Olfaction can enhance the experience of music, films, computer games and virtual reality applications. However, this area is less explored than other areas such as computer graphics and audio. Most advanced olfactory displays are designed for a specific experiment, they are hard to modify and extend, expensive, and/or can deliver a very limited number of scents. Additionally, current-generation olfactory displays make no decisions on if and when a scent should be released. This paper proposes a low-cost, easy to build, powerful smart olfactory display, that can release up to 24 different aromas and allow control of the quantity of the released aroma. The display is capable of absorbing back the aroma, in an attempt to clean the air prior to releasing a new aroma. Additionally, the display includes a smart algorithm that will decide when to release certain aromas. The device controller application includes releasing scents based on a timer, text in English subtitles, or input from external software applications. This allows certain applications (such as games) to decide when to release a scent, making it ideal for gaming. The device also supports native connectivity with games developed using a game development asset, developed as part of this project. The project was evaluated by 15 subjects and it was proved to have high accuracy when the scents were released with 1.5 minutes’ delay from each other.
In this study, a novel method is proposed for determining whether a child between the ages of 3 and 10 has autism spectrum disorder. Video games have the ability to immerse a child in an intense and immersive environment. With the expansion of the gaming industry over the past decade, the availability and customization of games for children has increased dramatically. When children play video games, they may display a variety of facial expressions and emotions. These facial expressions can aid in the diagnosis of autism. Footage of children playing a game may yield a wealth of information regarding behavioral patterns, especially autistic behavior. You can submit any video of a child playing a game to the interface, which is powered by the algorithm presented in this work. We utilized a dataset of 2,536 facial images of autistic and typically developing children for this purpose. The accuracy and loss function are presented to examine the 92.3% accurate prediction outcomes generated by the CNN model and deep learning.
Traditionally, water quality is assessed using costly laboratory and statistical methods, rendering real-time monitoring useless. Poor water quality requires a more practical and cost-effective solution. The machine learning classification approach appears promising for rapid detection and prediction of water quality. Machine learning has been used successfully to predict water quality. However, research on machine learning for water quality index (WQI) prediction is generally lacking. Therefore, this research aims to identify the important features for the WQI, which necessitated the classification of numerous indicators. This study develops four machine learning models (Artificial Neural Network, Support Vector Machine, Random Forest, and Naïve Bayes) based on the WQI and chemical parameters. The Langat Basin in Selangor dataset from the Department of Environment of Malaysia trains and validates each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by several feature selection strategies such as information gain, correlation, and symmetrical uncertainty. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized Random Forest classifier with the WQI parameter selected by the information gain feature selection method achieved the highest performance. The experimental results show that the WQI parameters are more relevant in predicting the WQI than the other variables. Consequently, this result shows that parameter oxygen (DO) and biochemical oxygen demand (BOD) are important features for predicting WQI. The proposed model achieved reasonable accuracy with minimal parameters, indicating that it could be used in real-time water quality detection systems.
Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achieving investment goals depends on selecting the right stocks to buy, sell, or hold. The output of this project is the projected closing prices over the next seven days, which aids investors in making informed decisions. Exponential smoothing (ES) was employed in this study to eliminate noise from the input data. This study utilized exponential smoothing (ES) to eliminate noise from data obtained from the Saudi Stock Exchange, also known as Tadawul. Subsequently, a sliding-window method with five steps was applied to transform the task of time series forecasting into a supervised learning problem. Finally, a multivariate long short-term memory (LSTM) deep-learning (DL) algorithm was employed to predict stock market prices. The proposed multivariate LSTMDL model achieved prediction rates of 97.49% and 92.19% for the univariate model, demonstrating its effectiveness in stock market price forecasting. These results also highlight the accuracy of DL and the utilization of multiple information sources in stock-market prediction.
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