The significant decrease in agricultural land and the rapid development of hydroponic system technology such as Nutrient Film Technique (NFT), have brought huge challenge to farmers. This hydroponic system requires special attention to several parameters such as the water temperature, water level, acidity (pH), and the concentration of the nutrient (EC/PPM). We first monitor and collect information from NFT Hydroponic farmer and then systematically evaluate and analyze them. Unfortunately, it is still controlled by using the conventional way (human), for example in controlling the concentrations of nutrient has to be done at least once a day, so much time is wasted. In addressing these issues, we need a system that can be applied and used easily. We built a hydroponic monitoring and automation system that can monitored using sensors connected to the Arduino Uno microcontrollerm, Wi-Fi module ESP8266 and Raspberry Pi 2 Model B microcomputers as the webserver with the concept Internet of Things, in which each block hydroponic farming can communicate with the webserver (broker). Web used as the interface of the system that allows user to monitor and control the NFT hydroponic farming. The NFT hydroponic web interface management systems using a responsive web framework, such as Bootstrap for the front-end, JQuery and JavaScript libraries. The result shows that this system helps farmers to increase the effectivity and efficiency on monitoring and controlling NFT Hydroponic Farm.

Based on WHO's data in 2012, cardiovascular diseases (CVD) is the most leading contributor to mortality, estimated at 17.5 million deaths. There are two effective way to examined heart condition, i.e. electrocardiogram (ECG) and echocardiogram. Although those two methods are effective, both methods are relatively expensive. The other method that can be used to examined heart condition is auscultation method. Auscultation requires substantial clinical experience. This process very subjective because it depends on the experience and the ability of paramedic hearing. Numerous research on developing an automatic system for detecting abnormal heart sound already conducted, but these studies still have some weaknesses making the accuracy in the classification less optimal. So, this paper aims to developed a scheme to classify the heart condition based on spectrogram of heartbeat sounds. To help increase the accuracy of the diagnosis, through this study, a CAD-based scheme was developed by applying a Convolutional Neural Network (CNN) algorithm. The achieved classification accuracy is 82.75%. This indicates that the proposed method has a potential to be implemented in the development of a computer aided diagnosis for CVD.

The use of autonomous vehicles in agriculture has increased in recent years. To fully automate agricultural missions, particularly the tillage process using the walk-behind hand tractor, the path planning problem for the robot must be solved so that all points in the intended region of interest may be traced. The current planning algorithm has been successful in determining the best tillage path. On the other hand, the algorithm ignores the path built using the dynamic starting point, finish point and path distance. We propose a path planning concept for back-and-forth path patterns. Our algorithm employs a novel approach based on Laravel and Google Maps, which considers the user’s desired distance interval, start point, and finish point. We demonstrated auto-generating vertex-edge pathways in this research. Field trials using a walk-behind hand tractor in a plowing mission have been successfully conducted to validate the accuracy of the resulting waypoint coordinates.

This paper presents Rudas, a low cost and energy efficient system that fully operational for IP-based network. The system comprised of few embedded sensors and networking devices in the house that connected to the Internet architecture. It can be used in many applications, leading to the emerging concept of the Internet of Things (IoT). The IoT describes the future where everyday things (or objects) will be connected to the internet and be able to communicate themselves to other devices. In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In particular, it includes a single board computer, microcontrollers, responsive web technology, database system, environmentally sensitive devices, tornado server software, cameras and could computing. The system is also implanted a fuzzy logic artificial intelligence to adjust the intensity of light and air condition in a room. Information will be recorded and calculated so that home owners will be more efficient in managing and monitoring all home devices. A result from this study is a well-worked home management and other aspects including security, energy saving and monitoring.

This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessive energy usage. Employing a combination of 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) methods within the framework of the Internet of Things (IoT), the study encompasses multiple components, including hardware, network, database, data analysis, and software. The research outcomes encompass a real-time web application for monitoring and control, temperature distribution visualizations within the incubator, a prototype incubator, and a predictive energy consumption model. Testing the LSTM method resulted in an RMSE of 42.650 and an MAE of 33.575, while the CNN method exhibited an RMSE of 37.675 and an MAE of 30.082. Combining CNN and LSTM yielded an RMSE of 32.436 and an MAE of 25.382, demonstrating the potential for significantly improving neonatal care.

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.