Welcome to AAB College Digital Repository
The AAB College Digital Repository is a centralized platform for collecting, preserving, and disseminating the academic and research output of the university. Our repository provides open access to a wide range of scholarly content, including:
- Research articles and papers
- Theses and dissertations
- Academic projects and reports
This platform aims to support knowledge sharing and collaboration among students, faculty, and the wider research community.
Visit AAB College the leading institution of non-public higher education in Kosovo!.
Communities in DSpace
Select a community to browse its collections.
- Scholarly publications authored by the institution’s academic staff, including journal articles, conference papers, and book chapters.
- Master's theses submitted by graduate students as part of the requirements for their degree.
Recent Submissions
TREATMENT AND VISUALISATION OF BIG DATA IN POWER SYSTEMS
(AAB College, 2025-07-11) Keka, Ilir
Because the energy is difficult to store in a long time then for the electric companies, especially for trading companies and for transmission operator, it is very important to know the network load. The aim of this paper is to find a model of the relationship between electricity load and time for some Substations using a sample of Big Data. This is achieved using Revolution R Enterprise language. Furthermore, Big Data were analyzed to find the distribution of data and to calculate the descriptive statistical parameters for these data. At the end of the paper is made evaluation of this mathematical model.
THE CENTER OF GYMNAST'S BODY GRAVITY DURING THE PERFORMANCE OF THE SALTO FORWARD TUCKED
(AAB College, 2025-07-11) Gashi, Besim; Rexhepi, Fadil
Salto forward tucked is one of the most spectacular elements of acrobatics. During her performance, gymnast manifests her technical skills by coordinating every movement of her body parts with maximum precision. With the system for kinematic analysis - APAS (Ariel Performance Analysis System), was analyzed the performance of salto forward tucked by a quality gymnast. The results of the body position and the trajectory of her movement show greater displacement of the body's gravity center in the anteroposterior direction - forward (208 cm), then in the vertical-high (60 cm) direction, while in the mediolateral direction - the displacement value is symbolic (3 cm), which shows the high precision of performing salto forward tucked. The highest point of the center of gravity of the body from the carpet during the flight phase is 154cm and is reached for 2.16 seconds of the fly-off phase that represents 55% of the flight phase time (3.92 seconds).At the top of the center of gravity trajectory, the angles in the angles are: knees-64 °, thigh-75 °, arms-20 ° and elbows-139 °.These values confirm correct performance, while the kinematic analysis of the salto forward tucked gives a very useful insight into the technique of performing, finding errors and improving them during the training process.
Comparing Linear and Nonlinear Models for Load Profile Data Using ANOVA, AIC, and BIC
(AAB College, 2025-07-11) Keka, Ilir
To select the best model for the relationship between the response variable and predictor variables different approaches can be used. In this paper the aim is to find the best model that gives the best forecast of the values for the line of best fit, or to find the model, which is mostly approximated to the real model. This study aims to compare linear and nonlinear models for analyzing electric data, addressing the research gap in identifying the most effective modeling approach. The research methods involved the application of Analysis of Variance (ANOVA), Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC) to evaluate six models, including polynomial regressions of degrees 2, 3, and 4, linear regression, multiple linear regression, and models based on interaction terms. The results revealed that nonlinear models, particularly the polynomial regression with a degree of 4 model, demonstrated superior performance in terms of goodness of fit and predictive accuracy. This model has the lowest AIC and BIC values and an adjusted R-squared of .07619 or 0.76%. The F-statistic for this model is high, at 279, which is greater than 1. The study’s main focus is on data transformation and visualization, which were essential for using the R tool to find patterns and relationships in the data. This study has a lot of potential because it provides useful information for decision-making in the energy sector.
EFFECT OF PARALLELISM IN CALCULATING THE EXECUTION TIME DURING FORECASTING ELECTRICAL LOAD
(AAB College, 2025-07-11) Keka, Ilir
The electrical load is of great economic importance to electric power industry. Load information is of a great technical importance for a stable electricity system as a secure supply could be guaranteed. The present paper aims at finding the effect of parallel computing in terms of execution time of the scripts used for the mathematical models of load profiles to address the forecast of electrical load. A parallel system based on the sequential and parallel execution of the scripts by splitting input data was used to investigate the effect of parallelism. The mathematical pattern was derived from the regression model with several independent variables and represent the dependence of electrical load upon time, temperature and humidity. Regression analysis involved the programming environment R. In addition, the data have been analyzed for a clearer picture of data distribution.
STATISTICAL ANALYSIS OF UNIQUE WEB APPLICATION VULNERABILITIES: A QUANTITATIVE ASSESSMENT OF SCANNING TOOL EFFICIENCY
(AAB College, 2025-07-11) Zogaj, Gani
Web application security is a critical aspect of modern cybersecurity, necessitating efficient and reliable vulnerability detection mechanisms. This study presents a quantitative analysis of unique web application vulnerabilities detected by four automated scanning tools: Nessus, Acunetix, OWASP ZAP, and BeSECURE. We scanned 67 web applications and sorted the vulnerabilities we found into four categories: Critical, High, Medium, and Low. This study evaluates each tool's effectiveness and reliability using mean and standard deviation, providing key insights into their performance consistency. Using straightforward statistical methods, we aim to determine which scanning tool performs best in finding vulnerabilities while maintaining consistent results across different web applications. Additionally, the analysis offers comparative insights into the performance variations among these tools, highlighting their strengths and limitations. The study paper contributes to strategic decision-making in cybersecurity, enabling organizations to select the most effective tools for vulnerability assessment. The findings demonstrate that OWASP ZAP exhibits superior detection capabilities and consistency across various severity levels, while integrating tools like Nessus, BeSECURE, and Acunetix enhances vulnerability detection, with Nessus excelling in identifying critical and high-severity vulnerabilities.