The ventilation systems are inadequate, the increase in the amount of carbon dioxide and carbon monoxide in the inner environment can damage human health. In addition, obtaining the level of external environment with excessive ventilation can lead to unnecessary energy consumption. In this study, a system that absorbs air from the environment using carbon dioxide, oxygen and carbon monoxide values in ambient air and controls the operation of the ventilation system that gives fresh air to the environment is designed with fuzzy logic method. The overall purpose of the system is to achieve the ideal ventilation level. Two separate ventilation valves were controlled at the same time with the fuzzy control system designed in the study. The fuzzy control system was created with MATLAB Fuzzy Logic Toolbox and the operating structure of the control system had analyzed.

In the educational setting, artificial intelligence (AI) technology, notably chatbots, has made substantial improvements in English learning. This study aims to determine the effectiveness of using the Artificial Intelligence Virtual Dream Friend and John English Boot applications on learning English in the 5.0 revolution era in English courses for first-semester students at university. The assessment method used is a quantitative research method and research design (quasi-experiment design). Based on the results of the study, it can be concluded that the results of the comparison test showed that My Virtual Dream Friend and John English Bot were both effective for use as computer tutoring in English courses and also increased interest in learning English in the 5.0 revolution era compared to previous conventional methods. The outcomes of this study might be used to direct future research into utilizing chatbots outside of the classroom as learning companions, and educators could use them to adapt evaluation and feedback procedures.

INTRODUCTION: Covid-19 has exposed the necessitate for the rapid acceptance of increasingly pioneering digital health technologies, especially remote health monitoring. The digital revolution in Healthcare is dynamic ease of use of inexpensive concern solutions, enhancing patient care, reducing complications, improving effectiveness, and authorizing healthcare decision-makers with intelligence insight at the point of care.OBJECTIVES: The primary objective of this work is to depict the need to recognize wearable sensors as a prerequisite for supporting paradigms in monitoring patients in real-time and enabling access information from the cloud.METHODS: Internet of Things (IoT) is the association of substantial objects where information and communication tools connect various embedded devices to the internet for gathering and switching over data. The combinations of embedded devices with cloud servers recommend extensive pertinence of IoT to several areas of human life. This paper has proposed a method through cloud-based IoT healthcare sensors to formulate patient monitoring remotely. In combination with the implementation of various inbuilt capabilities, internet-enabled heterogeneous wearable sensors can be used for the collection of biomedical data to transmit patient data directly to cloud severe systems to monitor health remotely.RESULTS: Smart healthcare monitoring includes channels of communication, embedded internal and external sensors, IoT server, and cloud storage. The health parameters activities are done at various levels of refining named application layer, management layer, network layer and layer of device. Different data sensors have been collected by wireless media from nodes. It is saved as an unstructured dataset in the cloud. For security with username and password, a patient database is created. Authorized individuals have access to the cloud in order to monitor cloud sensor data in data log, analogue log, digital input and digital output.CONCLUSION: Patient physical parameters like heart beat respiration ,high temperature and stress are calculated via sensors and can be progressed by WIFI unit in the cloud. From this healthcare practitioner will analyze medical to have effective medication. For face-to-face consultation between doctors and patients, the video feature can be added in the future work.

In recent years, the proliferation of fake accounts on social media has become a significant concern for individuals, organizations, and society. Fake accounts play an important role in spreading fake news, rumors, spam, unethical harassment, and other mischievous motives on social media platforms. Detecting such accounts manually is time-consuming and challenging, especially with the increasing sophistication of the methods used to create them. Therefore, there is a need for automated approaches to detect these fake accounts. This article aims to reveal fake accounts and their role on social media platforms. This article has explored various methodologies to detect fake accounts from social platforms, which will help prevent cybercrime. We have also proposed a generalized deep learning (DL) model to detect such accounts on social media using multimodal data. The proposed architecture uses a combination of textual, visual, and network-based features to capture the various characteristics of fake accounts. Specifically, we use a deep neural network that combines convolutional neural networks (CNNs) for visual data, long short-term memory (LSTM) networks for textual data, and convolutional graph networks (GCNs) for network-based data. We evaluated our model on a publicly available dataset of Twitter accounts and achieved state-of-the-art performance in detecting fake accounts, with an $F$ 1 score of 0.96. We also conducted experiments to show the effectiveness of each feature and the combination of the three features.