This article highlights the advantages of NoSQL databases compared to traditional SQL (relational) databases. It argues that the database concept is often implicitly tied to relational models, overlooking non-relational databases’ potential in Big Data, data analysis, and AI. Non-relational databases offer schema flexibility, horizontal distribution, and high read/write speeds—critical for processing very large data volumes efficiently.
Automatically identifying fake news is a complex challenge requiring detailed understanding of misinformation propagation and advanced data processing. Machine Learning and Deep Learning algorithms for detection demand continuous adaptation as disinformation tactics evolve. While promising, these technologies must be carefully calibrated for different contexts. This paper explores automated fake-news detection methods, analyzing their effectiveness and proposing improvements to address data quality, domain variability, and evolving disinformation strategies.
Relational/SQL and document/JSON data stores are competing but complementary technologies in OLAP (On-Line Analytical Processing) systems. Whereas traditional approaches for performance comparison use query execution time, this paper compares two distributed setups deployed on PostgreSQL/Citus and MongoDB by focusing solely on query completion within a 10-minute timeout. The TPC-H benchmark was converted into a denormalized JSON schema in MongoDB. An initial set of 296 SQL queries was executed in PostgreSQL/Citus and then mapped to MongoDB’s Aggregation Framework. Completion success was collected across six scenarios defined by two data sizes (0.01 GB, 0.1 GB) and three node counts (3, 6, 9). Relationships between completion rates and query parameters were assessed using statistical tests and machine learning techniques.
This study compares tree-based machine learning algorithms for predicting Bucharest residential apartment prices. Using a dataset from March 2025, comprehensive preprocessing—including imputation, categorical encoding, and feature engineering (e.g., distance to public transport)—was applied. Models were optimized via grid search with 5-fold cross-validation and evaluated using RMSE, MAE, and R². Results show XGBoost outperforms Random Forest and Decision Tree models across all metrics.
This study evaluates machine learning and deep learning algorithms for credit card fraud detection within a federated learning framework. With digital banking’s rapid growth facilitating customer access yet exposing new fraud vectors, real-time detection is critical. The paper trains XGBoost and a neural network on a highly imbalanced public dataset, reflecting real fraud scenarios. Both models achieve high accuracy, but the neural network consistently outperforms XGBoost on precision, recall, and F1 score, demonstrating deep learning’s superior capability in privacy-preserving, collaborative fraud detection.
This article examines the intersection of relational and non-relational databases with game development, highlighting the growing importance of Big Data in the gaming industry. It discusses how storing user data has become essential for marketing and game improvement. The concepts are reinforced through a web application prototype demonstrating NoSQL database implementation in game development and extraction of user insights. Overall, the findings aim to guide industry practitioners in choosing appropriate database technologies and leveraging consumer data to enhance products and services.
The way communication platforms are used in military operations has changed a lot over the years. They're now essential for mission success, quick decision-making, and maintaining strategic advantages. This paper dives into the modern communication tools that are making waves in military settings, with a spotlight on networked communication, signal support, cybersecurity strategies, and how different forces work together in joint and multinational missions. It also tackles some of the major hurdles, like congestion in the electromagnetic spectrum, cyber threats, and the need for secure data transmission in challenging environments. By pulling insights from FM 6-02: Signal Support to Operations and the latest scientific research, this paper highlights recent advancements in military communication tech and how they boost operational effectiveness. Additionally, the research looks ahead at future possibilities, such as AI-driven communication platforms, quantum encryption, and cutting-edge satellite networks for defense purposes. This paper's primary contribution is the development of a structured, AI-enabled communication workflow that integrates quantum-safe encryption, blockchain authentication, and satellite-based coordination to improve decision-making and resilience in multi-domain operations.
Combining big-data analytics, text mining, NLP, and Latent Dirichlet Allocation, this study reviews 7,145 open-access climate-change publications (2011-2022). Key research themes include urban health, smart technologies, and algorithmic modelling. While big-data applications grew steadily to 2022, a decline in 2023 suggests shifting priorities. Sentiment analysis shows predominantly neutral tones, and the integrated approach offers insights into evolving climate-research trends.
Financial reporting is key to presenting an organization’s financial information clearly and systematically. The rise of Artificial Intelligence (AI) has driven a shift toward automation, delivering improved data quality, cost and time savings, scalability, flexibility, and higher operational efficiency. Focusing on Alteryx with the AnaCredit dataset, this article examines the practical benefits of integrating automation into daily financial-reporting workflows and documents measurable improvements in data integration and reporting speed.
The advent of microservice designs, which prioritizes enhancing deployment timelines, scalability, and flexibility, marks an advancement period in software development. This article presents a tool designed to accelerate the construction of microservice architectures. Using an intuitive interface, the solution allows users to create fundamental code and graphically construct structures, streamlining the typically laborious first coding process. By automating project documentation and scaffolding, the solution reduces resource consumption and speeds development. A comparison with Spring Initializr demonstrates a straight path from conceptual design to deployable code, underscoring this tool’s potential to revolutionize software project development.
Energy communities (ECs) enable decentralized production and distribution of renewable energy. Using Business Process Model and Notation (BPMN), this article maps workflows of key EC actors—prosumers, storage owners, EV-charging facilities, aggregators, and market entities. The resulting process models uncover optimization points for energy use, grid stability, and economic value, demonstrating BPMN’s role in building efficient and resilient decentralized energy systems.
This paper presents a holistic approach to biological and agricultural research focused on the use of interconnected technologies in the context of climate change. Researchers from different countries have analyzed how smart technologies can help agriculture adapt to these changes. The most representative works in the field are analyzed. Among these tech-nologies are graph database systems such as Neo4j, which have demonstrated success in predicting the studied phenomena. The paper describes the development of a soybean crop productivity prediction model using monthly and annual data of meteorological phenomena such as precipitation, air temperature, hydrothermal coefficient, soil moisture and others. Some of the results of this promising research are also presented.
This article presents a comprehensive bibliometric analysis of research articles focused on Robotic Process Automation (RPA) project management. By analyzing a large dataset of scientific papers, this study aims to identify trends and gaps of this subject. To better understand the real-life implications, this study also analyzes different opinions coming from people actively working with this technology. The bibliometric analysis aims at identifying several key themes often addressed in scientific papers and correlations, while also understanding the interest in this field among re-searchers. The analysis is based on a correspondence between Intelligent Automation concepts and methodologies for projectsâ development. The analysis continues with the results of a survey completed by a more detailed series of interviews at the beginning of 2023 that focuses on a real-life perspective, with the objective to identify project phases where teams are often experiencing challenges. Implementation of Robotic Process Automation initiatives depends heavily on the project lifecycles, however RPA in the context of project methodologies is a topic not sufficiently researched at the moment. RPA teams are expressing different preferences regarding implementations, Agile developments being one of them, as it seems that Agile principles are closely matching RPA criteria. Furthermore, based on the findings, this article proposes a set of practical suggestions to enhance the success of RPA implementations in different project phases. The originality of this paper is reflected in the methodology adopted, that includes different techniques, in the attempt to complete each other: the literature overview and current perspectives.
The Internet of Things (IOT) is a paradigm that has changed the traditional way of living into one in step with technology. IoT has brought great changes in several fields such as agriculture, energy, healthcare, transportation and infrastructure. A lot has been done to improve IoT technology, but there are still study challenges (technical, political) that need to be solved to reach its full potential. The main purpose of this article is to provide an overview of what IoT means, its evolution and applicability in day-to-day life. This article discusses several aspects such as IoT architecture, IoT challenges, IoT applicability areas, importance of Big Data analytics in IoT and its evolution in the last few years. Furthermore, programming languages that can be used to create IoT-type software and small examples are presented, comparisons between them. This article will help the readers to better understand IoT, real-life applicability, evolution and overview of how to develop an IoT program using Arduino or Raspberry PI.
Artificial intelligence (AI) has made enormous strides in recent years, transitioning from science fiction to a technology that is revolutionizing every sector of the global economy. Thanks to advances in machine learning, natural language processing and computer vision, artificial intelligence is no longer a futuristic dream, but a reality today. Once optimised and integrated into everyday life, artificial intelligence will substantially enhance human capabilities and contribute to the betterment of society. This paper will embrace the opportunities offered by computer vision, as part of artificial intelligence, by showcasing the FurnishMe application, an image search engine for furniture recommendations. Buying furniture online, as well as offline, is an overwhelming process given the quantity and diversity of furniture products available. The FurnishMe software solution allows users to easily explore the furniture of the three largest furniture retailers in Romania: Ikea, Jysk, and Dedeman. The system analyses user-uploaded interior design images in order to identify furniture items and provide aesthetically similar products from the three big retailers mentioned above. Both consumers and traders benefit from this solution. Clients benefit from a quick and easy way to choose the product they desire which integrates various features such as design, texture and colour. Moreover, businesses gain from greater sales by luring clients and saving time on in-person consultations.
In the context of a pandemic that emerged with lightning speed, data science has become a cornerstone for governments decision-making processes. By analyzing numerous centralized databases, researchers have been able to identify trends, the spread of the virus, and run artificial intelligence (AI) simulations to anticipate crucial points of the COVID-19 pandemic. Data warehouses created during this period offer real-time monitoring of the global effects of the virus. The health databases are already common in national systems, but their usefulness rises above storing medical histories. The cross-disciplinary nature of the COVID-19 pandemic accentuates the need for collaboration between doctors, medical specialists and data analysts, data engineers, and Artificial Intelligence engineers. This article provides a comprehensive overview of how databases and data warehouses can offer different scenarios for citizens and health specialists alike.
This paper aims to study the evolution of artificial intelligence, its impact on education and the job market, and the attitude of a sample of the population towards its potential effects. The study also focuses on the newly emerged chatbot, ChatGPT. For a full perspective on the topic, a survey was conducted to capture the perspective of Romanian employees in corporations regarding the impact of artificial intelligence.
Nowadays, the global warming threat is a highly discussed matter. One of the factors that accelerates this process is the air pollution that can be caused by cars' emissions. This paper concerns how the size of the engine, the type of fuel, the fuel consumption and the transmission type influence the emission of CO2. In order to understand and predict that variable, we used several machine learning algorithms, such as Regression for Generalized Linear Model or K-Means for Hierarchical Cluster Model. The technology that empowered this analysis was Oracle's Machine Learning for Python (OML4Py) that allowed us to integrate both database and data management concepts and data analysis algorithms. By doing that, we managed to discover a pattern for the emission of CO2 based on the factors previously mentioned and, after that, predict future levels of CO2 emissions for various car models.
Many children in Bangladesh have ASD. The rate of Autism Spectrum Disorder (ASD) is increasing in Bangladesh and other countries, day by day. Autistic children find it difficult to talk and express themselves regarding what they want or not. Also, some autistic children are not comfortable dealing with the outside world. For example, they do not feel comfortable in social settings or in any program. There are some schools and organizations where many kind-hearted people are trying to help those autistic children in many ways. We all know that there is no cure for autism. But it can be reduced. After good treatment, an autistic child can recover. To help the treatment process, we have developed an interactive app that will help them to cope with social events and places, as well as help them with verbal tasks. We have developed a model to access the severity of an autistic child and help the child to improve communication. This paper presents our interactive app and also provides a concise comparison of it with existing apps to support children with ASD.
Nowadays, breast cancer is considered one of the most common causes of death among adult women. At the same time, the bright side is that among all the types of cancer, breast cancer is more curable, if diagnosed in the early stages. In this paper, the diagnosis of breast cancer has been proposed using the least possible number of features based on correlation. In the proposed method, we have used correlation to find the strength between the input and the target features. Then we provided a way to create a new subset that consists of only the most relevant features. We have used the Wisconsin breast cancer data set (WBCD) for the experiments. The performance of the model is justified using classification accuracy and the f-score. The result shows that our proposed method obtained the highest classification accuracy (95.26%) with the Random Forest classification using only 4 features from 29 available features, which led to a reduction of 86% in data set size.
