Computer vision is one of the most important areas of artificial intelligence and lip reading is one of the most important areas of computer vision. Lip-reading, which is more important in noisy environments or where there is no sound flow, is one of the working areas that can help the hearing-impaired people. There is no dataset in Turkish for lip reading, which there are different datasets at alphabet, word, and sentence level in different languages. The dataset of this study was created by the author and video data were collected from 72 people for 71 words. Audio streams were removed from the collected videos and a dataset was created using only images. Due to the small size of the dataset, the data was replicated with the Camtasia application. After the model of the research was designed and trained, the model was tested on adjectives, nouns, and verbs dataset and success rates of 71.8%, 71.88%, and 79.69% were obtained, respectively.

Today, with the increase in text data, the application of automatic techniques such as automatic text summarization, which is one of the most critical natural language processing (NLP) tasks, has attracted even more attention and led to more research in this area. Nowadays, with the developments in deep learning, pre-trained sequence-to-sequence (text-to-text transfer converter (T5) and bidirectional encoder representations from transformers (BERT) algorithm) encoder-decoder models are used to obtain the most advanced results. However, most of the studies were done in the English language. With the help of the recently emerging monolingual BERT model and multilingual pre-trained sequence-to-sequence models, it has led to the use of state-of-the-art models in languages with fewer resources and studies, such as Turkish. This article used two datasets for Turkish text summarization. First, Google multilingual text-to-text transfer transformer (mT5)-small model was applied on multilingual summarization (MLSUM), which is a large-scale Turkish news dataset, and success was examined. Then, success was evaluated by first applying BERT extractive summarization and then abstractive summarization on 1010 articles collected on the Dergipark site. Rouge measures were used for performance evaluation. This study is one of the first examples in the Turkish language and it is considered to provide a basis for future studies with good results.

In recent years, the huge volume of textual data has become a challenge, as this challenge is seen in various fields, including scientific articles, legal documents, Internet archives, and even online product reviews. Given the limited data processing capacity of humans, processing large amounts of data is impractical and causes confusion; on the other hand, it requires a lot of effort, which ultimately results in a waste of time. To overcome this problem, the need to implement automated techniques such as automatic text summarization has emerged. Automated text summarization is an automated technique used to create a more condensed version of the original content that provides the same meaning and information. In fact, the generated output should contain important information from the original document. Various techniques for automatic summarization have been proposed in studies. Many studies have been presented on automatic text summarization methods, however, limited papers have contributed to reviewing different techniques of summarization methods in different languages, so this topic is evolving to reach maturity. This study focuses on different automatic text summarization methods in Turkish by reviewing the literature and previous studies, thus analyzing the performance of automatic text summarization methods.

Technology has revolutionized education by completely changing the processes of acquiring and sharing information, which are the most important foundations of education. Technology has led to many significant changes, such as increased access and scope, personalized learning, improved interaction and collaboration, diversification of learning methods, and flexibility and mobility. This research aims to investigate university students' technology use habits in education and to determine the factors affecting these habits. The aim of this study is to determine the factors affecting the use of technology in education among university students. In this context, data was obtained through a survey consisting of 65 questions. Data obtained through a quantitative and cross-sectional study involving 1008 university students selected by convenience sampling method were analyzed with ordinal logistic regression and probit regression models. In this study, many factors affecting students' technology use habits in education were examined. For instance, social activities such as spending time with friends and participating in cultural and arts events affect the use of technology in education. The study also examined the positive and negative effects of technology use in education on academic success. In this context, it was determined that the use of online course materials and resources and interactive applications are a factor in the use of technology in education. In the study, it was observed that ordered logistic regression and ordered probit regression models produced similar results, but it was determined that the ordered probit regression model had lower AIC and BIC values.

The study employs an integrated Entropy-TOPSIS model to evaluate and rank the digital access readiness of the households of the 12 statistical regions of Türkiye, between 2019 and 2024. A Global Decision Matrix (72 × 5) was created to have all the composite scores on the same scale, and Entropy weights and TOPSIS ideal solutions were calculated once from the unified matrix, to achieve longitudinal comparability. The results show that Western Anatolia and Istanbul have always been among the top regions in the nation, and in 2020, 2022 and 2023, Western Anatolia overtook Istanbul in the ranking. The lowest scores are consistently observed in Northeastern Anatolia, Central Eastern Anatolia and Southeastern Anatolia in all years. The low discrimination power of the mobile phone penetration and internet access is shown by the entropy weights, which show that these have a near homogeneous distribution, resulting in no significant regional differences. The majority of the score variance is attributable to the usage of desktop computers, laptops and tablets, and this reaffirms the fact that the digital divide in Türkiye has gone beyond basic internet connectivity and is now also a matter of ownership of digital devices at home. The SD tracks plotted longitudinally shows a non-linear, cyclical pattern. However, the pandemic did not cause uniform growth but was expected to magnify the regional inequality in the first few years, followed by temporary convergence in 2021–2022. These gains were then undone by high inflation, leading to higher inequality in 2023 with a delay in hardware spending by eastern households. Ranking robustness is confirmed by the results of sensitivity analyses such as equal-weight simulation, leave-one-out criterion removal, SAW and VIKOR comparison, as well as Variance Inflation Factors checks. Regional inequality, measured by the standard deviation of composite scores, ranged from $\sigma =0.2095$ in 2019 to a peak of $\sigma =0.2495$ in 2023, with the highest-scoring region ($CC=0.924$, Istanbul, 2021) scoring nearly 90 times higher than the lowest ($CC=0.010$, Central Eastern Anatolia, 2023). The study provides the first hardware-access ranking, which is comparable across the statistical regions in Türkiye and which has been carried out over a long period of time, and reveals that the regional policy must shift from expanding broadband to providing targeted incentives for acquiring hardware in structurally disadvantaged eastern regions.