Search results

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

Within developing countries, particularly in Africa, there is an emerging literature which highlights the unique obstacles faced by women entrepreneurs who start and develop their own businesses (De Vita, Mari, & Poggesi, 2014; Jamali, 2009; Minniti & Naude, 2010; Naude & Havenga, 2005; Nziku & Struthers, 2018). A key objective of this chapter is to critically appraise some of the conceptual approaches adopted in this literature. In so doing, the authors revisit a seminal paper first developed by Granovetter (1973) which suggested that female entrepreneurs, instead of being disadvantaged by the so-called ‘weak ties’ that bind their business networks, actually enjoy compensating benefits which Granovetter referred to as the strength of weak ties (SWT). Building on the conceptual work of Nziku and Struthers (2018) which developed an innovative taxonomy for analysing the SWT concept within a Principal-Agent (P-A) paradigm, the chapter will set out new insights which challenge some of the assumptions of the extant entrepreneurship literature. In particular, that women are inherently more risk averse in their business decision making than men. The theoretical context for this will be derived from a behavioural economics methodology first developed by Kahneman and Tversky (1979). They introduced the concept of loss aversion as a more realistic approach to attitudes towards risk on the part of entrepreneurs than risk aversion. The chapter contends that the loss aversion perspective may be more appropriate to the decision-making frame adopted by female entrepreneurs, especially in the context of Africa as well as in other developing regions of the world. The chapter will therefore suggest that such an approach can yield fresh insights on the topic of female entrepreneurship which the extant literature heretofore has not addressed, though this will have to be subsequently tested empirically.

This paper aims to investigate the environmental marketing orientation of Muslim entrepreneurs and looks at its relationship with environmental marketing and organizational performance in the context of small and medium enterprises in Indonesian. The study also examines the role of religiosity as a moderator on the relationship between environmental marketing orientation and green marketing.

The paper is empirical and quantitative in nature. The sample of the study is Muslim entrepreneurs in West Java and Central Java Indonesia. The data were analyzed using descriptive statistics and partial least square analysis.

Environmental orientation has a positive relationship with environmental marketing and operational and economic performance. Nonetheless, the study suggests no significant influence of environmental marketing on commercial performance due to “greenwashing” practices. Religiosity appears to moderate the relationship between environmental orientation and environmental marketing practices.

The lack of papers on Islamic marketing makes the depth of discussion somewhat limited.

The recommendation of this study provides a new path to the local government in mitigating the issue of environmental destructions occurring because of entrepreneurs’ business practices. This study has demonstrated the importance of cultivating religious values among society and specifically entrepreneurs as moral guidelines to further strengthen ethical behavior while conducting businesses. The government may endorse more teaching hours on Islamic curriculum at school to create the generation of religious entrepreneurs.

The act of preserving the environments while conducting businesses is one form of worship in Islam as such we call for the elaboration and application of strategies to instill the paradigm of excellent merchants among Muslim.

This paper is the first of its kind which empirically testing the relationship between environmental marketing and firms performance with religiosity as a moderator among Muslim entrepreneurs in Indonesia.

Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction.

In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results.

The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task.

The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.

The purpose of this paper is to develop a conceptual framework which combines the strength of weak ties (SWT) concept with an innovative taxonomy for mitigating principal-agent (P-A) conflicts. The taxonomy highlights the mechanisms through which African women can overcome the obstacles faced when setting up businesses.

The paper discusses the role of “weak ties” networks in entrepreneurial activities and integrates the concept with the key parameters of the P-A paradigm. The aim is to develop a taxonomy (or scorecard) for mitigating the challenges faced by women entrepreneurs in Africa from a P-A perspective. Six P-A parameters are analysed, namely, attitudes towards risk; behaviour-based vs targets-based contracts; asymmetric information; risk-sharing; transaction costs; and verification and monitoring costs.

With the aid of the taxonomy developed in the paper, the authors analyse the channels through which “SWT” networks may impact in mitigating the problems arising from the P-A paradigm. Some implications for women entrepreneurs in Africa are highlighted.

The current conceptual study suggests that the “SWT” concept can be used by African women entrepreneurs to mitigate P-A problems. The authors argue that the original P-A taxonomy developed in the paper fills a conceptual research gap in the existing literature. Embedding the SWT concept within a P-A framework will facilitate further research not only to understand African women entrepreneurs’ attitudes (and responses) towards risk and uncertainty, but this will also facilitate greater understanding of the importance women attach to the role of incentives within their businesses.

The taxonomy presents new insights for understanding the most serious constraints that hinder women entrepreneurs in Africa. The taxonomy will be the basis for a follow-up empirical paper on selected African countries.

The originality of this study lies in the development of an innovative taxonomy which highlights the role of “SWT” social networks towards mitigating the P-A problem among African women entrepreneurs. The paper makes a significant contribution to the literature from a conceptual perspective.

The purpose of this paper is to identify the behaviour of leaders that enhances employee engagement (EE) in organisations. It locates the importance of EE and the role leaders play within the public sector in a developing country and finds answer to the question on the behaviour of the leader that causes employees to be engaged.

The paper uses qualitative method to identify the behaviour of leaders that causes and enhances employees to be engaged. It proposes a framework outlining what the leader does to ensure EE.

Based on results of data analysed, six behaviours of leaders were identified: seeking employee welfare and caring; openness and information flow; conscientiousness; good and cordial relationship; fairness and trust and lastly involvement in decision making.

Limitation of the study was that it did not focus on the entire public sector but was carried out in only one company. It is therefore recommended that further studies could be carried out with focus on other public sector organisations to confirm the leader behaviour identified. Again, a comparative study between public and private sector organisations could be carried out to ascertain if there are differences in the behaviour of the leaders that cause employees to be engaged in these two different sectors.

The study develops a framework outlining behaviour which leaders within organisations could exhibit to enhance EE and thus organisational success.

The paper provides a framework that shows leader behaviour that causes EE in organisations in a developing country. This framework will be helpful to leaders in organisations to behave in specific ways to enhance EE and for researchers who want to conduct research in this field of study.

This study aims to examine the impact of green marketing adoption on non-profitable organizations’ performance in Jordan.

A structured questionnaire was developed to collect the needed data and test the developed hypotheses to investigate the impact of green marketing adoption on non-profitable organizations’ performance. The data was collected using a self-administered questionnaire distributed to 183 respondents in non-profitable organizations operating in Jordan.

The findings indicate that the extent of green marketing adoption by profitable organizations in Jordan is relatively moderate. They also confirm that the corporate performance of non-profitable organizations is positively associated with the extent of adoption of green marketing dimensions, particularly environmental and social responsibility aspects.

Reviewing the existing literature revealed that similar studies had not previously been undertaken in Jordan as a developing country.

RF transceiver module is considered a vital part of any wireless communication system. This module consists of two important parts the RF transceiver and analog-to-digital converter (ADC). Usually, both these parts – RF transceiver and ADC – are used to enhance the perspective of size and power. The data processing in 4G communication makes hurdles and need research attention to make it faster and smaller in size. Accuracy and fast processing are the critical challenges in the modern communication system.

After theoretical and practical investigations, this research work proposes key new techniques for the RF transceiver module. These techniques will make RF transceiver small, power-efficient and on the other hand, make dual SAR-ADC more effective as well. The proposed design has no intermediate frequency where the RF transceiver is reduced its major blocks from five to four, which includes crystal oscillator, phase lock loop, power amplifier and low noise amplifier. Moreover, the shared circuitry is introduced in the architecture of the SAR-ADC for the production of dual outputs, specifically in bootstrapped switch and comparator.

The miniaturized RF transceiver and SAR-ADC are well tested separately before the plantation on the printed circuit board (PCB). The operating voltage and frequency of the RF transceiver module are 1.2 V and 5.8 GHz, where the sampling rate, bandwidth and output power are 25 MHz, 200 MHz and 5 dBm, respectively. The core area of the PCB is 58.13 mm². The bandwidth efficiency is 93% using surface acoustic wave less transmitter. The circuit is based on the library of 90 nm CMOS technology.

The entire circuit is highly synchronized with the input and reference clocks to avoid self-interference.

Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.

The new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.

The new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.

The new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

Two key concepts in organizational behaviour research, employee engagement and organizational commitment are examined in this bibliometric analysis.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses framework is used for the compilation of the papers, and the VOSviewer application with the SCOPUS database is used for bibliometric analysis. 387 authors wrote a total of 138 articles.

Through the examination of an extensive collection of scholarly articles, this research pinpoints significant patterns, noteworthy writers and recurring themes in the literature. The study helps to create better organizational practices by providing a roadmap for future research as well as a mapping of the current condition of the field.

The study is an original piece of work by the author with no conflict of interest with any party, person or organization.