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Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

This study aims to conduct a quantitative meta-analysis of previous research on lifelong vocational education to generate generalized conclusions about its effects, set directions for future lifelong vocational education and identify implementation measures.

To conduct a meta-analysis on research results that have a heterogeneous distribution, it is important to specify the analysis category for examining the effects of research variables.

First, lifelong vocational education has an effect on dependent variables. And action appears to have the highest effect size on dependent variables. Next, when calculating the size of variables that had an effect on lifelong vocational education by educational type, the effect size of informal education was found to be larger than that of formal education. Finally, regarding the effect on the participants, office workers were influenced most, followed by university students, North Korean defectors, job seekers and foreigners.

Although this study attempted to conduct an in-depth analysis of subcomponents, it was not possible to analyze variables at a more detailed level. Therefore, future studies should aim to conduct a more comprehensive analysis of different variables based on a wider composition. Because lifelong vocational education is relevant to people’s daily lives, it should be investigated in the context of their personal characteristics and social backgrounds.

This research was designed to uncover general effects of lifelong vocational education and discover relevant variables affecting lifelong vocational education in South Korea. A meta-analysis of 15 studies with 67 subgroups examining lifelong vocational education was conducted.

In the current era of VUCA (Volatility, Uncertainty, Complexity and Ambiguity), lifelong vocational education needs to be organized systematically, unlike in the past. With the rapid advancements in technology influenced by artificial intelligence and the fourth industrial revolution, there is a surge in social demands for continued reeducation and redevelopment of employees to prepare for talent development paradigm innovation, increasing unemployment among unskilled workers and competence enhancement needs among job seekers and employed individuals.

This study aims to conduct a quantitative meta-analysis of previous research on lifelong vocational education to draw generalized conclusions on its effectiveness and discuss its implications for implementation measures. Specifically, this study will analyze the general effect size; differences in the effect size among different dependent variable groups; and the effect size based on lifelong vocational education participants.

Classifying emails as ham or spam based on their content is essential. Determining the semantic and syntactic meaning of words and putting them in a high-dimensional feature vector form for processing is the most difficult challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model.

In this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets, SpamAssassin and Enron, are used in the experimentation.

In the first set of experiments, machine learning classifiers, the support vector machine (SVM) model, perform better than other machine learning methodologies. The second set of experiments compares the deep learning model performance without embedding, GloVe and BERT embedding. The experiments show that GloVe embedding can be helpful for faster execution with better performance on large-sized datasets.

The experiment reveals that the CNN model with GloVe embedding gives slightly better accuracy than the model with BERT embedding and traditional machine learning algorithms to classify an email as ham or spam. It is concluded that the word embedding models improve email classifiers accuracy.

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 paper is to review three international frameworks, including the International Standard Classification of Education (ISCED), in relation to one country’s higher professional and vocational education system.

The frameworks were examined in the context of English higher work-related education, and areas of mismatch identified. These were investigated to identify the extent to which they were due to weaknesses in the national system or to limiting assumptions contained in the frameworks.

Assumptions based on stages of education are problematic in the context of lifelong higher and professional education, while more open, lifelong-learning oriented assumptions can be too skeletal to aid comparisons between systems of initial vocational education and training. Particular problems are identified with assumptions contained in the ISCED that do not reflect the reality of professional education.

International frameworks need to take account of patterns of learning that take place outside of formal institutions and throughout life, but which lead to equivalent outcomes. Nevertheless, it is not adequate to substitute assumptions based only on the level of achievement.

The assumptions underpinning the ISCED in particular mean that equivalent achievements in different systems can be classified differently, leading to under-reporting of individual achievements, a lack of comparability in international statistics, and potential for policy distortion.

The paper builds on the work of Hippach-Schneider et al. by providing additional evidence, from a different national context, for issues relating to the ISCED in the context of higher professional and vocational education, and extends this analysis to the two major European frameworks.

Despite the burgeoning interest in corporate universities (CUs), their pragmatic application and ongoing evolution present challenges. This study aims to analyze the South Korean CU landscape from a balanced perspective to draw implications for the sustainable development of CUs.

The study uses a case study method to systematically explore CUs in South Korea by reviewing the South Korean government reports on CUs. The cases of CUs are analyzed based on the holistic model of CUs, which functions as an analytical framework.

By analyzing four groups of CUs, namely, in-house colleges, corporation colleges, technical colleges and in-house college-type lifelong educational establishments, implemented in South Korea, this study draws implications for the sustainable development of CUs, using the holistic CU model.

By analyzing cases of CUs from a new perspective, this study contributes to expand knowledge on CUs and suggests implications for organizations aiming to establish and sustain their own CUs tailored to their specific needs. Furthermore, this paper delves into the support necessary for the successful implementation and sustainable development of CUs, spanning organizational/team, national and individual levels.

The purpose of this study is to identify specific qualifications and competences required within the field of logistics and supply chain management from the perspective of lifelong learning and hierarchical level. It also reveals a hierarchy level-specific job profile for logistics and supply chain management jobs based on the European Framework for Key Competences and the German Federal Employment Agency.

A review of the literature on competences and skills in the field of logistics is combined with an analysis of 832 job postings by means of content analyses and thematic mapping.

The academic literature discusses more than 280 skills and competences related to the job profile of logistics and supply chain managers. The majority of these skills represent social skills. Analyses of job postings show significant differences in the required portfolio of skills and competences depending on the hierarchical level of the position. Contrary to the literature, job postings include more cognitive competences and meta-competences than social skills.

The first contribution of this research is that competences were examined from the perspective of lifelong learning, revealing how this is applied in the field of logistics. The second is that this conceptual framework can be utilised to compare skills and competences between different hierarchical levels. The third contribution refers to identifying a set of skills and competences different to what had been presented in the literature.

This account aims to introduce contrasting perspectives on teaching and learning methods, and to detail the growth of new forms and vocabularies of access to learning. As we move towards the new millennium, the development of national, yet diversified, credit frameworks and systems will provide an essential underpinning for the organisational culture that will be needed to sustain the wellbeing and growth of the educational system. These new systems are already being incorporated into the practice of ‘virtual’ education. Lifelong learning has widespread support across the social and political spectrum and its importance can hardly be over‐stated as we seek to maintain competitiveness in a changing world. Increasing knowledge and understanding to serve both the needs of the economy and of individuals to play a major role in democratic life has become an agenda of necessity as well as desire. An open society requires open systems of knowledge. A prognosis for the future is submitted where the significance of part‐time modular and open flexible learning is evaluated in terms of a curriculum rooted in useful knowledge and competences, acquired at different sites of learning, including the workplace. It is argued that modular structures, using the potential offered by credit accumulation and transfer to different institutions with different missions, can transcend and transform the learning opportunities for students in a mass system of higher education which is rapidly becoming part of a global market economy and society. Continuous lifelong learning involving its key features of open access, recognition of learning wherever it takes place and the growth of new learning networks and partnerships, is at the conceptual heart of the development of the virtual university.

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

The aim of the present study is to investigate why teachers participate in Information and Communication Technology (ICT) programmes, what their sociological approaches are, and where they focus their attention in order to achieve the objectives of their training in these new technologies.

The sample group of this research consists of 162 teachers who work in primary schools in Northern Greece. There were 70 (43.2 percent) men and 92 (56.8 percent) women. For the fulfilment of the general research an improvised questionnaire was designed concerning the training of teachers in ICT. In total, it comprises 80 questions.

The findings of the research study show that one of the main reasons for primary school teachers' participation in ICT programmes is their interest in exploiting ICT, in both their teaching and in their personal lives. They consider lifelong education as necessary to the practice of teaching and useful in settling educational inequalities, while at the same time they think that in‐service education should be in accordance with the requirements of the modern social‐cultural environment.

This is an interesting research on ICT training of teachers in Greece, developing a critical thinking approach towards ICT in‐service training. The article stresses that the in‐service training interact and are affected by the social and political orientations that prevail in social and educational policy.