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Automatic scoring approach for Arabic short answers essay questions
(Çankaya Üniversitesi, 2017) Alfalahi, Mohammed Abdulmunem Nsaif
There are different types of questions produced by the students in their exams, such as multiple-choice questions, true/false questions, and essay questions which require free text answers. Evaluation and scoring these types of exams traditionally are an exhausting process that takes from the instructors a lot of efforts, time and activities. In this regard, applying automated approaches to evaluate and score exams are essentially required to reduce time and efforts. Although there are many commercial tools for scoring multiple-choice and true/false questions, yet there is lack of approaches and tools for evaluating and scoring essay questions, especially for the Arabic language. In this research, the aim is to propose an automated scoring approach for short answers to Arabic essay questions. The scoring process is based on the similarity between the student's answer and model answer which is provided by the instructor. Cosine similarity measures will be used for this purpose. Cosine similarity is a heuristic evolutionary measure that has succeeded to solve text to text similarity problems. In this research, we will use the word root for each keyword in the student's answer and the model answer in order to achieve accurate results. The proposed approach will be tested on a data set proposed and will be compared to other approaches.
Text categorization based on semantic similarity with word2vector
(Çankaya Üniversitesi, 2017) Alsamurai, Ather Abdulrahem Mohammedsaed
With an increase in online information, which is mostly in the form of a text document, there was a need to organize it so that management and retrieval by the search engine became easier. It is difficult to manually organize these documents, therefore, machine-learning algorithms can be used to classify and organize them. Mostly, they are faster, more accurate and less expensive than manual classification. Most traditional approaches of machine learning algorithms depend on the term frequency in determining the importance of the term within a document and neglect semantically similar words. For this reason, we proposed to build a classifier based on semantically similar words in text classification by using the Word2Vector model as a tool to compute the similarity between documents and capture the correct topic. So we built two models by applying three phases: the first phase, we applied preprocessing steps and the second phase, we created a dictionary for top ten categories of Reuters 21578 datasets and the final phase we trained Word2Vector model on the Wikipedia English dataset and use it to compute similarity v between documents. Depending on the results of our study, we found that the second model (the most similar predicted topic) is better than the first model (average based predicted topic) in all categories. When we compare the results of our study with other studies, we found that result of our study is a parallel to the results of other studies, but not overcome them, although these studies use feature selection in the improvement of their results while we use feature extraction in explaining of our results.
An approach to improve the time complexity of dynamic provable data possession
(Çankaya Üniversitesi, 2016) Hawi, Mohammed Kadhim
In this thesis, we aim to take some actions for alleviating the fears when the data storage over outsourcing, and guarantee the integrity of the files in cloud computing. In this study, we have suggested some ideas to improve FlexDPDP scheme [13]. Particularly, proposed scheme successfully reduces the time complexity for verifying operations between the client and the server. The proposed scheme is a fully dynamic model. We involved some parameters to ensure the integrity of the metadata. In spite of the fact that auxiliary storage expenditure by Client-side (the client stores approximately 0.025% size of the raw file). The remarkable enhancement in this proposed scheme is reducing the complexity. The complexity of the communications and the computations decreased to O(1) in both Client-side and Server-side during the dynamically update (insertion, modification and deletion operations) and challenge operations.
Writer identification based on covariance features
(Çankaya Üniversitesi, 2016) Karadeniz, Talha
Local descriptors have been widely utilized in image analysis for automatic object categorization. In this work, an algorithm based on empirical covariance estimation of region descriptor vectors is formulated and developed. This technique is then specialized in order solve to the task of writer identification via a tricky way of keypoint extraction. Experiment results are reported for ETH-80 and ICFHR 2012 Writer Identification Contest datasets.

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