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For investors and financial analysts to make informed decisions, having precise forecasts of gold prices is crucial. This study examined the effectiveness of various time series models in predicting gold prices in Indian Rupeea variety of models, ranging from linear models like Auto Regressive Integrated Moving Average (ARIMA) and Simple Linear Regression, to more complex nonlinear models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) were utilised. In the study, a statistical technique was used to analyse the data collected over a twenty-year period, from January 2001 to December 2019. RMSE and MAPE were used to evaluate the models' performance. The Simple Linear Regression model achieved an RMSE value of 834.67 and a MAPE value of 2.22%, demonstrating its superior performance compared to the other models. The LSTM and GRU models achieved RMSE values of 1160.5 and 1214.8, respectively, suggesting comparable levels of performance. The MAPE values of the LSTM model and the GRU model differed by 2.96%, with the latter being 2.83%. On the other hand, the ARIMA model had a MAPE value of 22.9% and an RMSE value of 7121.1, which was noticeably lower than the previous model. Moreover, the results showed that both LSTM and GRU have the ability to capture nonlinear correlations in the fluctuations of gold prices. [ABSTRACT FROM AUTHOR]

Sentiment analysis is concerned with determining whether a certain material contains online information which expresses positive or negative sentiments. The tools for performing this analysis should be able to identify and assess thoughts and feelings with a reasonable degree of accuracy on feelings that are made openly available by people. It is expected that sentiment analysis would be performed for social media. That is why this paper investigates online social media, as sentiment analysis has become an important subject, and it is one of the approaches employed in the field of natural language processing. Sentiment analysis was applied for an Arabic Twitter dataset in order to identify the feelings expressed by the textual tweets and determine whether they were positive, negative, or neutral. Bigrams and unigrams were used when employing the multinomial Naïve Bayes, Gaussian Naïve Bayes, Logistic Regression, and Support Vector Machines (SVM) machine learning algorithms. The Logistic Regression algorithm achieved the highest accuracy, that is with 63.40%. The Long Short-Term Memory (LSTM) neural network was used for the deep learning-based classification, and it reached an accuracy rate of 70%, a figure which proved to be higher than the results shown in the related works. [ABSTRACT FROM AUTHOR]

The purpose of this article was to highlight the sentiment analysis for specific Arabic tweets related to the COVID-19 Worldwide Epidemic. The technique proposed in this paper focused on using the machine learning algorithm with the purpose of applying sentiment analysis on a dataset which contained 4,575 Arabic tweets on the COVID-19 pandemic while also employing the Logistic Regression and Naive Bayes algorithms as classifiers for comparing the achieved results between them. This study showed the suitability and efficiency of a system using machine learning models for the analysis of Arabic tweets. The experimental outcomes revealed that the highest accuracy was reached by employing the Logistic Regression algorithm”, namely, 97%”. Twitter is one of the most widely used gateways of social media for the people who want to express their opinions and emotions. This study contributes to highlighting the task of sentiment analysis for the Arabic tweets about the COVID-19 pandemic by predicting the people’s awareness about the Coronavirus in the Arab World. [ABSTRACT FROM AUTHOR]

Furniture shopping, when done in the traditional way, is a time-consuming task. A lot of time and effort is put to check different furniture items from different stores, and to make sure that these items match together. We propose an Augmented Reality based mobile application for furniture shopping. The main objective of the proposed application is to enhance users experience while shopping for furniture. We incorporate state-of-the-art technologies that enable interaction with the physical and virtual environment simultaneously. The proposed application allows users to view items from different stores, as well as ’try’ them before buying using AR. Thus, users can choose the right color, shape and size while they are sitting at home. In this work, we followed the waterfall software engineering model and described each main component. Specifically, details of requirement gathering, software design and implementation, and software testing are presented here. The proposed application was implemented using Vuforia. The software testing verified and validated the developed application, and the results were compared in light of related work. For future work, we aim to further improve the users experience in furniture shopping by incorporating more state-of-the-art techniques, such as an AI-based shopping assistant or an ML-based recommender system. [ABSTRACT FROM AUTHOR]

In the field of education, poor spelling is a significant learning obstacle. To help overcome this barrier, this current study designed and developed a mobile-based system entitled: “Let's Spell” in order to help children boost their spelling skills by utilizing up-to-date technical tools necessary for learning. The system was developed by following the generic software systems development process. The main features used for the systems are “exercises” associated with “games-like learning state”. Each game is based on the exercises on "Language Book" and is implemented using interactive voice recognition methods so that each letter in the set of letters and the names of various objects associated with the letter are expressed together aloud. Then the names will be called out to the children, who will in turn read it. If they get it right, they can go to the next level of the game, and in this way, they can continue to improve and succeed. On the other hand, if they get it wrong, a message will appear telling them to try again. In the event that they keep getting it wrong each time a help option is provided, a correction feature will be present to show them how to spell it correctly. Hence this contribute to the provision of a tool that will bring concentration on the practice of learning and enhanced confidence for the learners as well as improving the learning satisfaction. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease is a condition characterized by a progressive symptomatic decline over several years. It causes memory loss and affects daily task performance. Memory loss leads to challenges including remembering people's names, faces, places, or other information. In Saudi Arabia, the prevalence rate for Alzheimer’s disease is increasing and, accordingly, warrants attention and address. Thus, the objective of this work is to support Alzheimer’s patients with mild (early-stage) and moderate (middle-stage) conditions to remain involved in society and continue to live independently. We propose a mobile application which utilizes facial recognition technology and location detection using Google maps. The application aims to improve daily communication, enhancing their ability to perform daily tasks by the embedding of a notification feature. It has location detection to maintain the safety of Alzheimer’s patients, and help prevent them from getting lost by tracking their location. Results have shown that the application has benefited those living with the symptoms of Alzheimer's, and significantly support their daily lives. Therefore, this work highlights the importance of employing artificial intelligence (AI)-based features, i.e., face recognition in this specific case when developing healthcare applications which can have a significant impact on the community. [ABSTRACT FROM AUTHOR]

It is still an open argument that many AR applications in education which require some text on their interfaces impact learning positively. Even though the majority of AR educational tools in public domain contain English-based text, very few Arabic AR tools were adopted for use in teaching and learning, (rephrase the last part, it is not clear). It’s not clear whether user’s and end-user’s adoption of Arabic AR tools for use in teaching and learning is influenced by the system or the environment. That is why this study developed an Arabic alphabet-based AR and examined the subjective involvement on its adoption based on the Technology Acceptance Model (TAM) approach. An interactive experimental evaluation was carried out with the developed app. A sample of 51 participants have participated in the experiment and responded to the structured questionnaire. The analysis of the gathered data indicates that the measures of acceptance and adoption of Arabic Alphabet lies within the Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude toward usage (AT) and Behavioral Intention (BI). These are indicators which were used in the Technology Acceptance Model (TAM). Hence the findings of this study indicate that PEOU had the strongest impact on PU and both of them influence AT which impact BI of using Arabic Alphabet AR app. [ABSTRACT FROM AUTHOR]