Storage efficiency comparison of UML models in selected database technologies

.The study answers the question which database is the best choice for efficient data storage of UML models. Three products were considered: MongoDB, PostgreSQL and Neo4J. The effectiveness test consists of measurement the response time of queries that save and load data. This study also take into account the memory increase ratio during data insertion and the level of complexity of the implementation of the test data mappers used in database queries

Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection

The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine,  Naïve Bayes, and Random Forest. 1D Convolutional Neural Network (1D CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were deep learning methods that were used for the study. The models were tested on a dataset with 140 samples that were grouped into four class labels, and each sample has 2160 features. Those models were tested for classification performance. This research shows Random Forest and 1D CNN have the best performance

Recommendation Systems Based on Association Rule Mining for a Target Object by Evolutionary Algorithms

Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done using associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and cannot be used in the real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then the Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality

Application of intuitionistic fuzzy sets in determining the major in senior high school

Intuitionistic Fuzzy Set (IFS) is useful to construct a model with elaborate uncertainty and ambiguity involved in decision-making. In this paper, the concept relation and operation of intuitionistic fuzzy set and the application in major of senior high school determination using the normalized Euclidean distance method will be reviewed. Some theorem of relation and operation of intuitionistic fuzzy set are proved. In general, to prove the theorem the definition and some basic relation and operation laws of IFS are needed. The distance measure between IFS indicates the difference in grade between the information carried by IFS. There are science, social, and language majors in senior high school. The normalized Euclidean distance method is used to measure the distance between each student and each major. The major, which each student can choose, has been determined depending on test evaluations. The solution provided is the smallest distance between each student and each major using the normalized Euclidean distance method

Application of intuitionistic fuzzy sets in determining the major in senior high school

Intuitionistic Fuzzy Set (IFS) is useful to construct a model with elaborate uncertainty and ambiguity involved in decision-making. In this paper, the concept relation and operation of intuitionistic fuzzy set and the application in major of senior high school determination using the normalized Euclidean distance method will be reviewed. Some theorem of relation and operation of intuitionistic fuzzy set are proved. In general, to prove the theorem the definition and some basic relation and operation laws of IFS are needed. The distance measure between IFS indicates the difference in grade between the information carried by IFS. There are science, social, and language majors in senior high school. The normalized Euclidean distance method is used to measure the distance between each student and each major. The major, which each student can choose, has been determined depending on test evaluations. The solution provided is the smallest distance between each student and each major using the normalized Euclidean distance method

Design of a Novel Convolutional Deep Network Model for Car Accident Prediction

Real-time collision risk estimation is thought to be essential to a sophisticated traffic management system. To swiftly determine accident probability is the goal of real-time crash risk prediction.  However, due to the complex traffic situation on urban arterials, urban arterials were rarely included in previous studies, which mostly focused on highways. This paper suggests using Convolutional Deep Network model (CDNM) to forecast the probability of vascular accidents in real time.  This model has the benefit of being able to use both LSTM and CNN.  CNN retrieves the time-invariant characteristics, while LSTM captures the data's long-term dependability. To estimate the likelihood of an accident, many sorts of data are used, including weather, traffic, and signal timing data. There are also many other data preparation methods employed. The problem of data imbalance is also addressed by normalization which oversamples the crash cases. Using a variety of measures, the CDNM is enhanced on the training data and assessed on the test data.  Five more benchmark models are constructed for model comparison. K-NN, ISVM, ANN, CNN, CNN-EVT and GAN are some of the models in this group. Experimental findings show that the proposed CDNM beats the competition in terms of sensitivity, specificity, accuracy, AUC and G-mean value. The findings of this paper demonstrate that CDNM can real-time prediction of crash risk at arterials

Sentiment Analysis in Karonese Tweet using Machine Learning

Recently, many social media users expressed their conditions, ideas, emotions using local languages ​​on social media, for example via tweets or status. Due to the large number of texts, sentiment analysis is used to identify opinions, ideas, or thoughts from social media. Sentiment analysis research has also been widely applied to local languages. Karonese is one of the largest local languages ​​in North Sumatera, Indonesia. Karo society actively use the language in expression on twitter. This study proposes two things: Karonese tweet dataset for classification and analysis of sentiment on Karonese. Several machine learning algorithms are implemented in this research, that is Logistic regression, Naive bayes, K-nearest neighbor, and Support Vector Machine (SVM). Karonese tweets is obtained from timeline twitter based on several keywords and hashtags. Transcribers from ethnic figures helped annotating the Karo tweets into three classes: positive, negative, and neutral. To get the best model, several scenarios were run based on various compositions of training data and test data. The SVM algorithm has highest accuracy, precision, recall, and F-1 scores than others. As the research is a preliminary research of sentiment analysis on Karonese language, there are many feature works to improvement

A Taxonomy of Challenges for Cloud ERP Systems Implementation

Though success stories of Cloud Enterprise Resource Planning systems (Cloud ERP) are frequently published, the failure rate of Cloud ERP is relatively less reported and discussed. Implementing Cloud ERP systems is not always easy, and at times very challenging. Hence, client organizations and vendors should be aware of challenges to help ensure successful implementation of Cloud ERP systems. The aim of this study is to develop a taxonomy of Cloud ERP implementation challenges. A set of thirty-one challenges was identified from a systematic literature review, and were then categorized by following the taxonomy development process proposed by Nickerson et al. (2013). The taxonomy consists of two dimensions: type of challenges and locus of challenges. Our proposed taxonomy has implications for providing a springboard for further theory development in the Cloud ERP domai

A Conceptual Model for Assistant Platforms

Assistant platforms are becoming a key element for the business model of many companies. They have evolved from assistance systems that provide support when using information (or other) systems to platforms in their own. Alexa, Cortana or Siri may be used with literally thousands of services. From this background, this paper develops the notion of assistant platforms and elaborates a conceptual model that supports businesses in developing appropriate strategies. The model consists of three main building blocks, an architecture that depicts the components as well as the possible layers of an assistant platform, the mechanism that determines the value creation on assistant platforms, and the ecosystem with its network effects, which emerge from the multi-sided nature of assistant platforms. The model has been derived from a litera-ture review and is illustrated with examples of existing assistant platforms. Its main purpose is to advance the understanding of assistant platforms and to trigger future research

Data engineering and best practices

Mestrado Bolonha em Data Analytics for BusinessThis report presents the results of a study on the current state of data engineering at LGG Advisors company. Analyzing existing data, we identified several key trends and challenges facing data engineers in this field. Our study's key findings include a lack of standardization and best practices for data engineering processes, a growing need for more sophisticated data management and analysis tools and data security, and a lack of trained and experienced data engineers to meet the increasing demand for data-driven solutions. Based on these findings, we recommend several steps that organizations at LGG Advisors company can take to improve their data engineering capabilities, including investing in training and education programs, adopting best practices for data management and analysis, and collaborating with other organizations to share knowledge and resources. Data security is also an essential concern for data engineers, as data breaches can have significant consequences for organizations, including financial losses, reputational damage, and regulatory penalties. In this thesis, we will review and evaluate some of the best software tools for securing data in data engineering environments. We will discuss these tools' key features and capabilities and their strengths and limitations to help data engineers choose the best software for protecting their data. Some of the tools we will consider include encryption software, access control systems, network security tools, and data backup and recovery solutions. We will also discuss best practices for implementing and managing these tools to ensure data security in data engineering environments. We engineer data using intuition and rules of thumb. Many of these rules are folklore. Given the rapid technological changes, these rules must be constantly reevaluated.info:eu-repo/semantics/publishedVersio