Search Results (97)

Today, relational databases are widely used in information systems. SQL (structured query language) is taught extensively in universities and professional schools across the globe as a programming language for its data management and accesses. Previously, we have studied a web-based programming learning assistant system (PLAS) to help novice students learn popular programming languages by themselves through solving various types of exercises. For SQL programming, we have implemented the grammar-concept understanding problem (GUP) and the comment insertion problem (CIP) for its initial studies. In this paper, we propose an SQL Query Description Problem (SDP) as a new exercise type for describing the SQL query to a specified request in a MySQL database system. To reduce teachers’ preparation workloads, we integrate a generative AI-assisted SQL query generator to automatically generate a new SDP instance with a given dataset. An SDP instance consists of a table, a set of questions and corresponding queries. Answer correctness is determined by enhanced string matching against an answer module that includes multiple semantically equivalent canonical queries. For evaluation, we generated 11 SDP instances on basic topics using the generator, where we found that Gemini 3.0 Pro exhibited higher pedagogical consistency compared to ChatGPT-5.0, achieving perfect scores in Sensibleness, Topicality, and Readiness metrics. Then, we assigned the generated instances to 32 undergraduate students at the Indonesian Institute of Business and Technology (INSTIKI). The results showed an average correct answer rate of 95.2% and a mean SUS score of 78, which demonstrates strong initial student performance and system acceptance. Full article

Real-timenetwork protocol data are indispensable for network security analysis. However, the rapid evolution of protocol standards poses significant challenges to automated parsing and dynamic extensibility. While artificial intelligence (AI) techniques offer potential solutions, they often introduce semantic ambiguities and inconsistent results, thereby undermining parsing precision. To overcome these limitations, we propose PMDL (Protocol Model Description Language), a general-purpose protocol description language. PMDL abstracts protocols into structured sets of fields and attributes, enabling precise and unambiguous specification of protocol syntax and semantics. Based on PMDL descriptions, our execution engine dynamically instantiates and loads protocol templates on the fly, achieving accurate, automated, and dynamically extensible parsing of network traffic. We evaluate PMDL against representative tools such as Wireshark and Kelai, as well as approaches such as Nail and BIND. Experimental results demonstrate that PMDL provides concise yet expressive protocol specifications, and the execution engine achieves superior parsing throughput. Furthermore, performance evaluation using real-world HTTP, MySQL, and DNS traffic from a campus network confirms that our system robustly meets the throughput requirements of large-scale security analysis. Full article

Efficient interaction between PHP (Hypertext Preprocessor) applications and MySQL databases is essential for the performance of modern web systems. This study systematically compares the two most widely used PHP APIs for working with MySQL databases—MySQLi (MySQL Improved extension) and PDO (PHP Data Objects)—under identical experimental conditions. The analysis covers execution time, memory consumption, and the stability and variability of results across different types of SQL (Structured Query Language) queries (simple queries, complex JOIN, GROUP BY/HAVING). A specialized benchmarking tool was developed to collect detailed metrics over several hundred repetitions and to enable graphical and statistical evaluation. Across the full benchmark suite, MySQLi exhibits the lowest mean wall-clock execution time on average (≈15% overall). However, under higher query complexity and in certain connection-handling regimes, PDO prepared statement modes provide competitive latency with improved predictability. These results should be interpreted as context-aware rankings for the tested single-host environment and workload design, and as a reusable benchmarking framework intended for replication under alternative deployment models. Statistical analysis (Kruskal–Wallis and Mann–Whitney tests) confirms significant differences between the methods, while Box-plots and histograms visualize deviations and the presence of outliers. Unlike earlier studies, this work provides a controlled and replicable benchmarking environment that tests both MySQLi and PDO across multiple API modes and isolates the impact of native versus emulated prepared statements. It also evaluates performance under complex-query workloads that reflect typical reporting and analytics patterns on the ClassicModels schema. To our knowledge, no previous study has analyzed these factors jointly or provided a reusable tool enabling transparent comparison across PHP–MySQL access layers. The findings provide empirical evidence and practical guidelines for choosing the optimal API depending on the application scenario, as well as a tool that can be applied for further testing in various web environments. Full article

Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning. Full article

Data security is essential for protecting sensitive information that could compromise both the sender and the receiver. Encryption mechanisms, such as the Advanced Encryption Standard (AES), play a key role in this protection. However, encrypting or decrypting data can significantly impact the performance of the database. This study aims to evaluate the impact of AES on the performance of SQL Server, Oracle, and MySQL when using Transparent Data Encryption (TDE) with the Transaction Processing Performance Council-H (TPC-H) benchmark at different Scale Factors. Performance was assessed using metrics such as elapsed time and system resource usage. In terms of scalability and performance efficiency, SQL Server proved to be the best among the databases tested. However, TDE introduced performance overhead compared to non-encryption test cases. Full article

To enable remote and automatic monitoring of the farmland soil information, this paper has developed a soil monitoring system based on the Internet of Things (IoT), which mainly involves the development of a gateway server node, wireless sensor nodes, a remote monitoring platform, and photovoltaic (PV) modules. The Raspberry Pi 5-based gateway server periodically sends data acquisition commands to wireless sensor nodes via LoRa, receives soil data returned by sensor nodes, and stores them in a MySQL database. Using a remote monitoring platform, Internet users can monitor real-time and historical soil data stored in the database. The STM32F103C8T6-based wireless sensor node receives data acquisition commands from the gateway server, uses soil temperature and humidity sensors as well as a pH sensor to collect soil status, and then sends sensor data back to the gateway server via LoRa. The system is powered by both PV energy and batteries, which enhances the endurance capability. Experimental results show that the designed system works well in remotely monitoring soil information. Using the proposed query attempt dynamic adjustment (QADA) method, the wireless sensor node dynamically adjusts the number of query attempts, which reduces the data acquisition failure rate from 21–25% to no more than 0.33%. Using the obtained qualitative relationship that the data acquisition delay varies inversely with the LoRa transfer rate, the data acquisition delay can be reduced to less than 67 ms. Full article

The recent synergistic explosion of artificial intelligence and the world of machines, in a bid to make them smarter entities as a result of the fourth industrial revolution, has resulted in the concept of chatbots, which have evolved over the years and gained heightened attention for the sustainability of most human corporations. Organisations are increasingly utilising chatbots to enhance customer engagement through the process of agent-based autonomous sensing, interaction, and enhanced service delivery. The current state of the art in chatbot technology is such that the system lacks the ability to conduct text-sensing in a bid to acquire new information or learn from the external world autonomously. This has limited the current chatbot systems to being system-controlled interactive agents, hence, strongly limiting their functionalities and posing a question on the purported intelligence. In this research, an integrated framework that combines the functionalities and capabilities of a chatbot and machine learning was developed. The integrated system was designed to accept new text queries from the external world and import them into the knowledge base using the SQL (Structured Query Language) syntax and MySQL workbench (version 8.0.44). The search engine and decision-making cluster was built in the Python (version 3.12.7) coding environment with the learning process, solution adaptation, and inference, anchored using a reinforcement machine learning approach. This mode of chatbot operation, with an interactive capacity, is known as the mixed controlled system mode, with a viable human–machine system interaction. The smart chatbot was assessed for efficacy using performance metrics (response time, accuracy) and user experience (usability, satisfaction). The analysis further revealed that several self-governed chatbots deployed in most corporate organisations are system-controlled and significantly constrained, hence lacking the ability to adapt or filter queries beyond their predefined databases when users employ diverse phrasing or alternative terms in their interactions. Full article

Controlling the microclimate is vital in fruit fly breeding. This project develops an automated IoT system for monitoring and controlling temperature and relative humidity in chambers, optimizing processes through accessible and flexible technology. It uses a multi-layer system starting with the application layer with the ESP32 for data acquisition and actuator control. The second layer is the network layer, and the perception layer uses VisionFive2 with MQTT and HTTP protocols for communication, as well as NodeRed for flow orchestration and MySQL for data management and storage. In the validation, there are absolute errors of ±0.434 °C and ±0.5 RH, which are values within the acceptable ranges for these applications. Full article

This paper addresses the need for accessible and interoperable supervision solutions within the Industry 4.0 paradigm, particularly for small-scale or resource-constrained environments. The proposed system integrates a web-based architecture using opensource technologies to enable real-time industrial monitoring and data acquisition. A hybrid setup was developed, combining a virtual glass manufacturing process in Factory IO with a physical three-phase induction motor controlled by a Modicon M580 PLC. The system architecture includes a local HMI developed in Control Expert and a remote interface built with React and Node.js, both synchronized through a MySQL 8.0 database populated via Python 3.13 using the Modbus TCP/IP protocol. Experimental results demonstrate consistent data synchronization, reliable multi-platform integration, and an average end-to-end latency of 156 ms, validating the feasibility of the approach for IIoTbased applications. The solution demonstrates how general-purpose web technologies can be effectively repurposed for industrial use, offering a cost-effective and scalable alternative to traditional SCADA systems. The proposed architecture is easily replicable, adaptable to various process configurations, and suitable for academic, prototyping, and SME environments. Full article

Accurate estimation of engine thrust and overload is crucial for ensuring structural integrity and optimizing the aircraft’s life-cycle design. To address this issue, this study develops an integrated thrust and load prediction framework that combines vision-based flight maneuver recognition with an improved transformer-based deep learning model (YOLO), leveraging measured flight parameters. After maneuver recognition, the model achieves a mean absolute error of 1.86 and R² of 0.97 in prediction. The framework is implemented via a Python-based software system with a MySQL database, supporting functionalities including thrust/load prediction, trajectory visualization, and performance evaluation. Comparative experiments demonstrate that the framework achieves an average maneuver recognition accuracy of 81.06%, outperforming the existing PLR-PIP and DTW methods. This approach provides high-precision and reliable thrust data as well as tool support for real-time thrust estimation, fatigue life assessment, and flight safety risk prevention. Full article

Accurate quantity takeoff (QTO) is essential for cost estimation and project planning in the construction industry. However, current practices are often fragmented and rely on manual or semi-automated processes, leading to inefficiencies and errors. This study introduces a cloud-based framework that integrates automated QTO with a rule-based Quantity Precision Check (QPC) to ensure that quantities are derived only from validated and consistent BIM data. The framework is designed to be scalable and compatible with open data standards, supporting collaboration across teams and disciplines. A case study demonstrates the implementation of the system using structural and architectural models, where automated validation detected parameter inconsistencies and significantly improved the accuracy and reliability of takeoff results. To evaluate the system’s effectiveness, the study proposes five quantitative validation metrics, Inconsistency Detection Rate (IDR), Parameter Consistency Rate (PCR), Quantity Accuracy Improvement (QAI), Change Impact Tracking (CIT), and Automated Reporting Efficiency (ARE). These indicators are newly introduced in this study to address the absence of standardized metrics for automated QTO with pre-takeoff, rule-based validation. However, the current validation was limited to a single project and discipline-specific rule set, suggesting that broader testing across mechanical, electrical, and infrastructure models is needed to fully confirm scalability and generalizability. The proposed approach provides both researchers and practitioners with a replicable, transparent methodology for advancing digital construction practices and improving the quality and efficiency of BIM-based estimation processes. Full article

The COVID-19 pandemic has underscored the need for telemedicine to ensure continuity of pediatric care during health emergencies. This paper presents NS-Assist, a hybrid web–mobile decision support system for managing Idiopathic Nephrotic Syndrome (INS) in children. The system combines rule-based reasoning and fuzzy inference to assist clinicians in diagnosis, treatment adjustment, and relapse monitoring, while enabling caregivers to record and track daily health data. Implemented using Spring Boot, ReactJS, and Flutter with a secure MySQL database, NS-Assist integrates medical expertise with computational intelligence to support remote decision-making. A pilot evaluation involving 40 participants, including clinicians and caregivers, showed improved communication, reduced consultation time, and enhanced follow-up continuity. These results highlight the system’s potential as a reliable and adaptable framework for pediatric telemedicine in resource-constrained and emergency settings. Full article

Human-database interaction is inevitable in intelligent system applications, and accurately converting user-entered natural language into database query language is a critical step. To improve the accuracy, generalization, and robustness of text-to-SQL, we propose Nabil (a model for natural language conversion query language based on brain-inspired computing technology and a large language model). This model first leverages the spatiotemporal encoding capabilities of spiking neural networks to capture semantic features of natural language, then fuses these features with those generated by a large language model. Finally, a champion model is designed to select the optimal query from multiple candidate SQLs. Experiments were conducted on three database engines, DuckDB, MySQL, and PostgreSQL, and the model’s effectiveness was verified on benchmark datasets such as BIRD. The results show that Nabil outperforms existing baseline methods in both execution accuracy and effective efficiency scores. Furthermore, our proposed normalization and syntax tree abstraction algorithms further enhance the champion model’s discriminative capabilities, providing new insights for text-to-SQL research. Full article

The development of chatbots to access database services and information systems has triggered a lot of research on frameworks for service development, including the development of ISONER (Information System On Internet Messenger). This framework consists of multiple phases including pattern recognition, query processing, and response generation. In its implementation, the framework develops pattern recognition services that are currently based on Natural Language Processing (NLP). Improved pattern recognition algorithms enhance the system’s ability to accurately interpret user intent. The pattern recognition used in this research utilizes built-in plugins from MySQL, namely N-gram and Full-Text Search, which can be run directly on the MySQL engine to reduce latency and do not require another programming language. The FTS and fourgram algorithms gave the best results when applied on 100 test data points, with a threshold of 0.91, accuracy of 91%, precision of 99%, and recall of 92%; the average computation time was 19 s for 100 test data points and 2 min 49 s for 1000 data points tested simultaneously. Full article

Background/Objectives: This study presents Co.Ge. a Cognitive Generative digital platform for cognitive testing. We describe its architecture and report a pilot study. Methods: Co.Ge. is modular and web-based (Laravel-PHP, MySQL). It can be used to administer a variety of validated cognitive tests, facilitating administration and scoring while capturing Reaction Times (RTs), trial-level responses, audio, and other data. Co.Ge. includes a study-management dashboard, Application Programming Interfaces (APIs) for external integration, encryption, and customizable options. In this demonstrative pilot study, clinical and non-clinical participants completed an Auditory Verbal Learning Test (AVLT), which we analyzed using accuracy, number of recalled words, and reaction times as outcomes. We collected ratings of user experience with a standardized rating scale. Analyses included Frequentist and Bayesian Generalized Linear Mixed Models (GLMMs). Results: Mean ratings of user experience were all above 4/5, indicating high acceptability (n = 30). Pilot data from AVLT (n = 123, 60% clinical, 40% healthy) showed that Co.Ge. seamlessly provides standardized clinical ratings, accuracy, and RTs. Analyzing RTs with Bayesian GLMMs and Gamma distribution provided the best fit to data (Leave-One-Out Cross-Validation) and allowed to detect additional associations (e.g., education) otherwise unrecognized using simpler analyses. Conclusions: The prototype of Co.Ge. is technically robust and clinically precise, enabling the extraction of high-resolution behavioral data. Co.Ge. provides traditional clinical-oriented cognitive outcomes but also promotes complex generative models to explore individualized mechanisms of cognition. Thus, it will promote personalized profiling and digital phenotyping for precision psychiatry and rehabilitation. Full article