Dry season is a season where the room temperature exceeds the needs of the body so that it is unpleasant, unhealthy and can interfere with human productivity. In addition, the efficiency of use and ...resource requirements are also a concern for some people. To overcome this problem, an automatic room temperature control device was created using the ESP32 microcontroller with Tsukamoto's fuzzy algorithm optimization as a data processing technique to produce optimal fan speeds in duty cycle units based on temperature and humidity conditions in realtime. Four tests by running a fan for 30 minutes on each showed that the average difference between the maximum and minimum temperatures in the room was 0.95°C, while the average difference between maximum and minimum humidity was 2.0%. In addition, the test graph shows that when the fan is rotated in a closed room without air circulation, the relative temperature change increases from the initial minute to the last minute of the test. Meanwhile, changes in relative humidity decrease, although fluctuations increase within 1-4 minutes. This study found that fans are not effective in lowering room temperature optimally. Therefore, it is recommended to replace with an exhaust fan in future research.
The idea of the Internet of Things (IoT) is utilized to increase the advantages of internet connectivity. As long as the electrical device is still connected to the internet, IoT can be utilized to ...operate it. This study applies the concept of IoT as a fan control and monitoring of the humidity and room temperature through a mobile-based application, so that the room can be controlled remotely using a smartphone before use. It is necessary to always keep the room comfortable to use, so that users can increase productivity and always avoid potential disease attacks when the temperature is unstable. In addition, the IoT concept also makes it easier to turn off the fan if at any time you forget to turn it off. This fan is integrated with the ESP32 microcontroller which has been equipped with a Wireless Fidelity (WiFi) module to access data changes in the Firebase Realtime Database. Room temperature and humidity are measured with a DHT22 sensor and processed using Tsukomto's Fuzzy Inference System to produce the appropriate fan speed in the Duty Cycle unit. Tools and applications can work as predicted based on the findings of the research conducted. However, the impact of the fan on a closed room cannot cool the room temperature but the room temperature moves up with a change of 0.3 °C to 0.5 °C within 40-75 minutes. Therefore, in the next study it is recommended to use cooling devices such as Exhaust fans or Air Conditioners.
Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place ...if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.
This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective ...is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration.
In this paper, force sensor signals are classified using a pattern recognition neural network (PRNN). The signals are classified to show if there is a collision or not. In our previous work, the ...joints positions of a 2-DOF robot were used to estimate the external force sensor signal, which was attached at the robot end-effector, and the external joint torques of this robot based on a multilayer feedforward NN (MLFFNN). In the current work, the estimated force sensor signal and the external joints’ torques from the previous work are used as the inputs to the proposed designed PRNN, and its output is whether a collision is found or not. The designed PRNN is trained using a scaled conjugate gradient backpropagation algorithm and tested and validated using different data from the training one. The results prove that the PRNN is effective in classifying the force signals. Its effectiveness for classifying the collision cases is 92.8%, and for the non-collisions cases is 99.4%. Therefore, the overall efficiency is 99.2%. The same methodology and work are repeated using a PRNN trained using another algorithm, which is the Levenberg–Marquardt (PRNN-LM). The results using this structure prove that the PRNN-LM is also effective in classifying the force signals, and its overall effectiveness is 99.3%, which is slightly higher than the first PRNN. Finally, a comparison of the effectiveness of the proposed PRNN and PRNN-LM with other previous different classifiers is included. This comparison shows the effectiveness of the proposed PRNN and PRNN-LM.
Future Potential of E-Nose Technology: A Review Furizal, Furizal; Ma'arif, Alfian; Firdaus, Asno Azzawagama ...
International Journal of Robotics and Control Systems,
07/2023, Volume:
3, Issue:
3
Journal Article
Electronic Nose (E-Nose) technology unlocks the fascinating world of electronic detection, identification, and analysis of scents and odors, paving the way for innovative research and promising ...applications. E-Nose mimics the human sense of smell and has gained significant attention and is applied in various fields, including the food, health and drug industries, safety and crime, and the environmental and agricultural sectors. This technology has the potential to improve quality control, medical diagnostics, and hazardous material detection processes. The E-Nose consists of a combination of gas sensors that mimic the olfactory receptors of the human nose. These sensors detect and respond to different scent molecules, resulting in unique response patterns that can be interpreted and analyzed. E-Nose has found application in the food industry to assess food quality, detect contamination, and monitor fermentation processes. In the health field, it has been used for disease diagnosis, monitoring patient health, and detecting cancerous tissue. In addition, E-Nose has been used for security purposes, such as detection of explosives and prohibited substances, as well as identification of counterfeit products. In addition, it has been used in environmental monitoring for air quality assessment and agriculture for disease detection in crops. Despite its promising potential, widespread adoption of E-Nose faces challenges related to sensor sensitivity, data analysis algorithms (complex data interpretation), response diversity, regulatory considerations, implementation complexity, and cost. This article reviews the latest developments in E-Nose technology, explores its applications and future potential, and highlights challenges that need to be addressed. This is considered important because E-Nose opens up a world of electronic scent identification, and analysis with the potential to improve quality control, diagnosis, and detection.
The dry season is a season where most regions in Indonesia experience an increase in temperature. This unstable temperature can have a negative effect on the human body, so a control device is needed ...according to the needs of the body automatically. This study focuses on optimizing room temperature and humidity control in Yogyakarta City using a fan duty cycle unit with the Fuzzy Tsukamoto method. The ideal temperature and humidity range is obtained from measurements by the Indonesian Board of Meteorology, Climatology, and Geophysics (BMKG). The purpose of this study is to reduce the hot temperature in the room to normal temperature conditions. The calculation results with a temperature of 28.29°C and humidity of 79.06% resulted in a duty cycle of 40.92%. Based on 50 sample data taken each fan rotated for five minutes showed that the average change in temperature was -0.01°C and humidity -0.032%, meaning it could lower 0.01°C and humidity 0.032% every five minutes. This result is considered inefficient considering the very small changes, so in subsequent studies it is recommended to use technology such as air conditioning as a control tool
Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention ...of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models.
People in general find it difficult to determine the transportation route, because to get to one destination there are many alternative paths that must be passed. This study aims to model the search ...for alternative bus route routes that are faster to produce routes that must be passed. The method used in this study is Improved Breadth first search by modifying BFS so that its performance is improved in producing route search completion. The improved BFS method is basically the same as BFS doing a level-by-level search stop if a false finish point is found. As the experiment above with a starting point of 175 and an end point of 54 the BFS algorithm takes 27 seconds 564 milliseconds, while the Improve BFS algorithm takes 171 milliseconds. The results showed that improved BFS can improve the performance of the BFS method. Research can be a model to be applied to other optimal route finding cases.
