A Review of Algorithms for Credit Risk Analysis

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\u27s loan or to dismiss the customer\u27s 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.</p

Sequential Pattern Mining Model of Performing Video Learning History Data to Extract the Most Difficult Learning Subjects

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 a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Towards Intelligent Disaster Response Systems

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 a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Added Value of Modified Anderson–Wilkins Acuteness Score in Prognostication of Patients with Acute Myocardial Infarction

BACKGROUND: Electrocardiogram (ECG) signs on admission can serve as a prognostic marker in patients treated for myocardial infarction (MI). AIM: The aim of the study was to determine the predictive role of modified Anderson–Wilkins (MAW) ECG score of acuteness on the extent of myocardial injury, left ventricular (LV) remodeling, and clinical outcome in patients with acute MI. METHODS: Prospective, observational cohort study on patients treated for MI at the University Clinic for Cardiology. Subjects were analyzed for their demographic, clinical, ECG, LV functional, angiographic variables, course of treatment, and in-hospital outcome. MAW score was calculated for each patient. Patients were comparatively analyzed divided in two groups (score &lt;3 and ≥3). RESULTS: One hundred fifty patients (70% males and 30% females), aged 60.9 years were included in the study. Sixty-eight patients had MAW score &lt;3 (mean 1.7), and 82 had score ≥3 (mean 3.5), p&gt;0.001. Patients with ST-segment elevation MI had OR 2.1 (p&gt;0.000), and patients with multiple locations (excluding anterior) had OR 2.1 (p &gt; 0.000) of having MAW score ≥3. They received mechanical reperfusion 1.9 (p = 0.032) times more often. High MAW score was associated with stress hyperglycemia (OR 2.1; p = 0.032); low potassium (OR 2.8; p = 0.032), lower creatinine (p = 0.050), and higher NT-proBNP (OR 2.5; p = 0.050). High MAW score was associated with decreased LV function and increased LV dimensions on the follow-up echocardiography (p = 0.050 and 0.012, respectively). CONCLUSION: ECG is an important prognostic tool in MI patients. ECG-derived MAW score demonstrates a strong correlation with stress hyperglycemia, potassium, creatinine, and natriuretic peptides level and can serve as an early marker of LV remodeling after MI

Added Value of Modified Anderson–Wilkins Acuteness Score in Prognostication of Patients with Acute Myocardial Infarction

BACKGROUND: Electrocardiogram (ECG) signs on admission can serve as a prognostic marker in patients treated for myocardial infarction (MI). AIM: The aim of the study was to determine the predictive role of modified Anderson–Wilkins (MAW) ECG score of acuteness on the extent of myocardial injury, left ventricular (LV) remodeling, and clinical outcome in patients with acute MI. METHODS: Prospective, observational cohort study on patients treated for MI at the University Clinic for Cardiology. Subjects were analyzed for their demographic, clinical, ECG, LV functional, angiographic variables, course of treatment, and in-hospital outcome. MAW score was calculated for each patient. Patients were comparatively analyzed divided in two groups (score <3 and ≥3). RESULTS: One hundred fifty patients (70% males and 30% females), aged 60.9 years were included in the study. Sixty-eight patients had MAW score 0.001. Patients with ST-segment elevation MI had OR 2.1 (p>0.000), and patients with multiple locations (excluding anterior) had OR 2.1 (p > 0.000) of having MAW score ≥3. They received mechanical reperfusion 1.9 (p = 0.032) times more often. High MAW score was associated with stress hyperglycemia (OR 2.1; p = 0.032); low potassium (OR 2.8; p = 0.032), lower creatinine (p = 0.050), and higher NT-proBNP (OR 2.5; p = 0.050). High MAW score was associated with decreased LV function and increased LV dimensions on the follow-up echocardiography (p = 0.050 and 0.012, respectively). CONCLUSION: ECG is an important prognostic tool in MI patients. ECG-derived MAW score demonstrates a strong correlation with stress hyperglycemia, potassium, creatinine, and natriuretic peptides level and can serve as an early marker of LV remodeling after M

A Review of Algorithms for Credit Risk Analysis

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\u27s loan or to dismiss the customer\u27s 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.</p

Technology: COVID-19 and the ‘New-Normal’ Lifestyle vs. Security Challenges

In the last period, especially during the COVID-19 pandemics, individuals as well as institutions globally and in North Macedonia particularly, have failed to correctly respond to the new challenges related to cyber security, online attacks, and fake news. Being that in a state of isolation and quarantine most governmental institutions have heavily relied on online tools to communicate among each other and with the public, it is quite evident that they have not been well prepared to adopt new technologies. This paper aims to bridge together the needs for technology during the COVID-19 pandemics versus the security challenges that many forget to mention. The primary focus of this paper is to elaborate on the security challenges associated with technology with several examples from incidents around the world and from North Macedonia. As such, it represents a perspective paper with focus on current and emerging advances on IT security for running the “new normal” world