The Use of Recurrent Nets for the Prediction of e-Commerce Sales

The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance

A Comparison of Text-Driven and Coursebook Materials: Investigating their Potential Learning Effects on EFL Learners’ Perceptions and Communicative Performance Using Multiple Research Methods

This study compared the potential effects of Text-Driven (TD) and Coursebook (CB) materials on learners' perceptions and interactions in EFL classrooms. It also explored which teaching materials are more likely to facilitate learners’ communicative competence through theoretical and empirical evaluations. 82 EFL female students at A2 (CEFR) level were selected in this study from the English Language Institute (ELI) at the University of Jeddah in KSA and divided into two comparable intact classes taught by the researcher. The first group was taught the developed Text-Driven materials, whereas the second group was taught the Coursebook materials. The study adopted a multiple-method research design. Data were collected through six methods: questionnaires, individual interviews, classroom interaction analysis, teachers’ observations, virtual forums, and pre-post communicative tests. The data revealed that while both TD and CB materials were viewed positively by the participants, Text-Driven showed a number of advantages over coursebook materials in developing learners’ engagement and classroom interactions. The findings demonstrated that the frequencies of learners' turns using L1 or L2 are higher in the TD group than in CB and that the observed interactional patterns differ considerably among the groups. The TD interactional patterns involved more open than closed responses, and their interaction was meaningful, personally engaging, and focused on both content and forms compared to their counterpart. Similarly, the two ELI instructors who observed the researcher’s TD and CB video-recoded classes commented that TD materials seemed more effective than the CB in developing classroom interaction, resulting in meaningful interactional patterns among TD learners. The pre-post communicative test results supported the previous data and showed that the TD materials are more likely to accelerate the learners’ overall English “communicative competence” than the CB materials. The theoretical content analysis of the coursebook unit provided further evidence that most of the tasks are controlled and aimed at practising language points and thus may not facilitate L2 communicative competence. The findings of this study would benefit TESOL/Applied linguistics stakeholders as a flexible communicative teaching model was proposed. It reflects the findings of language learning studies that explore how second language competence can be developed. Furthermore, these iii results may assist the ELI and other contexts in considering the significance of L2 materials development and its potential impact on learners’ engagement and communicative performance. In light of these findings, several recommendations are proposed

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of Timestamp Influence on Bitcoin Value

The transaction and market of bitcoin is volatile, meaning it's uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction of timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a "well-formulated" statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency's performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market

Leveraging crowdsourcing in clould application development

The emergence of crowdsourcing has enabled workforce seekers to delegate various tasks to the unknown public to accomplish. Crowdsourcing serves in Software development where projects often fail due to the inability to find and allocate expertise. In cloud application (i.e. Software as Service - [SaaS]) development, projects severely suffer from shortage of expert developers due to its recent and rapid evolution. Therefore, many software manufacturers resort to crowdsourcing to find and recruit experts in SaaS development. To address this need, we conducted a survey of SaaS crowdsourcing to identify its challenges and to explore the crowdsourcing facilities that support addressing these challenges. Furthermore, we review two widespread existing approaches for software development crowdsourcing and propose a novel approach. Additionally, we discuss the challenges in SaaS development crowdsourcing and evaluate our proposed approach for its ability to address these challenges. Finally, we provide future adopters with a list of attributes to assist them in choosing the proper crowdsourcing service. This paper aims to provide a state-of-the-art assessment of the work carried out so far in applications development crowdsourcing and proposes recommendations to enhance it

Towards an effective crowdsourcing recommendation system: A survey of the state-of-the-art

Crowdsourcing is an approach where requesters can call for workers with different capabilities to process a task for monetary reward. With the vast amount of tasks posted every day, satisfying workers, requesters, and service providers-who are the stakeholders of any crowdsourcing system-is critical to its success. To achieve this, the system should address three objectives: (1) match the worker with a suitable task that fits the worker\u27s interests and skills, and raise the worker\u27s rewards; (2) give requesters more qualified solutions with lower cost and time; and (3) raise the accepted tasks rate which will raise the aggregated commissions accordingly. For these objectives, we present a critical study of the state-of-the-art in recommendation systems that are ubiquitous among crowdsourcing and other online systems to highlight the potential of the best approaches which could be applied in a crowdsourcing system, and highlight the shortcomings in the existing crowdsourcing recommendation systems that should be addressed

A MapReduce Based Approach for Secure Batch Satellite Image Encryption

The overarching goal of this research was to examine the state of satellite imagery security in relation to its deteriorating form due to rising demand. The most common approaches to safeguarding satellite images during transmission across transmission networks, which are not protected by standard encryption, are the focus of this investigation. Since satellite imagery can be encrypted both in transit and while stored on a computer’s hard drive, we put the suggested Image Encryption System to the test by applying it to a collection of satellite photos. Concurrently encrypting data and running MapReduce jobs is key to the study methodology employed. This will be carried out in the Hadoop ecosystem, where an innovative method of analysing random numbers for use in Image encryption will be put to the test. The encryption was processed using MapReduce in the Hadoop ecosystem. Images were saved as BMP files with added security metadata. The evaluation of experiments was based on four (4) indicators. It was found that the processing time for batch encryption calculations grew in proportion to the amount of computations. All cluster, map, and reduction processes were put to the test using encrypted images, exposing load balancing difficulties and inefficiencies. Histogram analysis, the basis of an image encryption technique, provides evidence that the encrypted pixel values are consistent. Therefore, compared to other methods, such as a histogram or information entropy, this one is superior. Because of how it was crafted, it can withstand even the most sophisticated attacks without being compromised

Identification of critical factors affecting the students’ acceptance of Learning Management System (LMS) in Saudi Arabia

Objective of the study: The objective is to identify factors that influence student’s acceptance of Learning Management System (LMS) using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study investigates how UTAUT factors affect students’ intention and attitudes to use Blackboard as a LMS at King Abdulaziz University in Saudi Arabia. Methodology/approach: This study proposes a research model based on UTAUT factors ‘Effort Expectancy (EE), Performance Expectancy (PE), Perceived Functionality (PF), Facilitating Condition (FC), Social Influence (SI)’, Behavioural Intention to use (BI) and Usage Behaviour. The survey research methodology was adopted using questionnaire to identifying factors that influencing the students’ intention and attitudes towards LMS. Originality/relevance: The study relates corporate LMS to support educational institutions to enhance the students’ acceptance and attitudes to use in Saudi Arabia. Main result: The results indicate that PE, PF, FC and SI factors were significant and directly influence on students' BI Blackboard. Both PE and FC are second factors that affect students’ intention and EE factor does not impact the student's BI. Theoretical/methodological contributions: The study contributes to the body of knowledge in developing the research model using UTAUT model by showing the factors affecting the students’ intention in using LMS and usage behavior in Saudi Arabia. Social/management contributions: This study contributes in understanding the critical factors affecting the Saudi Arabia students’ intentions in using Blackboard. Another study should be extended to other universities by using different methods to test the model and incorporate different moderating factors to accept and use the BlackboardObjetivo del estudio: El objetivo es identificar los factores que influyen en la aceptación por parte del estudiante del Sistema de Gestión del Aprendizaje (LMS) utilizando el modelo de Teoría Unificada de Aceptación y Uso de la Tecnología (UTAUT). Este estudio investiga cómo los factores UTAUT afectan la intención y las actitudes de los estudiantes para usar Blackboard como LMS en la Universidad King Abdulaziz en Arabia Saudita. Metodología / enfoque: Este estudio propone un modelo de investigación basado en los factores UTAUT 'Expectativa de esfuerzo (EE), Expectativa de rendimiento (PE), Funcionalidad percibida (FP), Condición facilitadora (FC), Influencia social (SI)', Intención conductual para usar (BI) y Usage Behavior. La metodología de investigación de la encuesta se adoptó utilizando un cuestionario para identificar los factores que influyen en la intención y las actitudes de los estudiantes hacia el LMS. Originalidad / relevancia: El estudio relaciona los LMS corporativos con el fin de ayudar a las instituciones educativas a mejorar la aceptación y las actitudes de los estudiantes para su uso en Arabia Saudita Resultado principal: Los resultados indican que los factores PE, PF, FC y SI fueron significativos y tuvieron una influencia directa en el BI Blackboard de los estudiantes. Tanto la educación física como la FC son segundos factores que afectan la atención de los estudiantes y el factor EE no afecta el BI del estudiante. Contribuciones teóricas / metodológicas: El estudio contribuye al cuerpo de conocimiento en el desarrollo del modelo de investigación utilizando el modelo UTAUT al mostrar los factores que afectan la intención de los estudiantes en el uso de LMS y el comportamiento de uso en Arabia Saudita. Contribuciones sociales / de gestión: Este estudio contribuye a comprender los factores críticos que afectan las intenciones de los estudiantes de Arabia Saudita al utilizar Blackboard. Otro estudio debería extenderse a otras universidades utilizando diferentes métodos para probar el modelo e incorporar diferentes factores moderadores para aceptar y utilizar BlackboardObjetivo do estudo: O objetivo é identificar os fatores que influenciam a aceitação do aluno do Sistema de Gestão de Aprendizagem (LMS) usando o modelo da Teoria Unificada de Aceitação e Uso da Tecnologia (UTAUT). Este estudo investiga como os fatores UTAUT afetam a intenção e as atitudes dos alunos para usar o Blackboard como um LMS na King Abdulaziz University, na Arábia Saudita. Metodologia / abordagem: Este estudo propõe um modelo de pesquisa baseado nos fatores UTAUT 'Expectativa de Esforço (EE), Expectativa de Desempenho (PE), Funcionalidade Percebida (FP), Condição Facilitadora (FC), Influência Social (SI)', Intenção Comportamental usar (BI) e comportamento de uso. A metodologia de pesquisa da pesquisa foi adotada por meio de questionário para identificar os fatores que influenciam a intenção e as atitudes dos alunos em relação ao LMS. Originalidade / relevância: O estudo relaciona o LMS corporativo para apoiar instituições educacionais a fim de aumentar a aceitação e as atitudes dos alunos para uso na Arábia Saudita. Resultado principal: Os resultados indicam que os fatores PE, PF, FC e SI foram significativos e influenciam diretamente no quadro negro de BI dos alunos. Ambos PE e FC são segundos fatores que afetam a atenção dos alunos e o fator de EE não afeta o BI do aluno. Contribuições teóricas / metodológicas: O estudo contribui para o corpo de conhecimento no desenvolvimento do modelo de pesquisa usando o modelo UTAUT, mostrando os fatores que afetam a intenção dos alunos em usar LMS e o comportamento de uso na Arábia Saudita. Contribuições sociais / de gestão: Este estudo contribui para a compreensão dos fatores críticos que afetam as intenções dos alunos da Arábia Saudita ao usar o Blackboard. Outro estudo deve ser estendido a outras universidades, usando diferentes métodos para testar o modelo e incorporar diferentes fatores moderadores para aceitar e usar o Blackboar

RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals

Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram’s (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model’s efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques

An Enhanced Autonomous Socio-Contact Tracing System of the Spread of Contiguous Diseases

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