Browsing by Keywords "Artificial intelligence"

Now showing 1 - 5 of 5
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    A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture
    (Springer, 2024-09) Gülmez, Burak; 222298
    This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.
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    A modern approach to data privacy with federated learning
    (Maltepe University, 2023) Kalkavan, Ziya Can; Şahinaslan, Ender; Şahinaslan, Önder; 122635
    Today, information technologies and their usage areas are increasing day by day. Advanced technologies such as the internet of things, smart devices and applications, machine learning and arti cial intelligence are a driving force in the spread of their usage areas. The increase in prevalence and use also increases the production and sharing of data. This increase causes various security problems and concerns in terms of data privacy. Therefore, a balance has to be struck between the need for data sharing and its security. For this purpose, the use of federated learning methods has been examined. Traditional data sharing methods focus on centralized solutions for the processing of private and sensitive data of data subjects, but this causes various problems and raises concerns in the sharing of sensitive data. In the federated learning model, it trains locally without data sharing. It has a distributed arti cial intelligence approach that can run di erent resources together. Thus, it o ers an alternative solution that can help address data privacy concerns arising from the traditional method. In this study, the basic principles, usage areas, advantages and difficulties of federated learning, which is also accepted as a modern approach in data privacy, are discussed. The data and examples obtained in the study will be presented.
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    ARTIFICIAL INTELLIGENCE INDUCED UNEMPLOYMENT FEAR SCALE
    (ISARC, 2026-02) Ulusoy, Kaan; İşcan, Orhan; Boz, Dursun; 331826
    The insurance sector is currently one of the industries that makes extensive use of artificial intelligence technologies (AI). In operational terms, AI is actively used in a wide range of areas, from sales and marketing processes to customer relationship management and after-sales services, from reporting and claims–compensation management to new product development and information technology applications. Within the “Fintech” concept, which has emerged and gained prominence through the convergence of finance and technology, insurance technologies known as “Insurtech” operate by developing digital solutions aimed at making existing models in the insurance sector more profitable, efficient, and effective. Technological initiatives continue their activities in regions such as Silicon Valley, where R&D activities are supported, benefiting from various incentives and industry collaborations. In the Turkish insurance sector, where similar practices are present, national companies are also observed to be adapting to modern technologies and being influenced by digitalization processes. One of the most prominent concerns arising from the rapid development and deep sectoral penetration of AI technologies is the possibility that AI may replace human labor in the future. In this study, fear of unemployment caused by artificial intelligence was taken as the central focus, and the fear of job loss experienced by individuals working in insurance companies operating in Turkey that use artificial intelligence technologies was examined. As a result of the literature review, no previous scale development study was found in the relevant field; therefore, the need to develop a new scale emerged. In the pilot study, the Artificial Intelligence Induced Fear of Unemployment Scale, consisting of 25 items and developed for the first time in the field, was administered to 100 insurance company employees through convenience sampling on a voluntary basis. As a result of the analyses conducted, it was determined that the factor loadings of the sub-dimensions of the scale named “Managerial Approach”, “Technophobia”, “Self-Efficacy”, and “Adaptation” ranged between 0.579 and 0.908. The developed scale has been introduced to the literature as a tool applicable not only to the insurance sector but also to different sectors.
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    Investigation 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; 385249
    In 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
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    Investigation of the effects of computer-aided animations on conceptual understanding through metaphors: An example of artificial intelligence
    (ICETOL, 2022) Aydın, Sevim; Duman, Emel; Bertiz, Yasemin; Birişçi, Salih; 385249
    In this study, it was aimed to investigate the change in the conceptual understanding of "artificial intelligence". For this purpose, the determination of the metaphors related to the concept of artificial intelligence in the stages before and after watching the artificial intelligence-themed computer-aided animation developed within the scope of the study, and the assessment of change between them constituted the research problem. During the 2021-2022 academic year 39 Information Technologies (IT) teachers and 103 fifth grade students studying in a secondary school in Nilüfer District of Bursa participated in this study. A phenomenological design pattern was adopted throughout the research. In the first stage of the study, many concepts were revealed as a result of the questionnaire applied to determine the concepts that were difficult to teach by IT teachers, and among them the concept of "artificial intelligence" was determined. In the second stage of the study, students were asked to complete the statement "Artificial intelligence is like … because ..." to determine the metaphorical perceptions on the concept of artificial intelligence. Based on the themes obtained from the metaphor results, computer-aided animation on the concept of artificial intelligence was developed by the researchers. After the animation demonstration to students, the metaphorical data collection process was repeated and it was attempted to determine the change in the students' understanding of the concept of artificial intelligence. As a result of the study, although the conceptual categories regarding artificial intelligence generally had similar characteristics, it was observed that the justifications in the post-animation conceptual perceptions were more meaningful.