Electrical fires frequently occur due to the ignition generated by electrical gadgets, wiring, cables, and multi-taps. These fires can cause numerous human casualties and property damage every year. The mechanisms that cause electrical fires are generally unique and require specialist research to understand their features fully. Due to the rising number of electrical fires, IT-based assessment models for electrical fire causes assessment have been proposed and experimented with to improve efficiency and accuracy. This survey aims to thoroughly examine and analyze the current advanced practices in detecting and evaluating electrical fire causes, focusing explicitly on IT-based assessment methods and simulations to provide a full review. This is achieved by comprehensively surveying the techniques and procedures utilized in prior works and research. Firstly, we introduce the survey of factors contributing to electrical fires. Next, electrical fire simulations to determine the causes and behavior of electrical fires are investigated. The assessment of electrical techniques to understand electrical fire causes has been emphasized. Lastly, the summaries and proposed enhancements to advance the progress in this domain are examined. To the best of our knowledge, the survey of electrical fire causes assessment IT-based methodology available has been reviewed and analyzed for the first time. These findings are expected to become a valuable reference in this domain.

The development of Internet of Things (IoTs) technology in agriculture, notably aquaculture, has increased over the years due to empowering real-time monitoring and improved environmental sustainability. To ensure the development and survival of aquatic life, farm employees must constantly check and take prompt action to protect the sustainable habitat in ponds. It is also crucial for providing technical assistance to farmers during the growing season. To address these issues, we present the building of a smart aquaculture assistant system integrated with IoT and Big Data. The main components of this system include the IoT layer assistant layer. First, tracking the pond environment functions through sensor systems set up in the pond, such as turbidity, pH, and temperature sensors, which are built for the IoT layer. Our assistant can make suggestions for farmers by visualizing farm environment conditions and outdoor weather analysis. The assistant layer uses a fine-tuning NLP model GPT 3.5 for our aquaculture farming 500 frequently asked questions and crawled knowledge dataset. The chatbot can provide naturally logical replies and knowledge related to farming activities. The system is implemented as mobile and desktop applications using React Native and Python to monitor or manipulate administrative tasks. Thus, thanks to our smart assistant, prompt preventive action can be taken to reduce losses, increase productivity, and enhance farmers’ knowledge related to farming.

Digital Twins technology is being increasingly adopted by various industries, heralding transformative changes and unlocking fresh possibilities. It offers high control over physical entities and efficiently manages intricate systems by integrating several technologies. While the aquaculture sector has witnessed several recent technological advances, it remains to be seen whether the industry is actively embracing the integration of digital twins into its operations. To address this research question, we investigate the applications based on Digital Twins technology in the aquaculture sector using a literature review methodology. Our research includes a comprehensive literature review on Digital Twins technology in aquaculture in many articles and reviews worldwide from 2017 to 2023. First, we research the extent to which Digital Twins technology has already been explicitly adopted in aquacultural systems and investigate their reported benefits. The researched systems are divided into three main types: simulation and digital model system, digital twins-based system, and metaverse-based system. Second, we identify potential opportunities for aquacultural based on Digital Twins technology. In addition, we will discuss the overview architecture of the Digital Twins-based aquaculture system and the properties of these trends in the future. The main contribution is to offer a comprehensive overview of the Aquaculture Digital Twins-based system, examining its development implications through various case-study systems worldwide.

The drone technology field has received attention and witnessed numerous impressive advancements that heralded substantial changes and opened new possibilities in different industries. Several advanced technologies, such as artificial intelligence, computer science, object avoidance technology, and others, have been collaboratively developed to empower drone operations. Drone technology software engineering overview can assist in identifying and prioritizing research gaps that require attention in future research. The main contribution is to investigate the recent development of drone technology by applying the literature review methodology. Our research involves a comprehensive literature review of drone technology software engineering, including worldwide journal papers and reviews published from 2010 to the present. To achieve this goal, we will first research the evolution of drone technology software engineering research trends. Second, we investigate approaches to ensuring the reliability, safety, and security of drone software systems. After that, software testing and validation methodologies have emerged to ensure the robustness and trustworthiness of drone systems are investigated. Finally, the potential of integration of AI/ML capabilities into drone software that has been progressing to enhance autonomous decision-making and adaptation in drone technology is described. As a result, summarized features of a drone system and its increasingly expanding variety of applications are discussed. After that, an analysis to identify the emerging trends, promising research directions, and ongoing challenges in the domain of drone software engineering is presented. These findings are expected to become a valuable reference in this research field.

Product reviews become more important in the buying decision-making process of customers. Exploiting and analyzing customer product reviews in sentiments also become an advantage for businesses and researchers in e-commerce platforms. This study proposes a sentiment evaluation model of customer reviews by extracting objects, emotional words for emotional level analysis, using machine learning algorithms. The research object is the Vietnamese language, which has special semantic structures and characteristics. In this research model, emotional dictionaries and sets of extract rules are combined to build a data training data set based on the semantic dependency relationship between words in sentences of the given Vietnamese context. The recurrent neural network model (RNN) solves the emotional analysis issue, specifically, the long short-term memory neural network (LSTMs). This analysis model combines the vector representations of words with a continuous bag-of-words (CBOW) architecture. Our system is designed to crawl realistic data in an e-commerce website and automatically aggregate them. These data will be stored in MongoDB before processing and input into our model on the server. Then, the system can exploit the features in products reviews and classify customer reviews. These features extracted from different feedback on each shopping step and depending on the kinds of products. Finally, there is a web-app to connect to a server and visualize all the research results. Based on the research results, enterprises can follow up their customers in real-time and receive recommendations to understand their customers. From there, they can improve their services and provide sustainable consumer service.

Coffee, the second-largest global soft commodity, can take advantage of a comprehensive mining of daily and historical market data for more effective informed trading decisions. Advanced ICT and data mining technologies can change the trading market operation. The existing systems are confronted with certain constraints, including incomplete data, insufficient documentation for storage, and a requirement for a scalable infrastructure for big data analytics, such as a data warehouse or data lakehouse. To address this issue, the paper presents a design and implementation of a coffee commodity trading big data warehouse capable of analyzing various essential parameters for supporting informed decision-making. First, the designed system can automatically collect coffee trading data for New York Arabica coffee futures prices from selected worldwide reports and financial data portals. Next, the Extract, transform, and load (ETL) process is adopted to ingest coffee futures trading crawled data into the 3 layers data warehouse. Finally, the analytical system will extract and visualize selected key dimensions that influence coffee futures prices within different observation windows and perspectives. As a result, we implement a prototype of a coffee trading data warehouse on the crawled data from January 2000 to October 2022 and visualize trends in coffee futures prices based on the collected data for informed decision-making. The construction system is capable of stably operating and processing large volumes of transaction data. This paper will be valuable documentation for reference and decision support for coffee commodity trading enterprises and contribute to the development of future forecasting algorithms.