| 19981. |
- Imeri, Shpend, et al.
(författare)
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Evaluation and selection process of supplier through analytical framework : An empirical evidence of evaluation tool
- 2015
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Ingår i: Management and Production Engineering Review. - : Walter de Gruyter GmbH. - 2080-8208 .- 2082-1344. ; 6:3, s. 10-20
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Tidskriftsartikel (refereegranskat)abstract
- The supplier selection process is very important to companies as selecting the right suppliers that fit companies strategy needs brings drastic savings. Therefore, this paper seeks to address the key area of supplies evaluation from the supplier review perspective. The purpose was to identify the most important criteria for suppliers’ evaluation and develop evaluation tool based on surveyed criteria. The research was conducted through structured questionnaire and the sample focused on small to medium sized companies (SMEs) in Greece. In total eighty companies participated in the survey answering the full questionnaire which consisted of questions whether these companies utilize some suppliers’ evaluation criteria and what criteria if any is applied. The main statistical instrument used in the study is Principal Component Analysis (PCA). Thus, the research has shown that the main criteria are: the attitude of the vendor towards the customer, supplier delivery time, product quality and price. Conclusions are made on the suitability and usefulness of suppliers’ evaluation criteria and in way they are applied in enterprises.
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| 19982. |
- Immerfall, Stefan, et al.
(författare)
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Introduction
- 2010
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Ingår i: Handbook of European societies. - New York ; : Springer. - 9780387881980 - 9780387881997 ; , s. 1-5-
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- It is always tempting for contemporaries to consider their times as exceptional. Still, today’s Europe does look very different from the one only one generation ago. This is not because social change was stalled or absent in the 1960s and 1970s. On the contrary! It was, quite rightfully, regarded as the time of turbulence which saw, just to mention a few social incidences, the expansion of education, the turn downwards of marriage and fertility rates, the shift towards the service sector, the finalization of the European welfare states, the return of women into the labour market, increasing affluence and the thawing of social and political cleavages. These changes took different forms in different countries, to be sure, and Western and Eastern Europe did display comparable but not converging patterns (Therborn 2000). Nevertheless, all of Europe was affected by similar and rapid changes.
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| 19983. |
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| 19984. |
-
Implementing Sustainable Development Goals in Europe : The Role of Political Entrepreneurship
- 2020
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- This unique book expertly analyses European political entrepreneurship in relation to the European Union’s approach towards the Agenda 2030 Sustainable Development strategy. It explores the role of European political entrepreneurs in shaping, influencing and realising the United Nation’s Sustainable Development Goals. Chapters examine EU actors in the context of numerous development goals to assess how political entrepreneurship challenges traditional EU institutions and promotes visionary activity.
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| 19985. |
- Imran, Ali Shariq, et al.
(författare)
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An Analysis of Social Collaboration and Networking Tools in eLearning
- 2016
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Ingår i: Learning and Collaboration Technologies. LCT 2016. - Cham : Springer. - 9783319394824 ; , s. 332-343
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Konferensbidrag (refereegranskat)abstract
- Many online learning websites and learning management systems (LMS) provide social collaboration and networking tools to aid learning and to interact with peers for knowledge sharing. The benefit of collaborating with each other is certainly undeniable, such tools, however, can be a distraction from the actual tasks for learners. The paper presents a study on social media tools supported by various eLearning systems to understand the impact on students learning activities. A survey questionnaire is designed for this purpose. The data is collected from students who have had experience using different massive open online course (MOOC) eLearning platforms and LMS from various universities. The results of the survey indicate that more than 95Â % of the participants use at least one of the social tools in their daily life activities, and almost 84Â % of them have used these tools in connection with the eLearning systems. It is also interesting to note that 92Â % of the participants intend to use social tools for study purposes. The results indicate that there is a need to integrate more of these social media tools into eLearning systems.
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| 19986. |
- Imran, Ali Shariq, et al.
(författare)
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Classifying European Court of Human Rights Cases Using Transformer-Based Techniques
- 2023
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 55664-55676
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Tidskriftsartikel (refereegranskat)abstract
- In the field of text classification, researchers have repeatedly shown the value of transformer-based models such as Bidirectional Encoder Representation from Transformers (BERT) and its variants. Nonetheless, these models are expensive in terms of memory and computational power but have not been utilized to classify long documents of several domains. In addition, transformer models are also often pre-trained on generalized languages, making them less effective in language-specific domains, such as legal documents. In the natural language processing (NLP) domain, there is a growing interest in creating newer models that can handle more complex input sequences and domain-specific languages. Keeping the power of NLP in mind, this study proposes a legal documentation classifier that classifies the legal document by using the sliding window approach to increase the maximum sequence length of the model. We used the ECHR (European Court of Human Rights) publicly available dataset which to a large extent is imbalanced. Therefore, to balance the dataset we have scrapped the case articles from the web and extracted the data. Then, we employed conventional machine learning techniques such as SVM, DT, NB, AdaBoost, and transformer-based neural networks models including BERT, Legal-BERT, RoBERTa, BigBird, ELECTRA, and XLNet for the classification task. The experimental findings show that RoBERTa outperformed all the mentioned BERT versions by obtaining precision, recall, and F1-score of 89.1%, 86.2%, and 86.7%, respectively. While from conventional machine learning techniques, AdaBoost outclasses SVM, DT, and NB by achieving scores of 81.9%, 81.5%, and 81.7% for precision, recall, and F1-score, respectively.
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| 19987. |
- Imran, Ali Shariq, et al.
(författare)
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Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets
- 2020
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 181074-181090
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Tidskriftsartikel (refereegranskat)abstract
- How different cultures react and respond given a crisis is predominant in a society’s norms and political will to combat the situation. Often, the decisions made are necessitated by events, social pressure, or the need of the hour, which may not represent the nation’s will. While some are pleased with it, others might show resentment. Coronavirus (COVID-19) brought a mix of similar emotions from the nations towards the decisions taken by their respective governments. Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, and hashtags past couple of months. Despite geographically close, many neighboring countries reacted differently to one another. For instance, Denmark and Sweden, which share many similarities, stood poles apart on the decision taken by their respective governments. Yet, their nation’s support was mostly unanimous, unlike the South Asian neighboring countries where people showed a lot of anxiety and resentment. The purpose of this study is to analyze reaction of citizens from different cultures to the novel Coronavirus and people’s sentiment about subsequent actions taken by different countries. Deep long short-term memory (LSTM) models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset. The use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.
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| 19988. |
- Imran, Ali Shariq, et al.
(författare)
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Pedagogical document classification and organization using domain ontology
- 2016
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Ingår i: Learning and Collaboration Technologies. - Cham : Springer. - 9783319394824 - 9783319394831 ; , s. 499-509
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Konferensbidrag (refereegranskat)abstract
- One of the challenges faced by today’s web is the abundance of unstructured and unorganized information available on the Internet in form of educational documents, lecture notes, presentation slides, and multimedia recordings. Accessing and retrieving the massive amount of such resources are not an easy task, especially educational resources of pedagogical nature. Much of the pedagogical content available on Internet comes from blogs, wikis, posts with little or no metadata, that suffer from the same dilemma. The content is out there but way out of the reach of the intended audience. For content to be readily available, it has to be properly organized into different categories and structured into an appropriate format using metadata. This paper addresses this issue by proposing an automated approach using ontology-based document classification. The paper presents a case study and describes how our proposed ontology model can be used to classify educational documents into predefined categories.
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| 19989. |
- Imran, Ali Shariq, et al.
(författare)
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Predicting Student Dropout in a MOOC : An Evaluation of a Deep Neural Network Model
- 2019
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Ingår i: Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence. - New York, NY, USA : ACM Publications. - 9781450361064 ; , s. 190-195
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Konferensbidrag (refereegranskat)abstract
- Massive Open Online Courses (MOOCs) have transformed the way educational institutions deliver high-quality educational material to the onsite and distance learners across the globe. As a result, a new paradigm shifts as to how learners acquire and benefit from the wealth of knowledge provided by a MOOC at their doorstep nowadays in contrast to the brick and mortar settings is visible. Learners are therefore showing a profound interest in the MOOCs offered by top universities and industry giants. They have also attracted a vast number of students from far-flung areas of the world. The massive number of registered students in MOOCs, however, pose one major challenge, i.e., 'the dropouts'. Course planners and content providers are struggling to retain the registered students, which give rise to a new research agenda focusing on predicting and explaining student dropout and low completion rates in a MOOC. Machine learning techniques utilizing deep learning approaches can efficiently predict the potential dropouts and can raise an alert well before time. In this paper, we have focused our study on the application of feed-forward deep neural network architectures to address this problem. Our model achieves not only high accuracy, but also low false negative rate while predicting dropouts on the MOOC data. Moreover, we also provide an in-depth comparison of the proposed architectures concerning precision, recall, and F1 measure.
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| 19990. |
- Imran, Ali Shariq, et al.
(författare)
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Text-Independent Speaker ID Employing 2D-CNN for Automatic Video Lecture Categorization in a MOOC Setting
- 2019
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Ingår i: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). - : IEEE Press. - 9781728137988 - 9781728137995 ; , s. 273-277
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Konferensbidrag (refereegranskat)abstract
- A new form of distance and blended education has hit the market in recent years with the advent of massive open online courses (MOOCs) which have brought many opportunities to the educational sector. Consequently, the availability of learning content to vast demographics of people and across locations has opened up a plethora of possibilities for everyone to gain new knowledge through MOOCs. This poses an immense issue to the content providers as the amount of manual effort required to structure properly and to organize the content automatically for millions of video lectures daily become incredibly challenging. This paper, therefore, addresses this issue as a small part of our proposed personalized content management system by exploiting the voice pattern of the lecturer for identification and for classifying video lectures to the right speaker category. The use of Mel frequency Cepstral coefficients (MFCC) as 2D input features maps to 2D-CNN has shown promising results in contrast to machine learning and deep learning classifiers - making text-independent speaker identification plausible in MOOC setting for automatic video lecture categorization. It will not only help categorize educational videos efficiently for easy search and retrieval but will also promote effective utilization of micro-lectures and multimedia video learning objects (MLO).
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