Big Data and Cognitive Computing

Detecting at-risk students provides advanced benefits for reducing student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag ($RF$). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general. Full article

A sports multi-bet is a bet on the results of a set of N games. One type of multi-bet offered by the Israeli government is WINNER 16, where participants guess the results of a set of 16 soccer games. The prizes in WINNER 16 are determined by the accumulated profit in previous rounds, and are split among all winning forms. When the reward increases beyond a certain threshold, a profitable strategy can be devised. Here, we present a machine-learning algorithm scheme to play WINNER 16. Our proposed algorithm is marginally profitable on average in a range of hyper-parameters, indicating inefficiencies in this game. To make a better prize-pricing mechanism we suggest a generalization of the single-bet approach. We studied the expected profit and risk of WINNER 16 after applying our suggestion. Our proposal can make the game more fair and more appealing without reducing the profitability. Full article

Internet social media is a key space in which the memorial resources of social movements, including the stories and knowledge of previous generations, are organised, disseminated, and reinterpreted. This is especially important for movements such as feminism, which places great emphasis on the transmission of an intangible cultural legacy between its different generations or waves, which are conformed through these cultural transmissions. In this sense, several authors have highlighted the importance of social media and hashtivism in shaping the fourth wave of feminism that has been taking place in recent years (e.g., #metoo). The aim of this article is to present to the scientific community a hybrid methodological proposal for the network and content analysis of audiences and their interactions on Twitter: we will do so by describing and evaluating the results of different research we have carried out in the field of feminist hashtivism. Structural analysis methods such as social network analysis have demonstrated their capacity to be applied to the analysis of social media interactions as a mixed methodology, that is, both quantitative and qualitative. This article shows the potential of a specific methodological process that combines inductive and inferential reasoning with hypothetico-deductive approaches. By applying the methodology developed in the case studies included in the article, it is shown that these two modes of reasoning work best when they are used together. Full article

The notion of Industry 4.0 encompasses the adoption of new information technologies that enable an enormous amount of information to be digitally collected, analyzed, and exploited in organizations to make better decisions. Therefore, finding how organizations can adopt big data (BD) components to improve their performance becomes a relevant research area. This issue is becoming more pertinent for small and medium enterprises (SMEs), especially in developing countries that encounter limited resources and infrastructures. Due to the lack of empirical studies related to big data adoption (BDA) and BD’s business value, especially in SMEs, this study investigates the impact of BDA on SMEs’ performance by obtaining the required data from experts. The quantitative investigation followed a mixed approach, including survey data from 224 managers from Iranian SMEs, and a structural equation modeling (SEM) methodology for the data analysis. Results showed that 12 factors affected the BDA in SMEs. BDA can affect both operational performance and economic performance. There has been no support for the influence of BDA and economic performance on social performance. Finally, the study implications and findings are discussed alongside future research suggestions, as well as some limitations and unanswered questions. Full article

Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low latency. This could be because the benchmarking techniques for SNNs are based on the methods used for evaluating deep neural networks, which do not provide a clear evaluation of the capabilities of SNNs. Particularly, the benchmarking of SNN approaches with regards to energy efficiency and latency requires realization in suitable hardware, which imposes additional temporal and resource constraints upon ongoing projects. This review aims to provide an overview of the current real-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future. Full article

The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model is focused on highlighting the most relevant attributes of a product or service along with customers’ estimation of how the presence of these attributes can be used to predict satisfaction about specific services or products. This research aims to develop a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to solve the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from UAE University. The experiment suggests that XGBoost Regression and Decision Tree Regression produce best results for this kind of problem. Based on the integration between the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that ANOVA features selection model’s integration with the Kano model gives higher Pearson correlation coefficients and higher R2 values. Full article

Big data frameworks play a vital role in storing, processing, and analysing large datasets. Apache Spark has been established as one of the most popular big data engines for its efficiency and reliability. However, one of the significant problems of the Spark system is performance prediction. Spark has more than 150 configurable parameters, and configuration of so many parameters is challenging task when determining the suitable parameters for the system. In this paper, we proposed two distinct parallelisation models for performance prediction. Our insight is that each node in a Hadoop cluster can communicate with identical nodes, and a certain function of the non-parallelisable runtime can be estimated accordingly. Both models use simple equations that allows us to predict the runtime when the size of the job and the number of executables are known. The proposed models were evaluated based on five HiBench workloads, Kmeans, PageRank, Graph (NWeight), SVM, and WordCount. The workload’s empirical data were fitted with one of the two models meeting the accuracy requirements. Finally, the experimental findings show that the model can be a handy and helpful tool for scheduling and planning system deployment. Full article

Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies. Full article

Generative adversarial networks (GANs) provide powerful architectures for deep generative learning. GANs have enabled us to achieve an unprecedented degree of realism in the creation of synthetic images of human faces, landscapes, and buildings, among others. Not only image generation, but also image manipulation is possible with GANs. Generative deep learning models are inherently limited in their creative abilities because of a focus on learning for perfection. We investigated the potential of GAN’s latent spaces to encode human expressions, highlighting creative interest for suboptimal solutions rather than perfect reproductions, in pursuit of the artistic concept. We have trained Deep Convolutional GAN (DCGAN) and StyleGAN using a collection of portraits of detained persons, portraits of dead people who died of violent causes, and people whose portraits were taken during an orgasm. We present results which diverge from standard usage of GANs with the specific intention of producing portraits that may assist us in the representation and recognition of otherness in contemporary identity construction. Full article

Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually projected using numerical climate methods. In this paper, we explore the potential of using machine learning as part of a statistical downscaling framework to project future CFC. We are not aware of any other research that has explored this. We evaluated the potential of two different methods, a convolutional long short-term memory model (ConvLSTM) and a multiple regression equation, to predict CFC from other environmental variables. The predictions were associated with much uncertainty indicating that there might not be much information in the environmental variables used in the study to predict CFC. Overall the regression equation performed the best, but the ConvLSTM was the better performing model along some coastal and mountain areas. All aspects of the research analyses are explained including data preparation, model development, ML training, performance evaluation and visualization. Full article

A business process re-engineering value in improving the business process is undoubted. Nevertheless, it is incredibly complex, time-consuming and costly. This study aims to review available literature in the use of machine learning for business process re-engineering. The review investigates available literature in business process re-engineering frameworks, methodologies, tools, techniques, and machine-learning applications in automating business process re-engineering. The study covers 200+ research papers published between 2015 and 2020 in reputable scientific publication platforms: Scopus, Emerald, Science Direct, IEEE, and British Library. The results indicate that business process re-engineering is a well-established field with scientifically solid frameworks, methodologies, tools, and techniques, which support decision making by generating and analysing relevant data. The study indicates a wealth of data generated, analysed and utilised throughout business process re-engineering projects, thus making it a potential greenfield for innovative machine-learning applications aiming to reduce implementation costs and manage complexity by exploiting the data’s hiding patterns. This suggests that there were attempts towards applying machine learning in business process management and improvement in general. They address process discovery, process behaviour prediction, process improvement, and process optimisation. The review suggests that expanding the applications to business process re-engineering is promising. The study proposed a machine-learning model for automating business process re-engineering, inspired by the Lean Six Sigma principles of eliminating waste and variance in the business process. Full article

Text classification is an important component in many applications. Text classification has attracted the attention of researchers to continue to develop innovations and build new classification models that are sourced from clinical trial texts. In building classification models, many methods are used, including supervised learning. The purpose of this study is to improve the computational performance of one of the supervised learning methods, namely KNN, in building a clinical trial document text classification model by combining KNN and the fine-grained algorithm. This research contributed to increasing the computational performance of KNN from 388,274 s to 260,641 s in clinical trial texts on a clinical trial text dataset with a total of 1,000,000 data. Full article

An information retrieval (IR) system is the core of many applications, including digital library management systems (DLMS). The IR-based DLMS depends on either the title with keywords or content as symbolic strings. In contrast, it ignores the meaning of the content or what it indicates. Many researchers tried to improve IR systems either using the named entity recognition (NER) technique or the words’ meaning (word sense) and implemented the improvements with a specific language. However, they did not test the IR system using NER and word sense disambiguation together to study the behavior of this system in the presence of these techniques. This paper aims to improve the information retrieval system used by the DLMS by adding the NER and word sense disambiguation (WSD) together for the English and Arabic languages. For NER, a voting technique was used among three completely different classifiers: rules-based, conditional random field (CRF), and bidirectional LSTM-CNN. For WSD, an examples-based method was used to implement it for the first time with the English language. For the IR system, a vector space model (VSM) was used to test the information retrieval system, and it was tested on samples from the library of the University of Kufa for the Arabic and English languages. The overall system results show that the precision, recall, and F-measures were increased from 70.9%, 74.2%, and 72.5% to 89.7%, 91.5%, and 90.6% for the English language and from 66.3%, 69.7%, and 68.0% to 89.3%, 87.1%, and 88.2% for the Arabic language. Full article

In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study. Full article

The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits. Full article

With the rapid development of 5G communications, enhanced mobile broadband, massive machine type communications and ultra-reliable low latency communications are widely supported. However, a 5G communication system is still based on Shannon’s information theory, while the meaning and value of information itself are not taken into account in the process of transmission. Therefore, it is difficult to meet the requirements of intelligence, customization, and value transmission of 6G networks. In order to solve the above challenges, we propose a 6G mailbox theory, namely a cognitive information carrier to enable distributed algorithm embedding for intelligence networking. Based on Mailbox, a 6G network will form an intelligent agent with self-organization, self-learning, self-adaptation, and continuous evolution capabilities. With the intelligent agent, redundant transmission of data can be reduced while the value transmission of information can be improved. Then, the features of mailbox principle are introduced, including polarity, traceability, dynamics, convergence, figurability, and dependence. Furthermore, key technologies with which value transmission of information can be realized are introduced, including knowledge graph, distributed learning, and blockchain. Finally, we establish a cognitive communication system assisted by deep learning. The experimental results show that, compared with a traditional communication system, our communication system performs less data transmission quantity and error. Full article

A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that random forest (RF) outperformed other ML methods, and especially linear regression models representing current practice in the literature. Explainable artificial intelligence (XAI) was further used to interpret the RF method and analyze the impact of different LC changes on temperature. The results mainly agree with the climate science literature, but also reveal new and interesting findings, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation, and XAI. Full article

Understanding the ways different people perceive and apply acquired knowledge, especially when driving, is an important area of study. This study introduced a novel virtual reality (VR)-based driving system to determine the effects of neuro-cognitive load on learning transfer. In the experiment, easy and difficult routes were introduced to the participants, and the VR system is capable of recording eye-gaze, pupil dilation, heart rate, as well as driving performance data. So, the main purpose here is to apply multimodal data fusion, several machine learning algorithms, and strategic analytic methods to measure neurocognitive load for user classification. A total of ninety-eight (98) university students participated in the experiment, in which forty-nine (49) were male participants and forty-nine (49) were female participants. The results showed that data fusion methods achieved higher accuracy compared to other classification methods. These findings highlight the importance of physiological monitoring to measure mental workload during the process of learning transfer. Full article

Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images. Full article