This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning.

This article presents a five-level three-phase cascaded H-bridge inverter for renewable energy applications, aimed at reducing total harmonic distortion (THD) and enhancing efficiency. The inverter uses a digital signal processing board, TMS320F28335, to generate pulse-width modulation signals through MATLAB/Simulink, ensuring precise control. The experimental setup includes an 84 VDC input voltage and a 300-watt load. Simulation and experimental results closely align, validating the accuracy of the simulation model. The output voltage shows a stepped pattern characteristic of multilevel inverters, significantly reducing harmonic distortion. THD analysis reveals a substantial reduction at higher modulation indices, with particularly low THD at a modulation index of 0.95. Consistent THD levels across modulation indices of 0.5, 0.8, and 0.95 demonstrate robust performance under varying conditions. Comparative analysis indicates that the proposed inverter achieves lower THD levels than traditional inverters, enhancing power quality and system efficiency. The five-level three-phase cascaded H-bridge inverter offers a promising solution for renewable energy applications by significantly reducing THD and improving power quality. Its robust performance and scalability potential contribute valuable advancements to renewable energy systems.

This paper introduces an innovative mobile-assisted language learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex's interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance assessment accuracy and provide nuanced feedback, we integrate a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. Experimental results demonstrate that our multi-feature model achieves 87% accuracy in accent classification across diverse linguistic contexts. User testing revealed statistically significant improvements in pronunciation skills, with learners showing 5-20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning.

This research introduces a novel model, the Financial Data Warehouses API (FDW-API), developed using PHP, Node.js, and Express.js. The model is designed to transform banking credit dataset information into a data warehouse format using a Non-Only SQL (NoSQL) database, stored in JSON format. Three types of databases were employed: MongoDB Node, MongoDB Serverless, and Cassandra. The study includes a comparative analysis of the data retrieval speed from all three databases. The model's applicability was tested in a real-time credit approval web application, demonstrating its effectiveness in transforming and storing data. Testing involved loading datasets ranging from 200, 300, 400, 500, 600, 800, and 1000 entries. Results indicate that the MongoDB serverless database outperformed others in terms of efficiency. Additionally, the FDW-API model streamlines data transformation and storage, facilitating real-time analysis and decision-making for financial institutions and data-driven businesses. Its flexibility integrates seamlessly with existing systems, reducing development time and costs, while its scalability accommodates growing data volumes and evolving business needs, providing a valuable tool for strategic insights and competitive advantage.

The shrimp farming industry at a global scale has experienced noteworthy growth over time; however, it is important to note that conventional farming methods in Thailand are currently encountering various obstacles, including low yields, high production costs, and risks associated with diseases. This research explores the development and implementation of a time-controlled, mobile automatic shrimp feeding machine aimed at optimising feed distribution and enhancing the overall efficiency of shrimp farming operations. The designed system utilises an Arduino-based microcontroller to automate and precisely control feeding schedules, ensuring the delivery of an optimal amount of feed based on the shrimp‘s weight and developmental stage. Through rigorous performance assessments conducted within a sizable 15 × 20 meter shrimp pond, the results demonstrated that the automatic feeding machine achieved a feed distribution coverage of 83% of the pond area, significantly higher than the 65.33% coverage achieved through manual feeding. The machine‘s mobility further ensures even feed dispersion throughout the pond, addressing the limitations of stationary feeders. Additionally, the automation of feeding processes significantly reduces the manual labour required, thus increasing operational efficiency and reducing the potential for human error associated with traditional feeding practices.