This paper aimed to explore ways to organize Spotify playlists, relying on clustering algorithms. Clustering algorithms were performed on playlists with extracted and standardized audio features obtained from the Spotify API, and the algorithms used were KMeans, DBSCAN, Affinity Propagation, and Spectral Clustering. Their performances were measured with the silhouette score, execution time, and inspection of clustered tracks, where it was determined that KMeans was the best algorithm in this case. Even though the execution time of KMeans is the third best, its silhouette score is the highest with 0.263. With this model, it is possible to effectively perform a mood-based organization of one's Spotify playlist, by dividing it into multiple smaller ones that share similar audio features.

This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions.

ABSTRACT

Due to increasingly widespread electoral corruption, citizens are slowly starting to lose trust in the fairness of democratic elections. The main objective of VoteChain is the elimination of the aspect of trust from the electoral process, in order to make voting more secure, transparent, and easily accessible. This paper proposes and implements a robust system that enhances voting efficiency by creating an electronic platform on top of a distributed Bitcoin Cash blockchain ledger. Blockchain represents a time-stamped series of immutable data records shared across a distributed network. When utilized in the context of voting, it guarantees full anonymity, vote integrity, and a fair, incontrovertible ledger with verifiable election results to all voters. Moreover, the system offers the ability to vote via any Internet-enabled computer or smartphone, dramatically decreasing the overall election organization costs. The system is envisioned as an application that connects to the Bitcoin Cash blockchain network via a custom feature-rich library. After discussing the system's characteristics, design, and underlying technology, this paper presents an example election scenario explaining how VoteChain works in-depth. In the end, the system's possible shortcomings are outlined, along with its prospective evolution and potential improvements that can be implemented.

The aim of the paper is to investigate the impact of SEO on the business performance of a private university in Sarajevo. Thus, the main research question provides the finding on how does the implementation of SEO influence the performance of the business. Moreover, the tested hypothesis presents whether SEO positively influences the business performance of International Burch University (IBU). The research strategy is to analyze primary data derived from a case study, which is generated following a conversation with the Head of the IBU Marketing and PR team. The data sample is derived from Google Analytics (focusing on the number of visits and sessions, average engagement time, keywords and SERP positioning). Seobility tools are employed in data analysis. Business performance is calculated through the IBU CRM system, focusing on student enrolment. Findings indicate that increasing a site's rankings on search engine results pages (SERPs) led to a variety of positive outcomes for companies including an increase in the number of visitors to the site, an increase in the average amount of time users spent on the site, increased user engagement, and an increase in student enrollment, which resulted in IBU increased annual sales revenue. It will benefit many different groups, including the government, which will benefit in both microeconomic and macroeconomic senses, digital marketing enthusiasts and SEO experts, and the academic world, which will benefit as a framework for future studies and research in the field of SEO recognition and implementation in business queries.

Nowadays there are ham and spam messages that are sent to the users via SMS. The aim of this article is to show how machine learning and text processing technologies can be used in order to predict the trustworthiness of SMS messages. The data we are going to use is collected from Kaggle. This study is very important because it helps us to understand how machine learning and text processing can be used in order to predict message trustworthiness. At the time of writing this article, there was not an article explaining how this can be done using the Multinomial Naive Bayes algorithm. The methodology we used in this article consists of dataset collection, data cleaning, data analysis, text preparation, and training model. This will be seen in the methodology section in great detail. At the end of this article, we will show to u the accuracy that we have got when implementing a Multinomial Naive Bayes algorithm for the classification of SMS messages. This study was quite beneficial because anyone can see how Multinomial Naive Bayes algorithm usage can be beneficial in order to predict the trustworthiness of SMS messages.

Since the early beginnings of education systems, attendance has always played a crucial role in student success, as well as in the overall interest of the matter. The most productive way of increasing the student attendance rate is to understand why it decreases, try to predict when it is going to happen, and act on causing factors in order to prevent it. Many benefits of predicted and increased attendance rate can be achieved, including better lecture organization (i.e. lecture time and duration, lecture class choice, etc). This paper describes the steps in the extraction of knowledge from the university's student database and making a model that predicts whether the student will attend the class or not. Results show that the attendance patterns are best reflected when employing a decision tree algorithm, a C4.5 model that is interpretable and able to predict the attendance with 0.81 AUC performance measure

This study presents a short-term prediction approach for honey production using ensemble regression technique. The data were recorded as a part of Habeetat project in Sarajevo, Bosnia and Herzegovina for 2016 season. This season has been entitled as one of the worst seasons for beekeepers in our country, which makes the problem of honey production prediction even more challenging. Random Tree regression algorithm was used for such purpose showing that the mean absolute error in predicting total honey production was less than 1.16 kg in all three hives monitored between November 2016 and April 2017. These findings are very significant for beekeepers since they can be notified in advance to visit individual hives and collect the honey. Besides, they can monitor trends in honey production throughout the season and perhaps change the position of hives in the current season and for the next upcoming season.