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The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are used. The assessment of the credit risk datasets leads to the choice to cancel the customer's loan or to dismiss the customer's request is a challenging task involving a profound assessment of the information set or client information. In this paper, we survey diverse automatic credit risk analysis methods used for credit risk assessment. Data mining approach, as the most often used approach for credit risk analysis was described with the focus to various algorithms, such as neural networks. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Next Generation Incident Response System (NICS) is a platform developed by MIT Lincoln Lab that is currently being used in Macedonia by the Centre for Crisis Management (CCM). It allows coordination during natural disasters between first responders of various departments and allows them to use state of the art tools to communicate and share information. This research focuses on advancing the platform by introducing intelligent agents to the platform, based on machine learning techniques and natural language processing. Our goal is to leverage data generated in social media and feed NICS with automatically processed information from these media categorized in twelve different needs (categories). This paper presents the current state of our research, preliminary results and final goals. This work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.

The paper aim is to define a method for performing video learning data history of learner’s video watching logs, video segments or time series data in consistency with learning processes. To achieve this aim, a theoretical method is introduced. Sequential pattern mining with learning histories are used to extract the most difficult learning subjects. Based on this method, it is designed a model for understanding and learning the most difficult topics of students. The performed video learning history data of learner’s video watching logs makeup of stop/replay/backward data activities functions. They correspond as output of sequence of the learning histories, extraction of significant patterns by a set of sequences, and findings of learner’s most difficult/important topic from the extracted patterns. The paper mostly aim to devise the model for understanding and learning the most difficult topics through method of mining sequential pattern. This work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.