ESTA School of Business and Technology

The effectiveness of many machine learning methods for identifying spam in SMS and email interactions is examined in this study's abstract.Our study assesses each method's performance using Multinomial Naive Bayes, Random Forest, Support Vector Classifier, and Extra Tree Classifier.Preprocessing labelled datasets and extracting relevant features for model training are part of the study.Additionally, interaction with the Google Spreadsheet API and Google Drive API is implemented to help with real-time analysis and decision-making.Comprehensive testing and assessment are used to provide insights into the scalability, computational efficiency, and algorithm performance.The results further the progress of digital environment communication security and spam detection.

Abstract Battery state of charge assumes a critical part in advancing electric vehicle energy management, improving battery capacity and energy utilization, keeping batteries from overcharging and over-discharging, for the purpose to guarantee the security and long-term use of batteries lifespan and the expected vehicle autonomy with the indicated battery state of charge. This study presents a battery state of charge estimation using coulomb counting technique with a constant and variable discharging current for Lead-acid battery. In this way, the current, voltage, and temperature are measured during the battery cycle of charge and discharge and contrasted with the estimated value depending on the battery generic model. Additionally, exploratory outcomes are done to approve this investigation.

The existing heart failure risk prediction models are developed based on machine learning predictors. The objective of this study is to identify the key risk factors that affect the survival time of heart patients and to develop a heart failure survival prediction model using the identified risk factors. A cox proportional hazard regression method is applied to generate the proposed heart failure survival model. We used the dataset from the University of California Irvine (UCI) clinical heart failure data repository. To develop the model we have used multiple risk factors such as age, anemia, creatinine phosphokinase, diabetes history, ejection fraction, presence of high blood pressure, platelet count, serum creatinine, sex, and smoking history. Among the risk factors, high blood pressure is identified as one of the novel risk factors for heart failure. We have validated the performance of the model via statistical and empirical validation. The experimental result shows that the proposed model achieved good discrimination and calibration ability with a C-index (receiver operating characteristic (ROC) of being 0.74 and a log-likelihood ratio of 81.95 using 11 degrees of freedom on the validation dataset.

With the emerging of apprenticeship (work-based learning form) in Lithuania, an important role is played by professional masters, who are monitoring young specialists' professional practice, form their professional skills, develop a well-oriented and skillful specialist. The main aim of this research is to analyze the development of pedagogical competencies of professional masters during work-based learning process. The qualitative research data were gathered during semi-structured interview. Twelve professional vocational masters from different Lithuanian vocational schools participated in the research. The performed thematic content analysis findings showed that the competencies of the professional vocational masters actively develop, grow, and change mainly through interaction with students in the process of work-based learning, when supervising practice masters become counselors, guardians, consultants, and inspirers. The most significant challenges include transferring subject knowledge to students, their motivation, and work with students with special needs. The results reveal that vocational practice masters need to improve their pedagogical competencies and psychological knowledge, which would help students to convey knowledge of the profession and motivate them to learn.

Search Engine Optimization (SEO) is the process of improving a website's position in the Internet search engine results. Firstly, this paper, by using an engineering meta-model, analyzes a range of different approaches discussed in the literature into a meta-model. This meta-model explains the key differences, and illustrates the relationships between them. Secondly, by clarifying the links between different models, the meta-model proposes to consider the SEO as a process to be designed rather than as a “combination” of techniques. This process has to integrate different models with the aim of improving and optimizing the SEO. Thirdly, two approaches are infered from this meta-model, and have been applied in a real situation. The results show that website ranking can be variable and fluctuating during the SEO process. It has to be an adaptive process which should be assisted by intelligent models and tools integrated in a holistic approach.