10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Bella Holub, Viktor Kyrychenko, Dmytro Nikolaienko, Maryna Lendiel, Dmytro Shevchenko, Andrii Khomenko Abstract - The article discusses the informational and algorithmic support for an atmospheric air quality monitoring system. It describes the system's architecture and individual components, along with a logical data model and two approaches to calculating the air quality index. Research on the use of caching methods, pre-aggregation, and sorting is presented to improve the efficiency of processing large volumes of data (Big Data).
Authors - Nguyen Ngoc Tu, Phan Duy Hung, Vu Thu Diep Abstract - In today's Industry 4.0 era, information technology has penetrated every industry, making work easier, faster and helping businesses operate more effectively. The ultimate measure of a business's success is customer satisfaction and loyalty. This work aims to enhance customer care by automating the processing of customer feedback through the development of an automatic classification system using deep learning techniques, specifically the Long Short-Term Memory model. The system will automatically classify customer problems, thereby improving service quality and enhancing the company's image. The study used customer feedback data from our company's customer care system, including 41,886 comments from Vietnamese customers. The study proposes to use the LSTM model to process text data and solve the problem of imbalanced data to improve the accuracy and efficiency of the classification system. Test results of the models show that the highest accuracy is about 80%. The study also recommends improving data labeling and testing more advanced natural language processing techniques to achieve better performance in the future.
Authors - Pham Hong Duong, Phan Duy Hung, Vu Thu Diep Abstract - Text classification, is a very popular problem with various application in natural language processing (NLP). One of the core tasks performed in text classification is assigning labels or tags to units in the text data such as sentences, paragraphs, and documents by exploring the relation between words or even characters. There are many applications derive from text classification, namely Sentiment Analysis, Topic Classification, Spam Detection, Document Classification, and so on. The main object of analyzing is text data. It can come from various sources like a newspaper, a document, some text messages that people use on daily basis. Naturally, as one of the most important form of communication, text is an extremely rich source of data. However, due to its unstructured nature and highly dependence on the context of use, extracting insights from text can be very challenging and time-consuming. This study focuses on exploring the data and forming a classification model on some of the gaming application test sets. We approach the problem using some basic text analysis methods and performing text classification by applying a Deep Learning method – the Convolutional Neural Network model. The dataset is collected from the handwritten test sets for various in-game content by the Quality Assurance Engineers. The main label to be classified is the Priority of the test cases on a whole test set, and eventually, the priority will be used to choose which test case fall into the Regression Test set, specifically 4 types of Priority from highest to lowest label. Finally, we provide an analysis of the performance of deep learning models based on the evaluation metrics as well as comparing it with a self-built traditional Machine Learning model using Logistic Regression and testing against real test case input. From that, we expect to learn to improve the deep learning model and discuss the possible future directions.
Authors - Makhabbat Bakyt, Khuralay Moldamurat, Luigi La Spada, Sabyrzhan Atanov, Zhanserik Kadirbek, Farabi Yermekov Abstract - This paper presents a geographic information system for monitoring and forecasting the spread of forest fires based on intelligent processing of aerospace data from low-orbit vehicles (LOA). The system uses convolutional neural networks (CNN) for fire detection and recurrent neural networks (RNN) for fire spread forecasting. To ensure the security of high-speed data transmission from LOA, a quantum key distribution (QKD) system is implemented, providing virtually unbreakable encryption. Experimental results demonstrate a 30% improvement in fire detection efficiency compared to traditional methods. The paper also discusses the potential costs of implementing QKD and AI, as well as the steps required for practical implementation of QKD on a large scale, taking into account factors such as the influence of the atmosphere on quantum key distribution.
Authors - Hiep. L. Thi Abstract - This paper investigates robust control strategies for managing unmanned aerial vehicles (UAVs) and other systems in emergency situations. We explore the challenges associated with maintaining stability and performance under unforeseen and critical conditions, present current approaches to robust control, and propose new methodologies to enhance system resilience. The paper also discusses practical applications and future research directions in this vital area of control systems engineering.
Authors - Fisiwe Hlophe, Sara Saartjie Grobbelaar Abstract - By adhering to a systematic design approach informed by scientific and engineering principles, Advanced Frugal Innovations yield products that optimize resource utilization, enhancing environmental sustainability and achieving significant cost savings. Following the Joanna Briggs Institute (JBI) framework, this article presents a scoping review that explores the landscape of AFIs in agriculture in developing countries. The Bibliometrix software package was used to facilitate the analysis of the bibliometric data included in this study. This study discovered that AFIs are based on advanced engineering techniques facilitated by research and development and rigorous design. This allows them to be suitable for mass production and have a wide range of novelty. The significant cost savings allow AFIs to be competitive in all markets, not exclusive to lower-income markets. This study discovered that factors such as a suitable innovation ecosystem, user-centered design, availability of highly skilled labor, and technology development enable the generation and development of AFIs. In contrast, skills shortage, lack of cohesion, funding issues, regulatory issues, and market access are some of the hindrances to the development of AFIs. We propose a research agenda for a better understanding of the requirements for setting up innovation ecosystems in the agricultural context that will drive the development and wide adoption of AFIs.
