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In this digital era, email is the most pervasive form of communication between people. Many users become a victim of spam emails and their data have been exposed.

Researchers contribute to solving this problem by a focus on advanced machine learning algorithms and improved models for detecting spam emails but there is still a gap in features. To achieve good results, features also play an important role. To evaluate the performance of applied classifiers, 10-fold cross-validation is used.

The results approve that the spam emails are correctly classified with the accuracy of 98.00% for the Support Vector Machine and 98.06% for the Artificial Neural Network as compared to other applied machine learning classifiers.

In this paper, Point-Biserial correlation is applied to each feature concerning the class label of the University of California Irvine (UCI) spambase email dataset to select the best features. Extensive experiments are conducted on selected features by training the different classifiers.

The purpose of this study is to identify the impact of online learning on university librarians’ professional development and library services.

A mixed-methods study through an explanatory research design was applied to address the study’s objectives. Quantitative data were gathered from 341 librarians working in 221 universities, while qualitative data were gathered from 27 experts working in 21 different universities of Pakistan.

The findings of the study revealed that online learning has a significant positive impact on the professional development of university librarians. Results revealed that online learning assists in the provision of sustainable, innovative library services in university libraries.

The study has offered a model in light of the study's quantitative and qualitative findings. It contributes to theoretical understanding by expanding the existing knowledge base. It offers managerial insights, enabling the development of policies that foster the professional development of library personnel and the implementation of smart library services.

This study aims to examine the perceptions of students about learning science and physics using the engineering design process (EDP).

The study employed a mixed-methods research design: The quantitative session features a pre–post-test control group study. In the qualitative aspect, the study conducted semistructured interviews for data collection. In the experimental group, the flipped classroom (FC) model and an instructional design are combined to design, develop and implement a physics course using the steps of the EDP, while the conventional method was applied to the control group. The respondents are students of the Department of Mechanical Engineering at Cao Thang Technical College in Vietnam for the academic year 2022–2023. The control and experimental groups are composed of 80 students each. An independent sample Mann–Whitney U test is applied to the quantitative data, while thematic analysis is employed for the qualitative data.

The results demonstrate a statistically significant difference between the experimental and control groups in terms of perceptions about learning science and physics using the EDP, which, when combined with a FC, enhances physics learning for engineering students.

This study implemented the EDP in teaching physics to first-year engineering students in the Department of Mechanical Engineering using the combined FC and instructional design models. The results revealed that a difference exists in the perception of the students in terms of integrating the EDP into learning physics between the experimental and control groups. The experimental group, which underwent the EDP, obtained better results than did the control group, which used the conventional method. The results demonstrated that the EDP encouraged the students to explore and learn new content knowledge by selecting the appropriate solution to the problem. The EDP also helped them integrate new knowledge and engineering skills into mechanical engineering. This research also introduced a new perspective on physics teaching and learning using the EDP for engineering college students.

The research findings are important for teaching and learning physics using EDP in the context of engineering education. Thus, educators can integrate the teaching and learning of physics into the EDP to motivate and engage student learning.

Using the EDP combined with a FC designed under stages of the analyze, design, develop, implement and evaluate (ADDIE) model has enhanced the learning of physics for engineering college students.

Gamification is an emerging technique of applying game elements to difficult and tedious learning activities to make them fun and exciting. This study aims to review the scientific landscape of the library’s readiness to adopt gamification with context to application in teaching and learning purposes based on computational tools. The present research also aims to study the growth of literature on gamification, to identify the most contributing authors, countries, affiliations and journals and collaboration status with different geographical settings. The study will also identify the most influential paper on the area with the highest citation and Altmetric Attention Score (AAS) as well as analyzing the keywords for locating the research trend in the subject area.

The study has adopted Scientometric and Altmetric approach by considering the research outputs of a decade (2013–2022) from Scopus database. First, the required data has been searched using appropriate keywords forming the search strategy by running title–abstract–keywords considering the limitation in the system. The exported data is systematically visualized for performing science mapping like the collaboration of authors, countries, organizations and co-occurrence of keywords using VOSviewer. For finding the Altmetrics score and Mendeley readership of the influential research works, the system Dimension.ai is further used.

The study found 928 records indicating an exponential growth over the years with total 2,750 authors. Samuel Kai Wah Chu from the University of Hong Kong, China, is the most contributed author. Spain and the USA are highly productive countries, but there needs to be a strong collaboration pattern among authors. It is found that gamification is widely applied in education discipline than any other. Some of the libraries have already implemented gamification tools for learning purposes in their services. The research on gamification still lacks social media attention and needs to be promoted more through various social media platforms for greater visibility.

The study explores the global scientific literature to identify the library’s awareness of implementing gamification tools in their services for teaching and learning purposes. As per the author’s knowledge, no such study has been conducted until date with such aims and objectives through the application of both Scientometrics and Altmetrics approaches.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.

To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Service-learning (SL) is a widely accepted pedagogy that can enrich the learning experience for students in higher education while they apply their skills in a meaningful community service. This research is part of a larger project that aimed to motivate educational achievement among youths living in a priority neighborhood through SL. Toward this goal, this study investigated the impact of SL on the college students from a college information technology programmer-analyst (ITPA) program, who were deployed as role models to youths in a priority neighborhood on the east coast of Canada.

The overall project used the design-based methodology. Seven college students were deployed in two phases to a community center as role models for the delivery of science, technology, engineering, art and mathematics (or STEAM) programs to youths living in a priority neighborhood. Data were collected using open-ended survey, journal entries and focus groups and was qualitatively analyzed by drawing on two frameworks: the experiential-learning framework by Kolb (1984) and the conceptual SL framework by Ash and Clayton (2009).

The findings describe the outcome of the college students' experiences in SL with respect to the development of skills and capacities needed by employers. Specifically, their experiences mirrored all aspects of the two frameworks applied. Therefore, the study validates the use of SL pedagogy in higher education. In addition, the study identified the role of SL as an integration strategy for international students. While the research contributes to the wider SL conversation for policymakers, faculty and administrators of higher education, it also promotes development opportunities for college students.

The integration of SL pedagogy is widespread among programs in higher education. However, there are no common SL frameworks used in literature. The study is novel in that it combines two theoretical frameworks – Kolb (1984) and Ash and Clayton (2009) in explaining the outcomes. In addition, it uses two high-impact educational practices – SL and role modeling to improve educational attainment for college students.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.