concepts that are difficult to teach have been revealed, and among them, the concept of “artificial intelligence” has been included in the study due to its current and open to development. In the second stage of the study, in order to determine the metaphorical perceptions of students about the concept of artificial intelligence, students were asked to “Artificial intelligence is like ... because …”. In the light of the themes obtained from the metaphor results, computer-aided animation was developed by the researchers, which takes the concept of artificial intelligence as a subject. After the animation was shown to the students, the metaphor study was repeated and the change in the students' understanding of the concept of artificial intelligence was tried to determine. As a result of the study, although the conceptual categories related to artificial intelligence had similar characteristics in general, it was found that the justifications in the conceptual perceptions after animation were more meaningful
- ItemGame-based teaching in teacher education: a systematic review(ICETOL, 2022-06) Aydın, Sevim; Bertiz, Yasemin; Parlak Yılmaz, Nuray; 385249Today, technological tools have entered almost all fields and have been used extensively. Games have inevitably been affected by this process and games have become played in virtual environments with digital tools. Today, people of all ages interact with games and children can spend long hours playing games without getting bored, and the results of the research have paved the way for the use of games in education. The research, it is aimed to systematically deal with the studies on game-based teaching used in teacher education. The distribution of these studies by years, the distribution of their methods, data collection tools, data analysis methods, keywords, citations and country distribution of citations were revealed. Advanced search modules ERIC, EBSCO, Web of Science and TR Index are used. Keywords in the search are “game-based teaching” or “digital game-based learning”. Among the research articles, articles used as teaching methods and educational studies in higher education were taken. In this context, the PRISMA statement was used in research and report creation. The obtained data and results were analyzed by discussing with other findings obtained from the literature.
