Sustainable water quality data are important for understanding historical variability and trends in river regimes, as well as the impact of industrial waste on the health of aquatic ecosystems. Sustainable water management practices heavily depend on reliable and comprehensive data, prompting the need for accurate monitoring and assessment of water quality parameters. This research describes a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. Internet of Things technology and a Eureka water probe sensor is used to collect and reconstruct the water quality dataset for the period from June 2022–February 2023, with Turbidity, Optical Dissolved Oxygen, Dissolved Oxygen Saturation, Spatial Conductivity, Acidity/Basicity, Total Dissolved Solids, Salinity, Temperature, Chlorophyll, and Depth as the recorded parameters from six different stations. The presented dataset comprises a total of 211,322 data points, which are separated into six CSV files. The dataset is then evaluated using the Long Short-Term Memory (LSTM) algorithm with a Mean Squared Error (MSE) of 0.0012256, and Root Mean Squared Error (RMSE) of 0.0350080. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices.

This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.

Detection and classification have an essential role in the world of e-commerce applications. The recommendation method that is commonly used is based on information text attached to a product. This results in several recommendation errors caused by invalid text information. In this study, we propose the development of a fashion category (FC-YOLOv4) model in providing category recommendations to sellers based on fashion accessory images. The resulting model was then compared to YOLOv3 and YOLOv4 on mobile devices. The dataset we use is a collection of 13,689, which consists of five fashion categories and five accessories' categories. Accuracy and speed analysis were performed by looking at mean average precision (mAP) values, intersection over union (IoU), model size, loading time, average RAM usage, and maximum RAM usage. From the experimental results, an increase in mAP was obtained by 99.84% and an IoU of 88.49 when compared to YOLOv3 and YOLOv4. Based on these results, it can be seen that the models we propose can accurately identify fashion and accessories categories. The main advantage of this paper lies in i) providing a model with a high level of accuracy and ii) the experimental results presented on a smartphone.

Over the past few decades, research on object detection has developed rapidly, one of which can be seen in the fashion industry. Fast and accurate detection of an E-commerce fashion product is crucial to choosing the appropriate category. Nowadays, both new and second-hand clothing is provided by E-commerce sites for purchase. Therefore, when categorizing fashion clothing, it is essential to categorize it precisely, regardless of the cluttered background. We present recently acquired tiny product images with various resolutions, sizes, and positions datasets from the Shopee E-commerce (Thailand) website. This paper also proposes the Fashion Category—You Only Look Once version 4 model called FC-YOLOv4 for detecting multiclass fashion products. We used the semi-supervised learning approach to reduce image labeling time, and the number of resulting images is then increased through image augmentation. This approach results in reasonable Average Precision (AP), Mean Average Precision (mAP), True or False Positive (TP/FP), Recall, Intersection over Union (IoU), and reliable object detection. According to experimental findings, our model increases the mAP by 0.07 percent and 40.2 percent increment compared to the original YOLOv4 and YOLOv3. Experimental findings from our FC-YOLOv4 model demonstrate that it can effectively provide accurate fashion category detection for properly captured and clutter images compared to the YOLOv4 and YOLOv3 models.

Consumers have shifted away from traditional transactions to online shopping as a result of the COVID-19 pandemic, digital social life, and staying at home. This has a significant impact on the volume of transactions for several ecommerce platforms, including Shopee, which must continue to improve customer satisfaction. One way to accomplish this is to improve the accuracy of the automatic category recommendation, which currently contains a significant number of errors made by the seller. The Shopee Data Scraper was used to collect data, and two qualitative analyses, namely content and thematic analysis, were used to determine how many instances of automatic category recommendation fraud were committed by the seller.According to the content analysis, there is a 29 percent error in product category selection. Meanwhile, 75.1 percent of products should be classified separately but are instead classified as ‘others.’ Following that, 72.7 percent of product titles contain the same words as existing categories.From this research, we managed to get an analysis of the percentage of errors in the automatic category recommendation mechanism from the Shopee platform which causes sellers to place their products in the wrong category so that they can be used for suggestions for improvement or further research.

Due to its advantageous location on the border of Jakarta and Tangerang, Tangerang Selatan is a highly developed city. In this instance, the Tangerang Selatan Department of Culture and Tourism (Dispar Tangsel) is required to use the application to assist the general public in realizing the availability of information in the Tangerang Selatan area. Twenty to twenty-five restaurants submit applications each year to the Dispar Tangsel Tourism Business Registry (TDUP). Dispar Tangsel must choose and decide on TDUP licensing priorities from among TDUP requests in order to open a restaurant. The purpose of the research is to offer recommendations to the community on food choices. Rapid application development (RAD) was used in the system's development. Additionally, the collaborative filtering technique has been employed by the decision support system to determine the amount of criteria or weight for restaurants using the weighted sum algorithm and for restaurants using cosine-based similarity algorithms. Additionally, the system design tool made use of MySQL as a database, PHP, the Codeigniter framework, and the unified modeling language (UML). The result demonstrates that the system is capable of displaying the output in accordance with the user's expectations during black box testing, which evaluates the functionality of the system based on the specifications. Collaborative filtering in SISRES can yield a significant improvement in recommendation accuracy. By collectively analyzing user preferences and behaviors, the algorithm can provide more relevant and personalized recommendations.