Publications
Agresa Qosja | Didier Georges | Eralda Gjika | Ligor Nikolla | Arben Cela
Abstract
Electricity consumption forecasting stands as acritical research domain within electrical engineering, with myr-iad of traditional forecasting models and artificial intelligence techniques undergoing rigorous examination. This paper is
devoted to a comparison of three Machine Learning approaches to surrogate modelling and forecasting of the daily electricity consumption in Tirana, Albania: A Radial Basis Function (RBF) approach, a feed forward Neural Network approach and a Recurrent Neural Network approach. Through meticulous
experimentation across four distinct scenarios encompassing variations in training/testing splits, historical data utilization, and hyper parameter optimization, we thoroughly evaluate the performance of each model. Comparative analysis is con- ducted based on model fit, computational efficiency, and error measurement metrics. Our findings highlight the remarkable
performance of the RBF ARX approach, underscoring its effectiveness in accurately forecasting electricity consumption.
Key-words: Electricity consumption forecasting, neural net- work modelling, Radial Basis Function modelling
Evis Plaku | Klei Jahaj | Arben Cela | Nikolla Civici
Abstract
Automated news article classification is a method of categorizing textual data into predefined classes. Addressing this problem finds applications in diverse domains, including information retrieval, topic modeling, sentiment analysis and content recommendation systems. In Albanian, though there is a rapid increase of digital content, there is limited availability of text corpora, presenting significant obstacles for advancement of natural language processing research and applications.
The contribution of this paper is twofold. First, we introduce a dataset consisting of 9600 news article titles spanning across various categories. Second, we utilize this dataset to assess the effectiveness of several machine learning algorithms for topic classification. Experimental results demonstrate the efficacy of recurrent neural networks in comparison to simpler classifiers and ensemble methods.
Agresa Qosja | Eralda Gjika | Didier Georges | Ligor Nikolla | Arben Cela
Abstract
Forecasting electricity consumption and production remains essential, necessitating the application of recent, hybrid, and classical models. Traditional approaches often fall short in capturing the intricacies of nonlinear and irregular data patterns. This prompts the need for new or adaptable models capable of achieving high accuracy. This paper compares three distinct Machine Learning approaches for surrogate modeling and forecasting of daily electricity consumption across five regions in Albania: a Radial Basis Function (RBF) approach, a feedforward Neural Network approach, and a Recurrent Neural Network approach.
Our aim is to develop a generalized model for these stations and provide highly accurate one-day ahead predictions. We conduct a comparative analysis based on model fit, computational efficiency, and error measurement metrics. Our findings underscore the exceptional performance of the RBF ARX approach, showcasing its efficacy in accurately forecasting electricity consumption based on daily data. Additionally, we identify daily mean temperature as a key factor influencing model performance.
Elton Domnori | Donald Elmazi | Gledi Tace
Abstract
The Internet of Things (IoT), has significantly impacted several industry sectors in recent years, particularly those that deal with air, soil, and water quality. With the growing diversity of sensors and their processing and communication capabilities, a number of methods have been put up for creating systems that continuously gather environmental data. These data can be further processed for long-term analysis or quick response. Every solution that has been put forth presents a static system, meaning that the sensors are positioned in fixed, designer-determined locations. Our work presents a dynamic and autonomous system in which sensors are free to travel within a given area and, by utilizing intelligent algorithms and distributed systems techniques, they determine the optimal way to employ in order to monitor the target environment by combining intelligent algorithms and distributed systems techniques.
Abdusamed Kura | Donald Elmazi
Abstract
Machine learning models are essential instruments in modern data analytics, propelling progress in a multitude of fields. However, the effectiveness of these approaches depends on careful model selection and data preprocessing. Consequently, it is imperative to devise an enhanced model builder that facilitate data preprocessing and improve the creation of machine learning models. Understanding the critical role that machine learning models play in contemporary data analytics is essential to this effort. On the other hand, careful data preprocessing and wise model selection are necessary for these models to function effectively. Our study presents a solution that automates data preparation activities by addressing data irregularities such as null values, incorrect inputs, and redundant entries. The program then creates a variety of models on its own, increasing the adaptability and effectiveness of machine learning applications in order to solve these issues. This work has produced software that is a substantial development in terms of both speedy preprocessing and data cleansing, as well as its ability to construct several machine learning models. The program employs a strict assessment methodology to choose and maintain the most effective model by looking at a wide range of performance measures. Our software framework’s versatility and effectiveness are demonstrated through experimental validation, allaying worries about inflexible code. Rather, the program is a flexible framework that can easily learn from and adjust to a variety of datasets. In conclusion, by presenting a novel software solution designed for effective data preprocessing and model building, this work makes a substantial contribution to the development of machine learning approaches. Our software can help advance machine learning research and applications by automating important operations and making the process of selecting the best models easier.
Evis Plaku | Arben Cela | Erion Plaku
Abstract
Path planning is essential for guiding a robot to its destination while avoiding obstacles. In practical scenarios, the robot is often required to remain within predefined safe areas during navigation. This allows the robot to divert from its main path during emergencies and follow alternative routes to a safety center. This paper introduces a novel method to incorporate safety zones into path planning. Each zone is defined by a central point and a radius. Our approach efficiently plans paths to the goal, ensuring that the robot can reach a safety center without having to travel more than the radius of the safety zone. Using sampling, our approach constructs a roadmap with navigation routes and identifies safe locations that satisfy the distance requirements for reaching a safety center. The safe portion of the roadmap is then searched to find a path to the goal. We demonstrate the effectiveness of our approach through simulated experiments in obstacle-rich 2D and 3D environments, utilizing car and blimp robot models.
Fabjan Pjetri | Fjorda Prifti | Rajs Kastrati
Abstract
The paper presented by the authors aims to research the role of Microfinance in Albania over the years, especially in improving living conditions for the less privileged groups. The authors have tried to give a summarized panorama regarding the progress and evolution of Microfinance in the state of Albania. Portraying the latter as a good alternative solution to the shortcomings of the Traditional Banking System, to address the needs of certain segments of the population. The analysis used by the authors focuses on the major and current challenges that the Microfinance sector in Albania is facing, it also examines the perspectives and potential future opportunities of this developing sector in our country. Since most of the economic entities that operate in our country are small and medium-sized businesses, where family businesses account for a significant number of the total number of economic entities, the authors have considered it important to research this segment of the economy. Keywords: Microfinance, Financial System, Financial Supervision Authority, Digitization of financial services in relation to market demands.