Modeling of mobile channels using TIMS in IT education

PurposeThe purpose of this paper is to directly link information technology (IT) education with real-world phenomena.Design/methodology/approachThe selected objectives are achieved by modeling line of sight (LOS) and nonline of sight (NLOS) mobile channels using corresponding distributions. Within the described experiments, students verify whether modeled generators generate random variables accordingly to the selected distribution. The results of observations are directly compared with theoretical expectations. The methodology was evaluated by students via questionnaires.FindingsThe results show that the proposed methodology can help graduate or undergraduate students better comprehend lectured material from mobile communications or mathematical statistics.Originality/valueThe hands on experience using the EMONA system make the approach original.

Clustering as feature selection method in spam classification: uncovering sick-leave sellers

PurposeThis paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter.Design/methodology/approachFour machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied.FindingsRadom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior.Research limitations/implicationsThe method applied is novel, more testing is needed in other datasets before generalizing its results.Practical implicationsThe model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online.Originality/valueThe research is proposing a new way textual clustering can be used in feature selection.

Predicting student performance in a blended learning environment using learning management system interaction data

PurposeStudent attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.Design/methodology/approachThis study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.FindingsIdentifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.Originality/valueThe best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

Using transfer learning for diabetic retinopathy stage classification

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

Longitudinal estimation of stress-related states through bio-sensor data

PurposeThe authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.Design/methodology/approachThe authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.FindingsThe authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.Research limitations/implicationsProposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.Practical implicationsLongitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.Social implicationsTimely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.Originality/valueThe study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

Why and how informatics and applied computing can still create structural changes and competitive advantage

PurposeIn the new digital age, enterprises are facing an increasing global competition. In this paper, we first examine how Information Technology (IT) can play an important role in giving significant competitive advantage in the modern enterprises. The business value of IT is examined, as well as the limitations and the trade-offs that its applicability faces. Next, we present the basic principles for a successful IT strategy, considering the development of a long-term IT renovation plan, the strategic alignment of IT with the business strategy, and the adoption of an integrated, distributed, and interoperable IT platform. Finally, we examine how a highly functional and efficient IT organization can be developed.Design/methodology/approachOur methodological approach was based to the answers of the following questions: 1. Does IT still matter? 2. What is the business value created by IT along with the corresponding limitations and trade-offs? 3. How could a successful IT Strategy be build up? 4. How could an effective? T planning aligned with the business strategy be build up? 5. How could a homogenized and distributed corporate IT platform be developed? and finally, 6. How could a high-performance IT-enabled enterprise be build up?FindingsThe enterprises in order to succeed in the new digital area need to: 1. synchronize their IT strategy with their business strategy, 2. formulate a long-term IT strategy, 3. adopt IT systems and solutions that are implemented with elasticity, interoperability, distribution, and service-orientation. 4. keep a strategic direction towards the creation of an exceptional organization based on IT.Originality/valueThis paper is original with respect to the integrated approach the overall problem is examined. There is a prototype combined investigation of all perspectives for an effective enforcement of IT in a way that causes acceleration in competitive advantage when conducting business.

Design of a small-scale and failure-resistant IaaS cloud using OpenStack

PurposeMajor public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.Design/methodology/approachTo demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.FindingsThrough the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.Originality/valueThe authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

Audiogmenter: a MATLAB toolbox for audio data augmentation

PurposeCreate and share a MATLAB library that performs data augmentation algorithms for audio data. This study aims to help machine learning researchers to improve their models using the algorithms proposed by the authors.Design/methodology/approachThe authors structured our library into methods to augment raw audio data and spectrograms. In the paper, the authors describe the structure of the library and give a brief explanation of how every function works. The authors then perform experiments to show that the library is effective.FindingsThe authors prove that the library is efficient using a competitive dataset. The authors try multiple data augmentation approaches proposed by them and show that they improve the performance.Originality/valueA MATLAB library specifically designed for data augmentation was not available before. The authors are the first to provide an efficient and parallel implementation of a large number of algorithms.

Supervised learning and resampling techniques on DISC personality classification using Twitter information in Bahasa Indonesia

PurposeGathering knowledge regarding personality traits has long been the interest of academics and researchers in the fields of psychology and in computer science. Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies).Design/methodology/approachIn this, cleaning the dataset and extracting relevant potential features “as assessed by psychological experts” are essential, as Indonesians tend to mix formal words, non-formal words, slang and abbreviations when writing social media posts. For this article, raw data were derived from a predefined dominance, influence, stability and conscientious (DISC) quiz website, returning 316,967 tweets from 1,244 Twitter accounts “filtered to include only personal and Indonesian-language accounts”. Using a combination of NLP techniques and machine learning, the authors aim to develop a better approach and more robust model, especially for the Indonesian language.FindingsThe authors find that employing a SMOTETomek re-sampling technique and hyperparameter tuning boosts the model’s performance on formalized datasets by 57% (as measured through the F1-score).Originality/valueThe process of cleaning dataset and extracting relevant potential features assessed by psychological experts from it are essential because Indonesian people tend to mix formal words, non-formal words, slang words and abbreviations when writing tweets. Organic data derived from a predefined DISC quiz website resulting 1244 records of Twitter accounts and 316.967 tweets.

An overlapping sliding window and combined features based emotion recognition system for EEG signals

PurposeThe purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.Design/methodology/approachClassical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.FindingsThe two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.Originality/valueThe present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.