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Dilemma between security and privacy on the internet
In this thesis, the following issues are discussed. Even in today society, it is hard to talk about security and privacy. And no one can imagine what will be structure of tomorrow society. We study what are the internet security systems and government security projects. Even we discuss that these systems effect to human rights and privacy. We define importance of cryptography and discuss relation with human rights. We examine internet privacy laws. We also study about George Orwell's book '1984' and consider similarity between novel and real life. We give information about intellectual property and its rights on the internet. We show that intellectual property rights are not enough to protect all properties and these rules are valid for some economic environments. We also discuss issue in instead of knowledge society, creates ignorance society. All these discussed and studied issues show big dilemma to us between security and privacy. The dilemma goes to a result that "there is no security" in today's society. Keywords: Security, privacy, internet security, internet privacy rights, cryptography, Orwell, intellectual property rights, knowledge society, ignorance society, internet and dilemma.
Modelling a university as a multi-agent system
Multi-Agent systems are an approach for designing and implementing complex social organizations in the software domain. In multi-agent systems; actors that interact with the system are modeled as software entities called "agents". These software agents can show autonomous and intelligent behavior. Also when a supporting runtime environment (or platform) is available, agents show mobility and can move between interconnected platforms that form the system. With these properties agents try to satisfy the expectations of their respective owner actors by using their knowledge and belief bases (derived from the actors). An implementation of a multi-agent system can both be used for simulation purposes (by initializing agents based on reference test data) and as an actual information system by attaching agents to real world actors.This work aims at analyzing a university as a complex social system and creating a corresponding multi-agent model. In the designed model academic and administrative units and students are represented by intelligent and mobile agents that can be implemented using JADE multi-agent system development framework. To provide a satisfactory mobile environment faculties and administrative units are defined to have their own (separate) agent platforms.
Design and development of an ontology-based hazelnut databank for Turkish hazelnut cultivars using crop-specific open dataset
Agriculture is one of the oldest professions in the history, it still plays the main role in economy for almost entire countries around the world. The activities performed by farmers in agriculture domain have been changed and developed over thousands of years. There have been several challenges that agriculture sector is facing like population growing, labor migration, in addition many young people are reluctant to work in the agricultural field. By 2050 population will increase with 2 billion mean while arable land will growth with only 4%, that means lower agricultural yields versus demand, higher crop prices, and increased food security concerns if farmers continue to adopt traditional agricultural methods. The emerging technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) can be used to provide robust solutions for overcoming the challenges in the context of agriculture. In addition, such technologies can help farmers for performing agricultural activities more precisely and for protecting agricultural crops from climate changes in a more effective way. The main objective of this thesis is to develop an integrated databank software system by using two different AI models to classify the Turkish Hazelnut Cultivars. While classifying the cultivars, databank is using ontology-based open data sets in different formats such as RFD/XML and RDF/JSON. Furthermore, an IoT-based device has been developed to classify the cultivars. The databank consists of a web-based and desktop-based applications. The AI models have been developed using .NET ML with the accuracy 90.34% and Python with the accuracy 85.32% respectively.
An analysis of project-based work of freelancers in it sector and development of a platform to facilitate it in Turkey
Project-based work model could be advantageous for both employees and employers. In fact, there are many business benefits of project recruitment: employers can gain access to the best talented and skilled job seekers, and it can save costs and time by project-based hiring rather than full-time recruitment. In addition, employees can also benefit from project-based work by having flexible work schedules, gaining new skills and practical experience. One of the most challenging problems of fresh graduates is finding a job since they have little or no work experience. In that case, working in a project-based role could be advantageous for those inexperienced employees by helping them to get practical experience in their degree subject. This could even lead to a full-time position in the future. Nowadays, most Information Technology (IT) leaders are offering remote roles with flexible project-based opportunities. However, in Turkey, in general, project based work is difficult to arrange. IT employees face difficulties in finding and establishing contracts for project based work. The purpose of this study, as mentioned above, is to examine the current importance of project-based work and the deficiency felt in this field in Turkey, and therefore to develop a project-based job board which connects project owners with the right job seekers for their short or long term IT projects. In order to develop this project-based job platform, SQlite and DB Browser tools were used for database development purposes. The web application development was accomplished by using Django framework and Visual Studio Code as an integrated development environment.
Comparing audio features for speech emotion recognition using machine learning algorithms
Voice is an integral part of our lives. The demand for voice technology in both art and human-machine interaction systems has recently been increased. More information can be transferred quickly by voice. Speech is a natural way of communicating and as a result of this, it is primarily preferred for contacting users in technological areas. Our voice conveys both linguistic and paralinguistic messages in the course of speaking. The paralinguistic part, for example, rhythm and pitch, provides emotional cues to the speaker. Emotions consist of cognitive, physiological and behavioural changes and all these phenomena are interrelated. Generally, an emotion is a state that affects the thoughts and is capable of determining behaviour. Emotion also creates physical and psychological changes. Speech Emotion Recognition topic examines the question 'How is it said?' and an algorithm detects the emotional state of the speaker from an audio record. Within the scope of this study, machine learning models are developed with classification methods to resolve the problem of speech emotion recognition. Voice consists of a lot of characteristics. However, the optimal audio feature set related to the emotional state cannot be determined yet. The main aim in this study is obtaining the most distinctive emotional features. For this purpose, in order to compare audio features based on different domains Root Mean Square Energy (RMSE), Zero Crossing Rate (ZCR), Chroma and Mel Frequency Cepstral Coefficients (MFCC) features are examined for emotion recognition. A pre-trained model namely wav2vec Large which has been developed more recently is used to create the inputs also. Support Vector Machine, Multi-Layer Perceptron and Convolutional Neural Network techniques are utilized for developing learning models for comparing traditional features and the pre-trained model representations. In this paper emotions namely, Happy, Calm, Angry, Boredom, Disgust, Fear, Neutral, Sad and Surprise are classified, and furthermore, the models are trained and tested with English and German speech datasets. When the classification results are examined, it is concluded that the most successful predictions are obtained with the pre-trained representations. The weighted accuracy ratio is 91% for both Convolutional Neural Network and Multilayer Perceptrons models while this ratio is 87% for the Support Vector Machine models. Among the emotional states, Fear has the highest recognition ratio with 95% f-score with Convolutional Neural Network technique which uses a pre-trained model.
The use of pretrained language models in sentiment analysis
Natural language processing is one of the sub-topics of linguistic science and artificial intelligence. Sentiment analysis classifies a text on any topic according to its subjective content. It is one of the methods used to examine, analyze and interpret data such as the thoughts, feelings or attitudes of individuals about a subject on various platforms. The increase in social media shares has also increased the sentiment analysis studies conducted on these platforms. Different methods are used during sentiment analysis. Classification is carried out by sentiment analysis using machine learning and natural language processing algorithms. In recent years, pre-trained language models have been used together with machine learning methods or alone. The aim of this thesis is to test the hypothetical advantages of sentiment analysis in social media comments with pre-trained language models. For this purpose, sentiment analysis was performed for Covid-19 related tweets on Twitter. Emotion intensities were determined using pre-trained language models and the results were compared. BERT, RoBERTa and BERTweet were used in the analysis. The results show that NLP techniques for sentiment analysis are as successful as other techniques.
Interviewster: A chatbot evaluating competency based interviews using transformer models
Recruitment is one of the most verbal and communicative area of Human Resources (HR). This department has many aspects that is open to innovation but also open to the bias, because of the dominance of the human factor. This brings the need (and chance) of many innovation possibilities coming up with the progress in artificial intelligence technologies. Here we will focus on creating a chatbot named as "Interviewster" that welcomes candidates, gathers information (such as name-surname, work status, computer knowledge, education, hobbies), and provides competency-based questions about their past experience and helps them to answer these questions correctly. This chatbot welcomes a candidate and starts the conversation, saves the data collected from the candidate, conducts a competency based interview and decides if candidate has the required competency or not by using natural language processing techniques that utilize neural network architectures and transformer-based technologies. The chatbot is running on the web, coded in python, published to the web by Flask, works on a python core with a Mysql database. In this thesis, interview practices are first introduced, and the methods and applications of competency-based interviews are described. The architecture of the chatbot named as Interviewster is then explained by providing details of the technologies, libraries and machine learning techniques being used. Finally, the results of an evaluation study where the transformer-based models BERT, DeBERTa, and ELECTRA models are applied to the competency-based interview results of real candidates are discussed in detail.
Evaluating cognitive absorption, technology acceptance, and online educational software usability of students in online programming courses
This thesis examines the online self-learning, cognitive absorption, and technology acceptance models of students in online programming courses. This thesis discussed the role and impact of various factors on the learning of programming students. Besides investigating the effect of the educational learning-based online course content of Comp109 Introduction to Programming (Java), Comp110 Object Oriented Programming ve Comp210 Data Structures and Algorithms courses on student success, the effects of students' cognitive absorption, the acceptance, and usability of educational online learning environments were discussed. The subjects of the research are a total of 181 online students in the student group. The researcher carried out the application of the questionnaire tools to the students in the programming courses. Based on the questionnaires, this research aims to examine the effect of students' cognitive absorption, technology acceptance, and online educational software usability on student success in programming courses. In this study, the following scales were applied in the survey measurements, respectively: Cognitive absorption scale (13 items), technology acceptance model (14 items), online educational software usability (39 items). The preparation, ethical approval and implementation of the relevant survey and data analysis research took three months. This study evaluates students' cognitive assimilation, technology acceptance, and e-education software usability scale and suitability for online education in online programming courses. Each of these three scales was examined from seven different perspectives: Gender, Course Code, Department, Age, Expertise, Frequency of Internet use, and Interest in technology. Normality test, ANOVA, Mann-Whitney U Test and Kruskal Wallis Test were used to compare and evaluate these measurements. In addition to these test measurements, the MANOVA test was also applied for multivariate analysis. MANOVA test was carried out in detail from 4 different perspectives. The results of each of these scales were investigated from seven different perspectives and compared with related studies, and suggestions were presented based on the obtained results. In line with my findings, the positive effect of online programming courses on cognitive absorption has been demonstrated. However, it was also shown that there was no significant difference between demographic variables. In addition to these, a literature search was made for all three scales for discussion and a discussion was written. Results are written according to the discussion and analysis.
Predicting the preference of liking using fNIRS and machine learning algorithms
The fMRI method, which is generally used to detect behavioral patterns, draws attention with its expensive and impractical features. On the other hand, the near-infrared spectroscopy (fNIRS) method is less expensive and portable, but it is as effective as fMRI in creating a good prediction model. With this method, a model has been developed that can predict whether a person likes a visual stimulus or not, using various classical machine learning algorithms including Support Vector Machines (SVM), Random Forests, XGBoost, LightGBM and K-Nearest Neighbors (KNN). With implementing tree-based and booster algorithms in addition to SVM and KNN which have been frequently used algorithms in this fNIRS domain, it was aimed to do a complementary comparison in addition to these acknowledged algorithms. Moreover, various missing value imputation methodologies were used to find the best suitable approach for this kind of classification problem. K-Means clustering, which is an unsupervised learning method, was also utilized to cluster similar fNIRS measurements of participants that may improve classification results by one-hot encoding those groups. Furthermore, certain feature extraction and wrapper methodologies were also applied for an attempt to enhance the performance of liking prediction models as a secondary goal. PCA, Isomap and t-SNE methodologies were implemented as feature extraction approaches, and forward selection wrapper design was utilized as an additional step to further development of the model by comparing their scores with each other. Cross-validation F1-scores of these models were used to find out the best model among them. Leave-one-group-out cross validation was exploited in comparison of the models. This meant that these cross-validation results corresponded to each of participants' data i.e. testing every participants' fNIRS measurements alone in each fold. This way both every score specific to each participant could be seen and it ensured models' results were statistically reliable. Following evaluations also included permutation and Wilcoxon Signed-Rank tests to compare each model's performance with each other by testing the statistical significance of those results. Keywords: machine learning, decision-making, optical brain imaging, fnirs, feature extraction, feature selection
Customer transaction predictive modeling via machine learning algorithms
Bu çalışmanın temel amacı faktoring sektöründe aktif olarak faaliyet gösteren bir şirketin müşterilerinin davranışlarını ve özelliklerini saptayabilmek, buna bağlı olarak da müşterilerin geçmiş verilerinden yola çıkarak, keşifçi veri analiziyle ölçülebilir parametreler yakalayabilmek ve akabinde hedefe yönelik tahminsel modellemeler gerçekleştirebilmektir. Sınıflandırma modeli algoritmalarından SVM ve Extra Trees modellerinde %80 seviyesi üzerinde isabet oranı yakalanmıştır. Bu sayede yüksek potansiyel gösteren, yani sorgulattığı çeki, işletme olarak onayladıktan ve teklif yapıldıktan sonra kabul etme ihtimali yüksek olan müşterileri tahmin edilmesi daha etkin, verimli ve doğru yaklaşımlar içerisinde hareket edip aksiyon alarak, işletme bazında işlem hacmine doğrudan katkısı sağlanması amaçlanmıştır.
Segmentation for factoring customers using unsupervised machine learning algorithms
Nowadays the fact that technology facilitates data collection is an important opportunity, as well as making the management of all this data difficult and makes no sense unless it is well processed. This stored data is extremely important, and companies use data provided by their customers. Catching the needs of the customer profiles of the changing world is now a necessity and takes the first place for companies. With the increase in the amount of stored data over time, it has become difficult to establish a relationship between the data and to separate them from each other. At this point, machine learning methods have become more involved in our lives. In this study, what segmentation is and its change over the years are mentioned. It has been mentioned which machine learning techniques will be useful in data selection. Then, possible machine learning methods are shown using the local factoring company's customer check data. Since this study aims to group unlabeled data, unsupervised learning techniques are emphasized. Among these methods, Hierarchical Clustering, DBSCAN, Gaussian Mixed Modeling methods, Fuzzy c - Means were used besides the most popular K-Means. The success criteria for each algorithm were examined and the appropriate cluster numbers were found, and the results were measured. When the clustering outcomes were examined, the optimal number of clusters was calculated very high with GMM, DBSCAN could not assign clusters, and Hierarchical clustering has been found to be very costly in terms of time. It was observed that the best results were obtained with the K - Means and FCM.
Fraud detection and prediction with machine learning applications
The main purpose of this study is to determine the fraudulent activities on transactions of the customers of a company that is active in the factoring sector, and accordingly, to capture measurable parameters with exploratory data analysis based on the historical transaction and connection data of the customers, and then to perform predictive models for the target. A hit rate of around 79% was achieved in XGBoost and CATBoost models, which are classification model algorithms. In this way, it is aimed to directly detect fraudulent activities on a trasnaction basis by acting in a more effective, efficient and correct approach after detecting the customer that shows high potential to make fraud.
Customer churn prediction for the Pay-TV sector
Understanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset.
A web of things system to control party equipment
This thesis focuses on the equipment needed to throw a successful party and how to coordinate and synchronize these components seamlessly. It also explores the integration of the Web of Things (WoT) paradigm into party organization to improve coordination and communication between entertainment devices. The research aims to provide a detailed description of the necessary equipment and presents innovative applications of the WoT paradigm to enhance the coordination and synchronization of entertainment elements. By doing so, this thesis contributes to the evolving field of WoT and its potential applications in dynamic and interactive environments such as parties.
Comparing the effectiveness of graph neural networks and machine learning algorithms for fNIRS-based neuromarketing research
Functional near-infrared spectroscopy (fNIRS) has some advantages over other brain imaging methods in terms of cost and portability. For this reason, its use in neuromarketing is increasing. However, fNIRS brings some challenges along with its advantages. Due to features such as multichannel measurement and high temporal resolution, the nature of fNIRS data is complex and multidimensional [7]. Neuromarketing researchers have utilized machine learning algorithms to overcome these challenges. When these studies are analyzed, it is seen that successful results have emerged. Machine learning has influenced researchers working on graphs as well as neuromarketing researchers. Thus, graph neural networks have emerged, which allow the application of artificial neural networks to graph data structures. Thanks to the fact that the brain can be modeled as a graph structure using functional connections [14] and the high temporal resolution of fNIRS [7], there are neuroimaging studies using graph neural networks and fNIRS together. However, despite successful results, there is no neuromarketing research using this combination. Therefore, in this study, the performance of graph neural networks in fNIRS-based neuromarketing was analyzed and compared with machine learning algorithms that have been shown to yield successful results in this context. For the comparison, fNIRS measurements of a neuromarketing experiment conducted to determine perceptions toward brands were used. In the experiment, consumers were asked to decide (yes/no) whether the adjective shown with the brand logo was appropriate for the brand. The data set was obtained by cleaning the obtained measurements. First, a supervised machine learning approach was applied to this dataset. After the dataset went through several data preprocessing stages, various algorithms were trained on it. These were K-Nearest Neighbors, Support Vector Machines, Random Forest, Naive Bayes, and XGBoost algorithms. Then, two different voting classifiers, one for soft voting and one for hard voting, were created from the algorithms that were more successful than the others. After the machine learning approach was completed, the graph neural network approach was applied. The data obtained through fNIRS was transformed into a graph structure using functional connections in the brain. The Pearson correlation coefficient was used to calculate the functional connections. Since a graph was created for each trial of the participants and each graph had a label (yes/no), classification was performed at the graph level. For graph classification, the generated graphs were given as input to graph neural network architectures. The architectures used in the study consisted of Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network. Finally, a soft voting classifier was created by combining these architectures. Test accuracy values of all methods were calculated and binomial confidence intervals were added to these values. The comparison results showed that machine learning algorithms generally outperform graph neural networks. Additionally, machine learning models based on ensemble learning have the best scores.
Customer churn prediction for a personal care product retail chain operating in Turkey
Understanding the reasons for customer loss and the customer behaviors leading to it, as well as being able to predict customer's loyalty to an industry or a company provides enormous advantages in retaining existing customers and avoiding revenue loss due to the marketing and advertising costs associated with attracting new customers. In this study, the 29-month data from a personal care product retail chain operating in Turkey was used, and because of the imbalanced values and non-customer entries of the dataset, the oversampling method and synthetic sampling was applied. During the model development phase, Logistic Regression, Decision Tree, K-Nearest Neighbors, Random Forest, Extra Trees Classifier, and MLP (Multi-Layer Perceptron) Classifier were applied, and their performances were evaluated using metrics such as accuracy, recall, F1-score, precision, and confusion matrix. Based on these comparisons, it was observed that the Random Forest and MLP Classifier models demonstrated the best performances for this dataset, while other tree-based algorithms, such as the Extra Trees Classifier and Decision Tree, achieved slightly lower but comparable performance.
Customer-merchant shopping behavior modeling in e-commerce
The rapid advancement of e-commerce has necessitated the adoption of sophisticated analytical models to decipher customer behaviors and evaluate seller performance. In this study, we propose a novel 'Customer-Seller Shopping Behavior Model' that integrates customer preferences, purchasing patterns, and seller product offerings. The model utilizes customer metrics such as RFM (Recency, Frequency, Monetary), category preferences, and customer lifetime value, alongside seller data on category focus and sales performance, to facilitate segmentation and personalized recommendations. Employing K-Means, hierarchical clustering, and decision tree algorithms, customers and sellers are categorized based on their behavioral similarities. Furthermore, predictive models and hybrid recommendation systems are implemented to forecast customer purchasing propensities and match them with suitable sellers. The results demonstrate that the proposed model significantly enhances customer engagement, seller effectiveness, and overall platform efficiency. This research contributes to a data-driven, scalable, and dynamic analytical framework for e-commerce platforms.
A two-tower retrieval approach for personalized product recommendations in cosmetic e-commerce
Recommender systems play a vital role in enhancing user experience and driving engagement in e-commerce platforms. This thesis presents a comparative analysis of two personalized product recommendation approaches using retrieval- based neural architectures implemented with TensorFlow Recommenders (TFRS). The first approach relies solely on ID-based embeddings using user and product identifiers. The second model enriches product representations by incorporating semantic content features, specifically product title and upper category information. Both models are trained using implicit purchase data and evaluated with the FactorizedTopK metric, which estimates recall@K through nearest-neighbor retrieval. The study assumes a warm-start scenario and emphasizes the value of semantic context in embedding space for product retrieval. Our findings highlight the benefit of including content features for improving recommendation accuracy, particularly in contexts where semantic similarity between products is relevant.
Deep learning for predicting soil properties and climate change
Soil is a fundamental natural resource that not only provides essential nutrients, minerals, and elements necessary for plant growth but also plays a vital role in climate regulation and broader ecosystem functioning. This master's thesis aims to improve the practises of modern Artificial Intelligence (AI) methodologies in Digital Soil Mapping (DSM) practices across Türkiye by integrating key environmental variables such as land cover, topography, climate data, and other spatial factors. To achieve this objective, a comparative analysis of seven different machine learning models was conducted to evaluate their performance in predicting soil properties based on these variables.
ESG scoring through digital transformation: Quantifying sustainability with machine learning
This study aims to systematically analyze the influence of Environmental, Social, and Governance (ESG) scores, particularly those attributed to subsidiaries of large firms, on the investment decisions of funds. The central objective is to examine the role that ESG scores play in shaping fund portfolios and investment strategies, especially in relation to risk management, sustainability, and long-term financial performance. The increasing importance of ESG metrics has shifted the decision-making processes of fund managers, requiring an in-depth evaluation of how companies with ESG scores are incorporated into investment portfolios. This research will explore the specific contributions of subsidiaries within larger corporate entities that possess ESG scores, investigating their role in the overall valuation of funds. By focusing on the subsidiaries, the study will highlight how the sustainability performance of individual business units influences the broader investment landscape. The significance of ESG scores for risk mitigation, alignment with sustainability objectives, and maximizing long-term returns will be central to this analysis. Furthermore, the study will assess the potential risks associated with investing in companies that lack ESG scores and examine how the absence of such scores impacts investment strategies. A key component of this research will be the examination of the decision-making criteria employed by fund managers when evaluating companies with and without ESG scores. It will explore how these scores influence portfolio composition and investment prioritization. The study will also address the broader implications for sustainability in the financial sector, particularly how ESG considerations intersect with traditional financial performance metrics. Ultimately, this research aims to provide a comprehensive understanding of the role ESG scores play in shaping the investment strategies of funds. By focusing on the subsidiaries of large firms, this study will contribute to the growing body of knowledge on ESG investing, offering insights into how these metrics affect fund performance, decision-making processes, and the long-term success of investment portfolios.
Anayasa Mahkemesi Kararlarının Simülasyonu: Türk Bireysel Başvuruları İçin Çok Etmenli Bir Büyük Dil Modeli (LLM) Çerçevesi
This research examines the ability of large language models (LLMs) to emulate judicial decision-making in constitutional court cases. We used three cutting-edge models—GPT-5, Gemini, and Claude—to look at 343 decisions made by the Turkish Constitutional Court between 2014 and 2024. We did this using a two-stage evaluation framework that mirrored how courts really work. The study evaluated model efficacy in both admissibility determinations and substantive rights infringement judgments. During the admissibility stage (Stage 1), the accuracy rates varied from 68.80% for Claude to 81.34% for GPT-5, with majority voting achieving 79.59%. GPT-5 had the fewest total mistakes and a balanced approach, while Gemini and Claude were more likely to think that something was not admissible. On the other hand, courts had a more moderate acceptance rate. In Stage 2, when rights were violated, all three models had the same accuracy of 81.50%. However, majority voting did better, with an accuracy of 83.24%. In this case, GPT-5 tended to have partial matches, Gemini had the most exact matches, and Claude was in the middle, showing that each had different strengths in legal reasoning. Patterns of inter-model agreement showed that there was a lot of convergence, but it wasn't always the same. In Stage 1, unanimous agreements, though less common, had the highest accuracy (87.32%), showing that consensus decisions are reliable. In Stage 2, GPT-5 and Claude were the most in line with each other (88.52%). These results indicate that ensemble methods and hybrid human–AI approaches could improve the consistency and robustness of judicial decision-making. The results show that even general-purpose LLMs can understand complicated constitutional principles and come up with structured, court-like reasoning that is serious enough to be used in legal situations. Although existing models struggle to replicate the comprehensive intricacies of judicial reasoning, the persistent superiority of ensemble methodologies suggests that specialized legal AI systems may exceed general models, potentially revolutionizing constitutional jurisprudence by improving efficiency, consistency, and accessibility to justice
Image classification of customs procedure documents using machine learning and deep learning models
The images of documents used in customs procedures were classified using machine learning and deep learning models. As image classification methods, Gaussian Naive Bayes, Random Forest, and Support Vector Machine were used in the machine learning field, and Convolutional Neural Network, Vision Transformer, ConvNext and EfficientNetV2 were used in the deep learning field. Datasets were organized specifically for each model type, and models were created with parameter values determined to ensure the models worked at the optimum level. Training and testing were conducted with the relevant datasets. The models were compared in terms of training times and accuracy rates. The best results were achieved with Convolutional Neural Network, which had a training time of approximately 11 minutes and an accuracy rate of 98,32%; second-best accuracy result achieved with Random Forest, which had an accuracy rate of 97,94% and second-best overall results achieved with EfficientNetV2, which had a training time of approximately 22 minutes and an accuracy rate of 95,89%.
Enhancing intrusion detection with privacy-preserving federated learning: Differential privacy and incremental learning integrating
In cybersecurity, Intrusion detection systems (IDS) are crucial in scanning networks and systems to detect malicious activities and help to identify the threats before sensitive data is compromised. The introduction of machine learning (ML) has enhanced IDS by providing mechanisms for automated and intelligent threat detection. However, the training of ML models in such distributed settings, e.g., in Federated Learning (FL), may be an issue for training and may still expose sensitive information through model parameters analysis. FL mitigates certain privacy issues by localizing the data, however, it is not sufficient for genuine privacy protection and requires improvement. Specifically, Incremental Learning (IL) improves IDS providing the ability models adjusting to new cybersecurity threats without the need for retraining from scratch. This keeps it computationally inexpensive and allows it to quickly adapt to novel attack behaviours. In particular, we propose Federated Differential Privacy Enhanced Model Aggregation, a method targeted towards enhancing both privacy and accuracy in federated ML context. It uses a server-client architecture, where a global model is initialized and further improved with client-side training, while updates are securely aggregated. We also used a multi-layer perceptron trained with the DP-SGD optimizer which drops noise into the gradients to achieve better data privacy. We evaluated the performance, the experimental results show that the accuracy of class incremental learning in our proposed approach can reach 92.4% and feature incremental learning can reach 99.4%, they demonstrate that the learning can match the new data well. The procedure remains privacy-preserving and efficient and has good performance over diverse dataset types, so we believe it to be a valid candidate for intrusion detection systems (IDS) in modernity.
Klasik soru değerlendirme karar destek sistemi
Bu tez çalışmasında, öğreticilerin klasik soru değerlendirme süreçlerinde kullanmaları için not önerisinde bulunacak bir karar destek sistemi geliştirilmiştir. Bu çalışmanın amacı öğreticilerin kendi not değerlendirmeleri ile sistemin not değerlendirmeleri arasında karşılaştırma yaparak adil bir değerlendirme süreci ortaya çıkarmalarına yardımcı olmaktır. Bu çalışmada klasik soru cevaplarını oluşturan cümlelerin kelimelere ve bu kelimelerin köklerine ayrılması için, aynı zamanda kelime yazımlarının denetlenmesi ve olası düzeltmeler için Zemberek DDİ Kütüphanesi kullanılmıştır. Her bir klasik sorunun doğru cevabı ve öğrenci tarafından verilen cevabın benzerliğini hesaplamak ve buna göre önerilecek notun hesaplamasını yapmak için ise Vektör Uzay modeli ve Kosinüs Benzerliği Teoremi kullanılmıştır. Geliştirilen algoritma sonuçlarının bilimsel geçerliliği, öğreticinin not değerlendirmesi ve sistemin not değerlendirmesi arasındaki ilgileşim (korelasyon) hesaplanarak ispatlanmıştır. Korelasyon hesaplamaları için Pearson's Korelasyon katsayısı (Pearson's Correlation) ve T-test yöntemleri kullanılmıştır. Yapılan analizler sonucunda Pearson's Korelasyon katsayısı 0,667 olarak bulunmuştur. T-test analizi sonucunda da Pearson's Korelasyon katsayısı hesaplamaları ile birebir benzer sonuçlar elde edilmiştir. Bu sonuçlar sistemin not değerlendirmeleri ve öğreticinin not değerlendirmeleri arasında güçlü bir korelasyon olduğunu göstermiştir.
Dikkat seviyesinin gerçek zamanda izlenmesi; bir web uygulaması
Dikkat seviyesinin ölçüldüğü ve izlendiği bu tez kapsamında; EEG ölçümleri Emotiv Epoc Headset ile yapılmış ve veriler gerçek zamanlı olarak veritabanına kaydedilmiştir. Kaydedilen frekans verileri FFT (Fast Fourier Transform) yöntemi ile filtrelenmiş ve bu filtreleme yöntemi bizi, elde etmek istediğimiz beta, teta, alfa, delta v.b. frekans aralıklarına ulaştırmıştır. Çalıştırdığımız bu filtre ayrıca, kişinin kullandığı bilgisayar üzerinde yüklü olan küçük bir uygulamanın içinde mevcuttur. Sahip olduğu bu filtre, kişinin duygu tipi ve stres düzeyini belirleyebilen aynı zamanda bilimsel normlara göre veri analizi yaparak kişinin, dikkat seviyesi gerçek zamanlı olarak ölçülebilir hale getirilmiştir. Ayrıca ölçülen frekansların ve alınan verilerin web arayüzünün yönetim paneli üzerinden, zamana bazlı frekans bilgilerine ulaşabildiğimiz ve grafik olarak da gösterebildiğimiz yönetici sayfası bulunmaktadır. Bu sayfadan her kullanıcının stres düzeyi ve duygu tipini öğrenebildiğimiz gibi, acil bir durumda kişiye hızlı bir şekilde müdahale etmemizi sağlayabilmektedir.
Optimizing network performance rough image processing techniques in omputer science it
A fundamental apparatus for exploring digital protection risks in Internet of Things (IoT) networks is the Botnet of Things (BoT-IoT) dataset. This examination proposes an original way to deal with order BoT-IoT information by changing over the crude information into RGB images and afterward applying calculations, for example, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Irregular Woodland to a helpful classification model. The BoT-IoT dataset is at first changed over into RGB images, where each component vector addresses a pixel in the image. To protect the spatial data of the first information, these photographs are in this manner saved in the.png design. We might take benefit of the strong capacities of example acknowledgment and image regulation procedures for IoT information examination with this change interaction. The RGB images delivered from the BoT-IoT dataset were then sorted utilizing an aggregate classification method. The ensemble includes the most striking Irregular Woodland classifiers, KNN, and SVM, every one of which contributes unmistakable qualities to the general classification issue. To show up at the last classification choice, the estimates of discrete classifiers are shared utilizing a weighted democratic system. The recommended strategy is compelling, as proven by trial discoveries on the BoT-IoT dataset, where the ensemble model accomplishes a great exactness of generally 99.36%. What's more, the model performs well with regards to precision, review, and F1-score, exhibiting its adequacy in isolating noxious action from harmless movement in Internet of Things networks. Inside and out review was likewise finished to explain every classifier's commitment inside the ensemble setting. This examination uncovers the fitting properties of the classifiers: Arbitrary Timberland consolidates the limit of ensemble learning for upgraded speculation, KNN makes utilization of nearby neighbourhood data, and SVM succeeds at dealing with testing choice limits. In any case, this work offers a new way to deal with BoT-IoT information recording using ensemble learning strategies and RGB image models. The extraordinary exactness achieved features this strategy's capability to improve digital protection in Internet of Things networks. Through effective distinguishing proof and alleviation of pernicious activities, the proposed approach adds to the advancement of IoT security exploration and safeguards networked frameworks from arising digital threats.
Discovering sustainable ecosystem with green iot
Internet of things (IoT) is one of the most extensively used concepts that enables large numbers of connected devices to obtain data, analyse it, and utilize it. However, the increased connectivity of objects and devices in IoT brings serious environmental problems, namely those connected with power consumption and e-waste generation. This paper aims to examine the possibility of "Green IoT" which encompasses using efficient energy technologies, recycled/reusable materials, and green energy to reduce the adverse effects IoT could cause to the environment. Case studies and real-world applications are used in the research where energy-efficient designs of Green IoT technologies are studied to show that Green IoT technologies can drastically minimize energy use, control emissions, and promote a more sustainable IoT environment. Several papers have been studied and some of the areas of contribution include enabling technologies which include Green Radio Frequency Identification (RFID), energy harvester devices, low power communication protocol, all of which enable improvement of energy efficiency in the IoT systems across smart cities, healthcare, and industrial automation. Furthermore, this paper outlines key issues arising from the application of Green IoT, including lack of protocol coordination and ineffective policy measures. The results confirm Green IoT as a viable solution regarding concerns over the lack of sustainability in IoT interventions and provide potential directions and strategies for policy makers and executives on how to create a less damaging footprint of IoT while making the most of the opportunities such technologies offer for the improvement of the quality of life and the creation of sustainable development.
Meslek liselerinde okul aile işbirliğini arttırmada bilgi teknolojisi kullanmanın etkisi: Karşılaştırmalı bir araştırma
Bu araştırmada amaç, meslek liselerinde okul aile işbirliğini güçlendirmede bilgi teknolojisi kullanımının katkısını incelemektir. Araştırmada elde edilen veriler SPSS 21 paket programında, frekans, yüzde analizi, ön test ve son test teknikleri ile analiz edilmiştir. Araştırma sonucunda; okul aile işbirliğini güçlendirmede bilgi teknolojisi kullanımının öğretmen, veli, okul yönetimi ve öğrenci arasında pozitif yönde bir katkısı olduğu; özellikle velilerdeki okul yönetime karşı olan önyargıları ortadan kaldırdığı ortaya çıkmıştır.
Sınıf yönetimi için bir sosyal ağ uygulaması geliştirilmesi
Bu çalışmanın amacı sınıf yönetiminde kullanılmak üzere bir sosyal ağ sitesi geliştirilmesi ve ardından bir grup öğrenci tarafından kullanılması sağlanarak eğitsel kullanımının ve kullanışlılığının test edilmesidir. Çalışma kapsamında www.mavikurdele.net adresinden erişilebilen, öğrenci, öğretmen ve velilere yönelik bir eğitsel sosyal ağ sitesi geliştirilmiştir. Geliştirilen sosyal ağ öğrenme yönetim sistemlerinde yer alan ödevler, sınavlar, dosya paylaşımı özelliklerinin yanı sıra sosyal ağların karakteristik özelliklerini de bünyesinde taşımaktadır. Geliştirme sürecinin sonunda eğitsel sosyal ağ 2013-2014 eğitim öğretim yılı 2. Döneminde Uşak ilinde bir ortaöğretim kurumundaki 51 öğrenci ile deneysel desenlerden tek grup son test modelinde 6 hafta süre ile test edilmiştir. Çalışma sonrasında veriler sosyal ağların eğitsel kullanım ölçeği, sosyal ağ kullanışlılık anketi ve eğitsel sosyal ağ log verileri aracılığı ile toplanmıştır. Verilerin incelenmesinde betimsel istatistiklerden yararlanılmıştır. Çalışma sonucunda geliştirilen eğitsel sosyal ağ sitesini kullanan öğrenciler eğitsel sosyal ağın iletişim, işbirliği ve kaynak ve materyal paylaşımı konusunda kullanılabileceğini, kullanımının kolay olduğunu ve tasarım olarak kendi yaş gruplarına uygun olduğu görüşünü belirtmişlerdir.
Mobil cihazların (tablet pc)eğitim/öğretime etkisinin belirlenmesi
Bu araştırmada Tablet PC'lerin Eğitim/Öğretime etkisinin belirlenmesi amaçlanmaktadır. Bu amaç çerçevesinde oluşturulan ölçekler yardımıyla veri toplama işlemleri gerçekleştirilmiştir. Tablet PC'lerin eğitim öğretime etkisinin araştırılması ile günümüzde hızla yaygınlaşan mobil cihazların eğitim sistemine dahil edilmesi yoluyla, bilimsel ve toplumsal açıdan öğrencilerin okula, eğitim ve öğretime olan ilgisinin artacağı düşünülmüştür. Bu araştırmanın mobil cihazların eğitimde kullanılması bağlamında öğretmen-öğrenci, öğrenci-öğretmen, öğrenci-öğrenci, öğretmen-öğretmen arasında etkileşim ve dayanışma kültürünün oluşturulması açısından yarar sağlaması amaçlanmaktadır. Araştırmada dört adet ölçek, Amasya il merkezinde Tablet PC dağıtımı yapılmış olan Amasya Anadolu Lisesi ve Amasya Anadolu İmam Hatip Lisesi'nde öğrenim gören 10'uncu sınıf öğrencilerine uygulanmıştır. Tutum ölçeği, "Tablet PC'nin Derslerde Kullanımına Yönelik Öğrenci Tutumlarını" ölçmek amacıyla, Tablet PC kullanan 216 öğrenciye uygulanmıştır. Tutum ölçekleri sonucuna göre araştırmaya katılan lise öğrencileri genel olarak Tablet PC'nin derslerde kullanılıp kullanılmaması konusunda kararsız görüş belirtmişlerdir. Gözlem formları ve başarı testleri, deney grubu (Tablet PC'li) ve kontrol grubu (Tablet PC'siz) olarak ayrıştırılan sınıflara ayrı ayrı uygulanarak iki grup arasındaki fark bulunmaya çalışılmıştır. Gözlem formu, deney ve kontrol gruplarında sözel derslerden seçilen "Dil Anlatım" ve sayısal derslerden seçilen "Matematik" derslerinde belirtilen konu ve üniteler ders öğretmeni tarafından anlatılırken araştırmacı tarafından doldurulmuştur. Gözlem formu sonuçlarına göre, deney grubundaki (Tablet PC'li) ders anlatım sürelerinin, kontrol grubundaki (Tablet PC'siz) ders anlatım sürelerinden kısa olduğu belirlenmiştir. Başarı testleri deney ve kontrol gruplarına, "Dil Anlatım" ve "Matematik" derslerinde konu anlatımları yapılmadan önce Ön-Test olarak uygulanmış, konu anlatımları yapıldıktan sonra Son-Test olarak uygulanmıştır. Başarı testleri sonuçlarına göre deney grubundaki (Tablet PC'li) Ön-Test ve Son-Testler arasındaki farklar, kontrol grubundaki (Tablet PC'siz) Ön-Test ve Son-Testler arasındaki farklardan önemsenmeyecek derecede fazladır.
Siber suçlar üzerine bir araştırma afyonkarahisar örneği
Bilgi iletişim teknolojilerinin ve bu teknolojilere erişilebilirliğin her geçen gün artmasına paralel olarak bu sistemlere yönelik suç türleri de artmaktadır. Sanal bir ortamda paylaşılan bilgiler bilinçsiz veya yetersiz güvenlik adımları uygulamalarıyla kötü niyetli kullanıcıların eline geçebilmektedir. İnternet kullanıcıları bazı mağduriyetler yaşayarak bilmeden adli bir konunun tarafı olabilmektedir. Bu noktada siber suçun insanlar tarafından nasıl algılandığı son derece önemlidir. Bu çalışmada Afyonkarahisar ölçeğinde lise öğrencilerinden yükseköğretim öğrencilerine ve kamu çalışanlarına kadar siber suç algısının temel düzeyde ölçümü yapılmıştır. Afyonkarahisar örnekleminde kamu çalışanları ile öğrenim gören öğrencilere yönelik internet kullanım alışkanlıklarının tespiti, siber (bilişim) suça ilişkin görüşlerinin tesbiti ve suç farkındalıklarının ortaya konması amacıyla bir anket çalışması yapılmıştır. Anket sonuçları yorumlanarak katılımcıların internet kullanım araçları, amaçları, alışkanlıkları, siber suç bilgileri, tehdit algıları ve farkındalıkları, internet ortamında bireysel özgürlüklerin kullanımı ve korunması, güvenlik uygulamalarına yaklaşımları ve duyarlılıkları hakkında bir düzey ölçümü yapılmıştır.
Üniversitelerde bilişim teknolojileri hizmet yönetimi için bilgi teknolojileri altyapı kütüphanesi (ITIL) kullanımı
Bilgi Teknolojileri Altyapı Kütüphanesi olan ITIL, dünyada en yaygın olarak benimsenmiş ve kullanılan Bilgi Teknolojileri (IT) servis yönetimi yaklaşımıdır. ITIL kullanımı hızla yaygınlaşmaktadır. Teknoloji, perakendecilik, eğitim, finans, üretim gibi birçok sektörde bilişim teknolojilerini etkin ve verimli bir biçimde yönetmek için ITIL kullanılmaktadır. ITIL'ın uygulanması organizasyonlar için kritik bir süreçtir. Bu çalışmada, Afyon Kocatepe Üniversitesi bünyesinde ITIL'ın uygulanabilirliği ve bilgi teknolojileri hizmetini alan akademik personel ile idari personelin kullanıcı görüşlerini almak amacıyla bir anket çalışması yapılmıştır. Bu anket çalışması ile bilgi teknolojisi hizmetinin sunumundaki ihtiyaçlar belirlenerek organizasyonun ITIL sürecindeki konumu belirlenmiş ve ITIL'ın kuruma sağlayacağı faydalar gösterilmeye çalışılmıştır. Çalışmamızda "ITIL konusunda neredeyiz, ITIL'a göre hizmetler nasıl sunulur?" sorularına cevap aranmıştır. Elde edilen anket verilerinden öncelikli olarak hangi ITIL süreçlerine ihtiyaç duyulduğu tesbit edilmiştir. Çalışmada ayrıca ITIL uygulamak için kavramsal bir yol haritası da hazırlanmıştır. Bu çalışma ITIL'a nasıl başlanacağına dair bir kaynak oluşturmaktadır.
Phishing website detection based on a novel artificial intelligence technique
Due to the increasing use of Internet, researchers widely study to prevent cybercrimes. One of the cybercrimes is phishing websites. As cybercriminals employ increasingly sophisticated tactics, there is a growing need for innovative approaches to detect and prevent these threats. In this thesis, the time-varying mirrored S-shaped transfer function is applied for BGWO, BPSO, and BHHO, and the proposed models are performed on the phishing websites dataset. The proposed models have promising results on the phishing websites dataset
Şarkı sözlerinden yeni şarkı türetme: Derin öğrenme yöntemleri ile sanatsal üretim
Şarkı sözü yazımı yaratıcı bir süreçtir. Geleneksel olarak bu süreç ilham, sezgi ve bireysel yaratıcılığa dayanır. Ancak yapay zekâ teknolojilerinin gelişmesiyle birlikte bu süreç yeni bir döneme girmiştir. Son yıllarda, özellikle yaratıcı metin üretimi alanında, Doğal Dil İşleme (NLP) kapsamında önemli ilerlemeler kaydedilmiştir. Özellikle GAN mimarileri, özgünlük ve rastlantısallık gerektiren içeriklerin üretiminde dikkat çekmektedir. Bununla birlikte, GAN'ların metin üretimine uyarlanması, görüntü üretimine kıyasla daha karmaşık bir süreçtir. Türkçe gibi sondan eklemeli dillerde hem anlamlı hem de yapısal olarak tutarlı metinler üretmek daha da zordur. TextGAN, SeqGAN, LeakGAN, RankGAN, MaliGAN ve RelGAN gibi mimariler, Türkçe şarkı sözü üretimi bağlamında sistematik olarak değerlendirilmemiştir. Bu durum, çalışmanın temel problemini oluşturmaktadır. Bu tez çalışmasının amacı, mevcut şarkı sözlerini analiz ederek, özgün ve sanatsal değeri yüksek içerikler üretebilen en uygun yapay zekâ modelini belirlemektir. Bu kapsamda, derin öğrenme modellerinin yanı sıra duygu analizi, ritmik yapı analizi ve yaratıcı metin üretimi teknikleri kullanılmıştır. Elde edilen sonuçlara göre, düşük BLEU ve Jaccard skorları, modellerin referans metinlerle yüzeysel düzeyde benzerlik kurmakta zorlandığını göstermektedir. Buna karşılık, RelGAN modeli Cosine Similarity metriğinde en yüksek skoru (0.0693) elde ederek, anlamsal olarak en tutarlı çıktıları üretmiştir. MaliGAN modeli ise düşük perplexity değeri (9588.23) ile dil modelleme açısından öne çıkmıştır. Ancak, çeşitlilik metriklerinin (TTR ve Unique Word Ratio) 1.0 değerine ulaşması, anlamlı tekrarların eksikliğini ve doğal akışın zayıf olduğunu göstermektedir. Edebi analizlere göre, yalnızca RelGAN modeli tematik bütünlük ve duygu yoğunluğu açısından tatmin edici şarkı sözleri üretebilmiştir. Sonuç olarak sayısal başarı ve edebi değerlendirme birlikte ele alındığında, RelGAN en dengeli ve başarılı model olarak öne çıkmıştır. Türkçe gibi eklemeli yapıya sahip dillerde şarkı üretimi için GAN modelleri hâlen sınırlı kalmakta olup, gelecekte Transformer tabanlı yapılarla desteklenmesi önerilmektedir.
Renkli görüntüler için kriptografik şema
Due to the extensive use of multimedia applications, image coding standards are one of the ways that supported multimedia spreading. Internet is full of digital documents that are uploaded by thousands every second through social media or any websites for commercials or business. Encryption and decryption techniques are required for digital multimedia distribution. Today, privacy and authentication services are crucial for data dissemination through the Internet. This work focuses on image security integrating encryption with multimedia compression systems. Also, image encryption algorithms combining with JPEG encoding are also proposed. Three approaches for integrating encryption with image compression system are proposed. These are Color Plane Permutation, Discrete Cosine Transform (DCT) coefficients, and sign encryption of DCT coefficients. The proposed approaches are supported by selective encryption, are a portion of the coefficients from either the final results or intermediate steps of a compression system. The securities of the proposed approaches are also analyzed. Simulations results show that the proposed system is secure, low cost, and compatible with direct bit-rate control. These properties make the study suitable for multimedia applications with the real-time operation and image transmission.
Kablosuz sensör ağlarında saldırı tespiti
Given the possibility of using intelligent agents for the development of intrusion detection systems, obtained through a bibliographic survey, this work will first seek to develop the following hypothesis: The use of intelligent agents in the development of intrusion detection and prevention solutions in wireless sensor networks is quite suitable for such equipment, as they favor the development of solutions. In addition, the use of intelligent agents allows a better development of these systems, to provide satisfactory computational performance, adaptability, scalability, shorter time latency and load balancing. The general objective of this paper is to propose a framework, based on agents, for detecting intrusion in wireless sensor networks, with the best possible adaptation resources of network devices
Bireyleri, kuruluşları ve toplumun davranış kalıplarını verilerin analiz edilmesi ve jeosuzayal bilgilere dayanarak çözümler bulması için güçlendirecek büyük veri odaklı elektronik devlet
In the information era, it is more crucial than ever to use big data effectively in order to increase the efficiency, accountability, and openness of electronic government (e-Government). This essay investigates the revolutionary potential of big data-driven e-Government in altering societal behavioral patterns in Turkey. We aim to provide examples of how e-Government may employ these technologies to improve public services, inform decision-making, and actively engage citizens in governmental activities. Modern big data analytics, machine learning techniques, and predictive models are used to achieve this. This research examines the various aspects of big data applications in Turkish e-Government, such as how they support tailored public services, efficient resource management, sound policy-making, and effective citizen participation. We go into more detail on how these applications affect people's and organizations' behavioral habits. The findings indicate that big data-driven e-Government holds great promise for rethinking the citizen-government interaction model and fostering a society that is better data-informed in Turkey. As a result, this might promote organizational performance, foster the development of new social norms in communities, and make it possible for individuals to participate actively and meaningfully in governance in Turkey. The study emphasizes how careful management and ethical use of big data in e-Government can contribute to building a more inclusive, transparent, and ABSTRACT BIG DATA-DRIVEN ELECTRONIC GOVERNMENT TO EMPOWER INDIVIDUALS, ORGANIZATIONS AND SOCIETY BEHAVIORAL PATTERNS TO ANALYZING DATA AND FINDING SOLUTIONS BASED ON THE GEOSPATIAL INFORMATION ALI, Ali Hussein Ali M.S., Electrical and Computer Engineering, Altınbaş University, Supervisor: Asst. Prof. Dr. Sefer Kurnaz Date: June / 2024 Pages: 54 viii participatory government while being aware of the challenges that are inevitably there in Turkey.
Akıllı araç görüşünde derin evrişimli sinir ağı kullanılarak şerit segmentasyonu ve yol tespiti
This thesis will delve into the intricacies of designing, training, and evaluating a deep CNN architecture for lane segmentation and subsequently harness the discriminative power of SVMs for road detection. Through a comprehensive investigation of these methodologies, this thesis aims to contribute to the advancement of smart car vision systems, ultimately paving the way for safer and more reliable autonomous driving solutions in an ever-evolving urban landscape. As society becomes increasingly reliant on digital communication and data, the ability to understand, process, and generate human language has become a critical component in various applications, from virtual assistants and sentiment analysis to machine translation and content recommendation systems.
Değiştirilmiş KNN algoritmasını ve özellik ağırlıklandırmayı kullanarak DNS trafiği tabanlı izinsiz giriş tespiti
All computer networks can be the target of attacks, from the smallest, such as home networks, to the largest and most complex, such as corporate networks. To make the situation even worse, programs can easily be obtained through the Internet (or by other means), and then used for malicious actions, making any inexperienced person have the same skills as a professional It is necessary to propose a new database that contains a portion of the traffic that is present on an existing actual network environment in order to carry out testing in a real environment. This is necessary in order to accomplish the goal of conducting testing in a real environment The main objective is to create a network user control mechanism in order to recognize the user's legitimacy through their actions.
Öğrenci devamının kaydedilmesi ve tanınması derin öğrenmeyi kullanan yüzleri
There are several methods available to monitor student attendance in classes, such as biometric, radiofrequency, face recognition, and paper-based systems. However, the face recognition-based approach has been found to be both efficient and secure. In this study, a threshold to confidence has been implemented through Euclidean distance values to enhance the identification process. The Local Binary Pattern Histogram (LBPH) algorithm has been utilized for this purpose, as it has been demonstrated to be more effective than other methods such as Eigenfaces and Fisher faces. The Haar cascade method has been used for facial detection due to its robustness. The system's performance has been assessed in various scenarios, including recognition rates, false-positive rates, and detecting unknown individuals with or without a threshold. The system has demonstrated an impressive 79% recognition rate for students, with a 24% false-positive rate, and can identify students wearing glasses or a beard. The LBPH algorithm and Haar Cascade method contribute to the system's exceptional performance. The recognition rate for unregistered individuals in facial recognition technology is noteworthy even without the use of a threshold value, sitting at a commendable 64%. Moreover, the rate of false positives is impressively low, remaining at approximately 15% and 31%.
Elektrokardiyogram kayıtlarına dayanarak kalp hastalıklarının sınıflandırılmasına yönelik sinir ağları
The precise and timely detection of cardiac arrhythmias is crucial for healthcare practitioners since it significantly impacts patient outcomes. This study is centered on the enhancement of electrocardiogram (ECG) signal classification, with a specific emphasis on deep learning and the proposed model. The complex patterns included in electrocardiogram (ECG) data, commonly used in clinical practice, pose challenges to conventional classification methods. The efficacy of the technique is evidenced by its ability to generate notable results. The classifier has been trained and validated using a comprehensive electrocardiogram (ECG) dataset that has undergone preprocessing. The performance measures (accuracy, precision, recall) underscore the exceptional ability of the system to identify cardiac arrhythmias. The validation of the model on an independent dataset reveals its capacity to generalize, maintaining a high level of accuracy and providing valuable insights into arrhythmia. The findings are supported by a comprehensive classification report, confusion matrices, and ROC analysis. This work showcases the potential of deep learning in transforming the detection of cardiac arrhythmias, using the model as an illustrative example. The results of this study contribute to the enhancement of ECG classification techniques, hence enhancing the accuracy and reliability of diagnoses and patient treatment in the field of cardiology.
Optik fiberlerin ve modernin katkıları internet üzerinde öğretimi geliştirme teknikleri corona Covid-19 pandemisi sırasında
Internet service has become an indispensable aspect of daily life, extending its necessity to homes, institutions, and organizations worldwide. With the surge in technological advancements, increased demand, and a growing number of internet users, traditional copper wires have proven inadequate to meet the evolving needs. The progression of internet technology from the first to the fifth generation underscores the imperative to adapt and keep pace with developed nations. In response to these demands, optical fibers have emerged as a pivotal solution, providing high-speed data transmission essential for millions of users globally. This is particularly crucial given the substantial data volumes, often exceeding hundreds of gigabytes. Optical fibers find diverse applications, with Fiber To The Home (FTTH) standing out as a specialized conduit for efficiently transferring internet services from companies to homes and workplaces. Moreover, optical fibers play a pivotal role in the deployment of submarine and terrestrial cables, fostering global connectivity through the World Wide Web (WWW). This not only ensures seamless data flow but also supports critical applications such as Fiber To The Home, connecting nations and facilitating robust internet services worldwide. The shift towards optical fibers is imperative in the context of evolving internet technology, with the fifth generation becoming increasingly prevalent. This transition demands the adoption of means, media, and high-speed transmission lines capable of handling the demands of modern internet usage. Optical fibers, with their capacity for high-speed data flow, emerge as a key enabler in this scenario. In conclusion, the utilization of optical fibers is instrumental in addressing the escalating demands for high- vii speed and reliable internet services. From FTTH applications to the establishment of global connectivity through submarine and terrestrial cables, optical fibers play a central role in meeting the evolving needs of the digital age.
Çay bitkisindeki hastalıkların sinir ağları kullanılarak tespiti
This thesis investigates the automated detection of leaf illnesses in tea plants by combining optimization algorithms with cutting-edge computer methods, particularly neural networks. The need for agriculture is growing to maintain crop health with guarantee maximum yields, it is critical to promptly and accurately diagnose plant diseases. With Convolutional Neural Networks (CNNs) serving as the main architecture, the aim of this thesis is to build a reliable and effective technique that can identify several diseases that harm tea plants. The methodology entails the procurement from differing dataset of tea plant leaves, comprising the healthy and unhealthy specimens. Preprocessing the dataset takes into account variables including disease severity, illumination, and image resolution in order to improve model performance. The most recent optimization techniques, such as the mayfly and pelican optimization algorithms (MA), are used in this study to train the neural network using this well selected dataset. The method's performance in disease detection is estimated using metrics like accuracy, MSE, F-score and recall, and sensitivity. The CNN-POAMA model, which was proposed, attained values of 94.5%, 0.035, 0.91, 0.93, and 0.92, respectively. The findings of this study have important ramifications for the farming sector as well as for the growth of automated technologies that have the possible to totally convert the way tea plants manage illness. The model's scalability and potential for real-world application demonstrate how well neural networks and optimization algorithms work together to solve challenging agricultural problems.
Kardiyovasküler hastalık tahmininin geliştirilmesi için entegratif makine öğrenmesi yaklaşımları: XGBoost ve ANFIS algoritmalarının karşılaştırmalı analizi
Cardiovascular diseases (CVDs) are the leading cause of death globally, underscoring the need for advanced detection and diagnostic methods to enhance patient outcomes. This study investigates the efficacy of two machine learning algorithms, XGBoost and the Adaptive Neuro-Fuzzy Inference System (ANFIS), in predicting heart disease across diverse datasets. Utilizing datasets from the UCI Machine Learning Repository, including Switzerland, Cleveland, Hungarian, Long Beach VA, and Statlog Heart, standard preprocessing techniques such as imputation, standardization, one-hot encoding, and SMOTEENN were applied to ensure consistent modeling conditions. Both models underwent extensive training and optimization. XGBoost excelled, particularly achieving 100% accuracy in the Switzerland and Statlog datasets, while ANFIS demonstrated its strength in modeling complex patterns, notably achieving perfect accuracy in the Cleveland dataset. Performance evaluations using accuracy, precision, recall, F1 score, F2 score, and ROC-AUC score highlighted XGBoost's consistent high precision and recall, vital for reliable CVD diagnosis. In contrast, ANFIS showed potential in clinical settings with its high F2 scores, emphasizing the reduction of false negatives. The study highlights the advantages of using advanced machine learning models like XGBoost and ANFIS in cardiovascular diagnostics, suggesting further research with larger and more varied datasets to refine these models and advance medical diagnostics using machine learning.
İş zekası araçları ile Türkiye'de pazar analizi ve finansal değerlendirmenin geliştirilmesi
The Turkish market emerges as a key player amid global shifts, owing to its unique cultural blend and strategic location. Amid economic intricacies, robust market analysis becomes vital for stakeholders to navigate complexities and seize opportunities. Market research provides a compass for decision-making, enabling identification of trends and mitigation of risks. Business Intelligence (BI) emerges as a transformative tool, blending technology and strategy to harness data for informed decision-making at all organizational levels. As businesses strive for agility and resilience, BI adoption becomes not just a competitive edge but a strategic necessity for sustainable growth in a dynamic landscape. This research provides a thorough method to examine Turkey's market and financial dimensions, utilizing the capabilities of Business Intelligence tools. Its objective is to connect data with practical insights, serving various stakeholders invested in understanding the Turkish economy. Keywords: Macro Economy, Business Intelligence, Financial KPI, Market Analysis, Machine Learning
Makine öğrenme metodolojilerini kullanarak yetkisiz erişime dayanıklı, sağlam bir bilgisayar ağ mimarisi geliştirin
As the use of IT spreads rapidly into new areas, the necessity to ensure the security of these systems has grown. Cyberattacks have also become much more sophisticated as a result of the widespread availability of information technology. Consequently, traditional security measures like SIDS have failed to identify new types of assaults. Intrusion Detection Systems (IDS) make it possible to track and gather harmful data inside a network. The majority of IDSs rely on signatures to identify potential threats. They use a set of rules—either manually entered by the administrator or created automatically by the system—to identify and respond to known threats. To maintain the availability of services at all times, network security experts focus on both preventing and responding to intrusion attempts. To find and categorize suspicious actions, security professionals employ tools like IDS. Hence, to protect privacy, security, and the ongoing delivery of services, it is crucial that the IDS continually keeps up-to-date with the most recent intrusion attack signatures. Important factors to consider while evaluating IDS performance are its speed and its capacity to learn new assaults. This study demonstrates how several Machine Learning techniques may be evaluated using the Knowledge Discovery and Data Mining (KDD) dataset, which is also called Knowledge Discovery in Databases. The primary focus is on creating a comprehensive and representative dataset for experimentation, with a strong emphasis on KDD. For this analysis, we have chosen to use the K-Nearest Neighbor (KNN) and Multilayer Perceptron (MLP) classifiers. The KNN classifier has shown the highest accuracy in recognizing and classifying all types of KDD dataset attacks (DOS, R2L, U2R, NORMAL, and PROBE), both for binary class (NORMAL vs. ABNORMAL) and multi-class scenarios. The experimental findings utilizing the proposed KNN and MLP models showed that the accuracy of binary classification using KNN and MLP was 99% and 97% respectively. Furthermore, the multi-class classification produced improved results compared to earlier work, with reported high-level accuracies of 92% and 87% respectively. This thesis investigates the effectiveness of using deep learning techniques, namely MNN and MLP designs, for network flow-based intrusion detection.
Sağlık IoT cihazlarının güvenliğinin sağlanması ve SIEM entegrasyonunun sağlanması: aAdresleme
This thesis explores the growing use of Internet of Things (IoT) devices in healthcare, which has transformed patient care with advanced monitoring and treatment options. However, this integration poses security challenges, demanding robust protective measures. The study focuses on integrating Security Information and Event Management (SIEM) systems with healthcare IoT devices as a solution. Through a thorough literature review, it sheds light on the current state of IoT security, emphasizing the need for improved protective measures. The main issue addressed is the vulnerability of healthcare IoT devices to security breaches and the associated risks to patient data and device functionality. To address this, the research suggests a comprehensive security framework tailored for these devices. This framework, drawn from literature and best practices, includes crucial security measures such as authentication, data encryption, access controls, and anomaly detection. Integrating SIEM systems into this framework provides real-time threat detection and swift incident response, bolstering the overall security of healthcare IoT devices. The thesis highlights the importance of this integration, offering a roadmap for secure, efficient, and effective implementations of healthcare IoT
Bulut bilgisayar: Bilgi teknolojisi dış kaynak kullanımı için daha iyi bir araç
A demonstrate for giving and utilizing IT administrations online is called cloud computing. This permits businesses to concentrate on their center competencies whereas taking off the complex errands of foundation administration to specialized suppliers. Cloud computing offers an approach to IT outsourcing that is more spry, temperate, and scalable. The appearance of cloud computing has reshaped the scene of data innovation outsourcing, advertising organizations a compelling elective to conventional models. This proposal see into the transformative potential of cloud computing as a predominant implies of outsourcing IT administrations. This consider investigates the benefits that cloud computing offers to the outsourcing worldview by implies of a comprehensive audit of suitable inquire about and case thinks about. It looks at how cloud-based solutions surpass traditional outsourcing contracts in terms of "scalability, cost-effectiveness, accessibility, reliability, security, innovation, and data recovery". Furthermore, this thesis assesses the suggestions of cloud computing on trade procedures, organizational structures, and IT administration hones. It examines the vital contemplations for receiving cloud administrations, including vendor selection, migration strategies, governance systems, and administrative compliance. By synthesizing hypothetical bits of knowledge with observational prove, this study gives a factual understanding of how cloud computing revolutionizes IT outsourcing.
Yüz tanıma sistemleri verimlilik karşılaştırması
Bu çalışmada, yüz tanıma teorilerine ve algoritmalarına artan bir ilgi olduğu belirtiliyor. Video gözetimi, suç tanımlama, bina erişim kontrolü ve insansız araçlar gibi endüstri uygulamalarında bu teknolojilerin önemli rol oynadığı vurgulanıyor. Hayatın birçok alanında kullanıma giren bu yüz tanıma sistemlerinin kendi içinde ayrıldığı yerel yaklaşımlar, bütünsel yaklaşımlar ve hibrit yaklaşımlar adlı sınıfları hakkında detaylı bilgi verilmektedir. Bu teknikler, yüz görüntülerini sadece belirli yüz özellikleri veya tüm yüz özellikleri kullanarak açıklamak için kullanılmaktadır. Sistemler arasındaki verimlilik düzeyleri karşılaştırılmaktadır. Literatür taramasında elde edilen çeşitli araştırma bulguları birbiriyle kıyaslanarak 3 yaklaşım öne sürülen teknikler gözden geçirilmekte ve verimlilik düzeyleri üzerine tartışma sunulmaktadır. Yaklaşımların hayatımızdaki yerleri, yüz tanıma hizmeti sunulurken ortaya sergiledikleri performansı; doğru eşleşmesi, tutarlılığı, sağlamlığı, yapısal durumu, birbirlerinden farkları bakımından avantajları ve dezavantajları listelenmektedir. Görüntü işleme ve bilgisayarlı görme alanındaki çalışmaların hızla yaygınlaştığı son yıllarda, tarım, tıp, eğitim, sağlık ve güvenlik gibi birçok alanda bilgisayarlı görme uygulamaları geliştirilmekte ve günlük yaşantımızda kullanılmaktadır. Özellikle yüz ve nesne tanıma işlemleri, farklı uygulamalarla her geçen gün dahada yaygınlaşmaktadır. Bu yüz tanıma uygulamaları, kullanıcılardan yüz bilgileri toplamak suretiyle veri ve bilgi tabanları oluşturmaktadır. Öne çıkan bir uygulama ise işyerlerinde personel giriş ve çıkışlarını takip etmek amacıyla geliştirilen yüz tanıma tabanlı personel kontrol ve takip sistemidir. Bu sistem, kartlı veya manuel kayıt tutma gibi yöntemlerin yerine daha hızlı, etkili ve doğru bir çözüm sunmaktadır. Giriş ve çıkışlarda kameralar kullanılarak personellerin yüzleri tespit edilir ve kimlik doğrulaması yapılır. Bu sayede personellerin işe geliş, gidiş saatleri ve fazla mesai bilgileri otomatik olarak takip edilebilir hale gelir. Geliştirilen bu sistem, özellikle hijyen ve sağlık koşulları açısından kart veya parmak izi gibi geleneksel yöntemlerin zorlayıcı olduğu durumlarda etkili bir çözüm sunmaktadır. Biyometri, bireyleri birbirinden ayırt etmeyi mümkün kılan fiziksel ve davranışsal özellikleri inceleyen bir bilim dalıdır. Bu kapsamda, biyometrik sistemler, bireylerin kimliklerini belirlemek amacıyla özel biyometrik özellikleri kullanarak tasarlanmış sistemlerdir. Bu sistemler arasında yer alan yüz tanıma sistemleri, bireyleri tanımlamak için yüz özelliklerini kullanır. Yüz tanıma sistemleri genellikle güvenlik ve personel devam kontrolü gibi alanlarda, cep telefonlarında, sosyal medyada, bina giriş ve kontrol alanlarında yaygın olarak kullanılmaktadır. Bu çalışmanın amacı mevcut yüz tanıma sistemlerinin uygulama alanları hakkında bilgi vermek, yüz tanıma sistemlerinin sınıflarını incelemek ve verimliliklerini karşılaştırmaktır. Anahtar Kelimeler: Yüz Tanıma Sistemleri, Güvenlik Sistemleri, Biyometrik Sistemler, Kişi Tanımlama, Verimlilik
Şehir içi trafik sıkışıklığını azaltmak için bir araba paylaşım uygulaması tasarımı
Urban areas across the globe are negatively impacted by traffic congestion, and Istanbul is no exception. The city's traffic situation is especially daunting due to its large population of 16 million individuals who reside and work there [1]. To address this issue, carpooling has been identified as an effective method for decreasing traffic congestion and promoting sustainable transportation. As a result, we created a carpooling application tailored for Istanbul with the goal of mitigating traffic congestion. The app permits drivers and passengers to generate personal profiles, seek out carpooling companions, and communicate with one another to coordinate rides. In addition, the app offers services like booking rides, processing payments, and assessing and evaluating experiences to guarantee safety and liability. Overall, to ensure the success of the project, the app will be designed to address the specific needs of Istanbul's urban population and to provide a viable solution to the city's traffic congestion problem using the database schema and API architecture using the PHP programming language and MySQL database management system. The app will be also scalable and can be adapted to other urban areas facing similar traffic problems and compatible with multiple platforms.