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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]

Recently, Sentiment Analysis (SA) has become a crucial area of research as it enables us to gauge people's opinions from various sources such as student evaluations, social media posts, product reviews, etc. This paper aims to create an Arabic dataset derived from student satisfaction surveys conducted at the University of Jeddah regarding their subjects and instructors. In addition, this study presents an evaluation of classical machine learning models such as Naive Bayes, Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest classifier for Arabic SA, whereas the results are compared using various metrics. Furthermore, AraBERT was used for the pre-trained transformer to improve the performance, achieving an accuracy of 78%. The paper fills the lack of SA research in the education domain in the Arabic language. [ABSTRACT FROM AUTHOR]

The ability to accurately predict how students will perform has a significant impact on the teaching and learning process, as it can inform the instructor to devote extra attention to a particular student or group of students, which in turn prevents those students from failing a certain course. When it comes to educational data mining, the accuracy and explainability of predictions are of equal importance. Accuracy refers to the degree to which the predicted value was accurate, and explainability refers to the degree to which the predicted value could be understood. This study used machine learning to predict the features that best contribute to the performance of a student, using a dataset collected from a public university in Jeddah, Saudi Arabia. Experimental analysis was carried out with Black-Box (BB) and White-Box (WB) machine-learning classification models. In BB classification models, a decision (or class) is often predicted with limited explainability on why this decision was made, while in WB classification models decisions made are fully interpretable to the stakeholders. The results showed that these BB models performed similarly in terms of accuracy and recall whether the classifiers attempted to predict an A or an F grade. When comparing the classifiers' accuracy in making predictions on B grade, the Support Vector Machine (SVM) was found to be superior to Naïve Bayes (NB). However, the recall results were quite similar except for the K-Nearest Neighbor (KNN) classifier. When predicting grades C and D, RF had the best accuracy and NB the worst. RF had the best recall when predicting a C grade, while NB had the lowest. When predicting a D grade, SVM had the best recall performance, while NB had the lowest. [ABSTRACT FROM AUTHOR]

Riyadh is the most populous city in Saudi Arabia, with a population of over five million people. The governmental and economic centers of Saudi Arabia are located in the city. Due to the fact that the metropolitan region that surrounds Riyadh is continuously growing and expanding, appropriate planning is essential. To be able to formulate efficient plans, one needs access to trustworthy facts and information. Failing to have a clear picture of the future renders planning inefficient. Along with a hybrid time-series prediction of the expansion of the wider Riyadh metropolitan area, an urban growth forecasting model was constructed for the Riyadh region as part of this study. This model was used to make projections about the city's future population. This prediction was conducted with the application of Linear Regression (LR), Seasonal Auto-Regressive Integrated Moving Average (SARIMAX), and Auto-Regressive Integrated Moving Average (ARIMA). The dataset for this study consisted of satellite images of the region surrounding Riyadh that were acquired between 1992 and 2022. Mean Absolute Percentage Error (MAPE) was applied to measure the performance of the proposed hybrid models. The calculated MAPE vales are 2.0% for SARIMAX, 12% for LR, and 22% for ARIMA. As a consequence, the hybrid model's forecast for the future of the region suggests that the projections made regarding the expansion are keeping pace. [ABSTRACT FROM AUTHOR]

This study investigated the strategic planning procedure used by the University of Jeddah to determine which of its efficacy criteria are the most significant for future development. A university's performance is founded on its ability to capitalize on its specialization and set of skills obtained through meticulous planning and development and involves setting goals using analysis tools to compare options and prioritize constructs. Evaluation approaches to strategic planning lack adaptability and durability. Thus, a high-level deductive instrument that aggregates trade-offs and prioritizes the most essential aspects is needed. This study used the Fuzzy Analytical Hierarchical Procedure (FAHP) to examine whether the University of Jeddah's strategy formulation process improves strategy and planning. This study defined the objectives and criteria, established pairwise comparisons based on the owners of the strategic plan and the faculty and administration questionnaire responses, assigned weights to each criterion, verified their consistency, and ranked them in importance order. This study showed that FAHP can help groups make strategic planning decisions in universities. [ABSTRACT FROM AUTHOR]

The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. [ABSTRACT FROM AUTHOR]

Individuals who are deaf or those having hearing problems can communicate through alternative modes of communication, such as sign language, both within and outside their community. As a result, the sign serves as a tool that mediated communication goals. With the aid of recent advances in computer vision, there has been promising progress in the fields of motion and gesture detection using deep learning. In this work, convolutional neural networks are utilised to develop a model for recognising the Arabic language's alphabet signs in order to aid communication goal. The research used the Arabic Alphabets Sign Language Dataset (ArASL2018), which contains sets of images indicating each specific sign for each letter of the alphabet. Experimental analysis was carried out using the proposed model. The results of the analysis reveal a recognition accuracy of 94.46%. Furthermore, the comparative analysis with the previous studies indicates that the proposed model outperformed all of them in terms of recognition accuracy. [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]

There is of course, a question about the use of any software, and that is whether or not people ever read the "terms and conditions". In the last few years, Saudi Arabia has become very eager to expand and modernize all of its technical and knowledge fields to achieve a high quality of life by motivating students to participate in problem solving and disseminating their ideas by using applications and sites that help the general public achieve their goals in the field of Artificial Intelligence, Security, and user experience. The goals of this study are to develop an application that simplifies reading the terms and conditions by highlighting the important terms and conditions that may cause problems and to help users to be aware of any potential problems that could occur in the system. The app's final design and look should also enhance the appeal to the user. The results of this study will further online resource use by providing more justification for subscription and usage of online resources, as many online subscriptions and usages are based on agreeing to terms and conditions, and in the current Internet era, people must have agreed to these terms and conditions at least once in their lifetime. [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]

As a futuristic emblem, smart cities represent a goal to provide all of the services required for a highquality existence while conserving natural resources to the maximum extent possible. It turns out that the "Line City in Saudi Arabia" is the most famous and modern of these smart cities in terms of preserving 100 percent natural life, owing to the fact that they rely totally on natural energy and do not use any automobiles. The issues and challenges that have been highlighted will assist in the improvement of municipal management. One of the most generally implemented projects in the field of smart cities is the technological and integrative developments of the use of sensors and data collecting in conjunction with rising difficulties aimed at enhancing human well-being and improving human livelihood. This is especially true in Saudi Arabia, where visitors can arrive quickly by just walking through the checkpoints, resulting in a journey of little more than a few minutes to reach any regular destination. It also splits the city into many different levels of the ground, enabling for the conservation of natural excess energy. [ABSTRACT FROM AUTHOR]

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TFIDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news. [ABSTRACT FROM AUTHOR]

This research is concerned with the data generated during a network transmission session to understand how to extract value from the data generated and be able to conduct tasks. Instead of comparing all of the transmission flags for a transmission session at the same time to conduct any analysis, this paper conceptualized the influence of each transmission flag on network-aware applications by comparing the flags one by one on their impact to the application during the transmission session, rather than comparing all of the transmission flags at the same time. The K-nearest neighbor (KNN) type classification was used because it is a simple distance-based learning algorithm that remembers earlier training samples and is suitable for taking various flags with their effect on application protocols by comparing each new sample with the K-nearest points to make a decision. We used transmission session datasets received from Kaggle for IP flow with 87 features and 3.577.296 instances. We picked 13 features from the datasets and ran them through KNN. RapidMiner was used for the study, and the results of the experiments revealed that the KNN-based model was not only significantly more accurate in categorizing data, but it was also significantly more efficient due to the decreased processing costs. [ABSTRACT FROM AUTHOR]

Whereas Big Data analytics can provide a variety of benefits, processing heterogeneous data comes with its own set of limitations. A transaction pattern must be studied independently while working with Bitcoin (BTC) data. Hence, this study examines twitter data related to BTC and investigate communications pattern on BTC transactional tweet. Using the hashtags #BTC or #BTC on Twitter, a vast amount of data was gathered, which was mined to uncover a pattern that everyone either (speculators, teaches, or the stakeholders) uses on Twitter to discuss BTC transactions. This aim is to determine the direction of BTC transaction tweets based on historical data. As a result, this research proposes using Big Data analytics to track BTC transaction communications in tweets in order to discover a pattern. Hadoop platform MapReduce was used. The finding indicate that in the map step of the procedure, Hadoop's tokenize the dataset and parse them to the mapper where thirteen patterns were established and reduced to three patterns using the attributes previously stored data in the Hadoop context, one of which is the Emoji data that was left out in previous research discussions, but the text is only one piece of the puzzle on BTC transaction interaction, and the key part of it is "No certainty, only possibilities" in BTC transactions. [ABSTRACT FROM AUTHOR]

This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction. [ABSTRACT FROM AUTHOR]

COVID-19 and other neighbouring diseases spread widely, resulting in a global epidemic that was impossible to manage and control. While numerous measures have been put in place to detect an infected person and protect uninfected areas from contracting these contagious diseases, the spread of diseases like COVID-19 continues to be rapid. As of the time of writing this paper, the number of affected people has continued to rise, and there is no clear indication of the number of people who are infected but have gone undiscovered and are spreading the infections. That is why, in order to combat the threat of contiguous disease spread, this research presented an upgraded autonomous socio-contact tracing system on a mobile platform. As a result, a generic system development process was used to create a system that allows an infected person who has been tested positive to track their electromagnetic ID card in order to determine their exact location and the risk of spreading contagious diseases. by an autonomous smart assistant that assists in describing symptoms. As a result, the system is equipped with notifications alerts for the stages of social group identification, processing, and control in order to avoid the spread of contagious diseases. The development of this system is critical for controlling epidemic diseases that are spreading over the world (particularly COVID-19) and posing a threat to people's lives. Furthermore, it contributes to a greater understanding of the seriousness of epidemic diseases and how to avoid them. [ABSTRACT FROM AUTHOR]

Land use and census data are of a significant value to urban planners, decision-makers, and investors. Important decisions are made based upon historical data, i.e, inductive reasoning. Such data become increasingly valuable at a fine-grain level to decision-makers in businesses where audience segmentation is needed. In rapidly developing countries, land use and census data are widely available at a coarse-grain level. However, accurate fine-grain mapping of such data is a very challenging task. In this paper, we propose a hierarchical methodology that enriches Geographical Information Systems (GIS) by utilizing existing datasources, rule-based algorithms, and workers (mechanical turks) to identify and map socioeconomic data at a fine-grain level. The proposed methodology was tested and evaluated on multiple cities in Saudi Arabia, with an overall accuracy of 90%. The output of this work, a GIS enriched with socioeconomic data and population count, is of high importance to decision makers and can be utilized in different problems to help make a better informed decision. [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]

Copyright of International Journal of Innovation (2318-9975) is the property of International Journal of Innovation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

With the tremendous technological progress and the widespread use of a variety of technologies, we note how smart cities are providing services efficiently by using technologies. The aim of this project is to build a Smart Traffic Control System (STCS) to facilitate and optimize traffic flow, minimize traffic congestion, and reduce the waiting time by detecting the density on each street. This work has been carried on four phases. Firstly, collecting data by a questionnaire and we received 331 responses. Secondly, using Proteus simulation. Thirdly, building a low fidelity prototype, and fourthly: building the STCS model by using hardware (Arduino tools) and software (Arduino Software IDE). Finally, we learned how to build a system and we recommend using such a system in busy roads to reduced congestion and making traffic flow more efficient. [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]

Dyslexic individuals have serious difficulties in learning to read, and several software programs have been developed to overcome them. Previous research studies found that in most cases, providing correct spelling and word recognition with greater accuracy constitutes the key function. However, more research has to be conducted on the software programs for dyslexia. Another issue is visual-spatial attention; the aspect of directing attention to a location of what should be learnt while reading has been ignored in most studies. This research analysis of the previous studies outlines some research gaps as follows: recording children's eye movements during reading were uncertain. Another research gap is understanding the frequencies of Arabic word recognition for dyslexic children the meaning of the Arabic word during reading for dyslexic were not adequately presented in the previous research. Therefore, this paper presents an experimental study on identifying Arabic words and their meaning by dyslexic individuals focusing on the sequence of interactions. A decision-making analysis of the key interactive components of the Arabic alphabet puzzle app for dyslexics is performed, and the finding of the study reveals that the speed and accuracy of visual word recognition and meaning description increases and significantly influences visual short-term memory The analysis of the most important factors that influence all the other factors of the user experience of the Arabic alphabet puzzle app for dyslexics shows that 'desirability' constitutes the key attribute in enhancing visual-spatial attention and accelerating word recognition and meaning description for Arabic-speaking dyslexics. [ABSTRACT FROM AUTHOR]

Learning is not limited to a certain age group or a formal study environment. However, the first eight years, and particularly the first three years, are the most important because the effects of the care and attention provided to the child during this period will continue throughout his/her life. A child's early learning processes develop his/her educational abilities and skills and fosters his/her academic achievements. Preschool can be very beneficial to the development of and learning for children, but there are differences between countries in this area. Preschool programmes of high quality can reduce repetition in the classroom and raise achievement scores for students (Jacobson and Linda 2008). There are many applications in the Middle East that teach children but none link the vowels of the Arabic alphabet characters and sound for each character and the training to select the correct format of the Arabic alphabet (e.g. fatha, dama and kasra). This study aims to develop an educational application for children aged 3-5 to help improve their skills and abilities. The application will prepare them for school by teaching them the Arabic letters, pronunciation and the short vowels (fatha, dama and kasra). The application also aims to help children communicate and interact more effectively with their external environment by employing several theories about learning skills, such as Piaget's "Theory of Cognitive Development", Dale's "Cone of Learning" and Bloom's "Taxonomy of Cognitive Goals". The application will focus on the first three levels of Bloom's "Taxonomy Pyramid", which are "Remember", "Understand" and "Apply". The application will be designed to aid children with their Arabic pronunciation, aural and oral skills, as well as to train and educate them in preparation for attending school. The expected result is that it will be considerably better than educational books for children's education. Combining education and play in e-learning applications is an important way to attract children. [ABSTRACT FROM AUTHOR]

Sustainable products and their marketing have played a crucial role in developing more sustainable consumption patterns and solutions for socio-ecological problems. They have been demonstrated to significantly decrease social consumption problems. Neuromarketing has recently gained considerable popularity and helped companies generate deeper insights into consumer behavior. It has provided new ways of conceptualizing consumer behavior and decision making. Thus, this research aims to investigate the factors influencing managers' decisions to adopt neuromarketing techniques in sustainable product marketing using the fuzzy analytic hierarchy process (AHP) approach. Symmetric triangular fuzzy numbers were used to indicate the relative strength of the elements in the hierarchy. Data were collected from the marketing managers of several companies who have experience with sustainable product marketing through online shopping platforms. The results revealed that the accuracy and bias of neuromarketing techniques have been the main critical factors for managers to select neuromarketing in their business for advertising and branding purposes. This research provides important results on the use of neuromarketing techniques for sustainable product marketing, as well as their limitations and implications, and it also presents useful information on the factors impacting business managers' decision making in adopting neuroscience techniques for sustainable product development and marketing. [ABSTRACT FROM AUTHOR]