For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets has been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. In addition, 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

Class imbalance is a challenge that affects the prediction rate on a minority class. To remedy this problem, various SMOTEs (synthetic minority over-sampling techniques) have been designed to populate synthetic minority instances. Some SMOTEs operate on the border of a minority class, while others concentrate on the class core. Unfortunately, it is difficult to put the right SMOTE to the right dataset because distributions of classes are varied and might not be obvious. This paper proposes a new technique, called FLEX-SMOTE, that is flexible enough to be used with all sorts of datasets. The key idea is that an over-sampled region is selected based on the characteristics of minority classes. This approach is based on a density function that is used to describe the distributions of minority classes. Herein, we have included experimental results showing that FLEX-SMOTE can significantly improve the predictive performance of a minority class.

This research aims to prototype a small smart off-grid solar cell system. o provide an alternative electrical supply for a smart Lingzhi mushroom farm. This research applied the use of IOT with voltage and current sensors to measure and monitor the voltage of solar cell charging, amp (ampere) of current charging from the solar panel into a battery and (ampere) of current loading from a battery to the irrigation systems (fog and sprinkler pumps). Voltage and current data processed through Blynk was developed as an IOT cloud service. Data was stored into the Blynk and displayed on mobile devices in real time. The functional status (switching on and off for periods of time, current consumption) pushes notifications through Blynk IOT cloud service on the Blynk application. The equipment and tools used in this research were NodeMCU, voltage and current sensors, relay modules, DC sprinkler, and fog pumps. A small off-grid solar cell was applied. The solar cell system consists of a charger, battery, and solar panels. The IOT cloud used is Blynk. The results of the research shows that using IOT with voltage and current sensors enhances the prototype of a smart farming as an alternative and green electrical resource.

This research aims to prototype a smart Lingzhi mushroom farm. This research applied the use of IOT with a sensor to measure and monitors the humidity in the Lingzhi mushroom farm. The humidity data processed through NETPIE was developed and provided by NECTEC as a free service for IOT. Humidity data was stored into a NET FEED (a sub service from NETPIE) and displayed on mobile devices and computers through NET FREEBOARD (another sub service of NETPIE). This research also controlled sprinkler and fog pumps automatically and the functional status (switching on and off for periods of time) pushes notifications through LINE API on the LINE Application. The equipment and tools used in this research were NodeMCU, humidity sensor, RTC (real time clock), relay module, sprinkler and fog pumps. C++ and Node.JS were used as programming. The services and protocol used were NETPIE (Network Platform for internet of everything) with subservices such as NETPIE FEED, NETPIE FREEBOARD, and NETPIE REST API. The results of the research showed that using IOT with the sensor enhanced the prototype of smart farming.

In this paper, we have proposed an automated medical system for detecting type 2 diabetes from exhaled breath. Human breath can be used as a diagnostic sample for detecting many diseases as it contains many gases that are dissolved in the blood. Breath-based analysis stands out among the different non-invasive ways of detection as it provides more accurate predictions and offers many advantages. In this work, the concentration of acetone in the exhaled breath is analysed to detect type 2 diabetes. A new sensing module consisting of an array of sensors is implemented for monitoring the acetone concentration to detect the disease. Deep learning algorithms like Convolutional Neural Networks (CNN) are normally used to automatically analyse medical data to make predictions. Even though the CNN performs well, a few modifications to the network layout can further improve the classification accuracy of the learning model. To analyse the sensor signals to generate predictions, a new deep hybrid Correlational Neural Network (CORNN) is designed and implemented in this research. The proposed detection approach and deep learning algorithm offer improved accuracy when compared to other non-invasive techniques.

This review investigated research works on affective computing by using electrocardiogram (ECG) and electrodermal activity (EDA). The 27 related research papers, including 23 from IEEE Journals and 4 from other Q1 Journals in the last five years, were studied. The main goals have been to summarize common trends in this field in recent years as well as provide discussions and a big picture of how future research should be done. The content of this review covers the fundamental pipeline of affective computing research consisting of stimuli, intelligent affective computing, and sensor design and data processing. Additionally, we discuss future research directions and potential applications, including stress monitoring, music cognition, and robotics, where ECG and EDA will play a significant role with wearable devices. Therefore, this review serves as an information summary for everyone who is interested in affective computing and improving AI’s understanding of human emotion.