Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

There has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption

Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

Service level agreement (SLA) management is one of the key issues in cloud computing. The primary goal of a service provider is to minimize the risk of service violations, as these results in penalties in terms of both money and a decrease in trustworthiness. To avoid SLA violations, the service provider needs to predict the likelihood of violation for each SLO and its measurable characteristics (QoS parameters) and take immediate action to avoid violations occurring. There are several approaches discussed in the literature to predict service violation; however, none of these explores how a change in control parameters and the freshness of data impact prediction accuracy and result in the effective management of an SLA of the cloud service provider. The contribution of this paper is two-fold. First, we analyzed the accuracy of six widely used prediction algorithms - simple exponential smoothing, simple moving average, weighted moving average, Holt-Winter double exponential smoothing, extrapolation, and the autoregressive integrated moving average - by varying their individual control parameters. Each of the approaches is compared to 10 different datasets at different time intervals between 5 min and 4 weeks. Second, we analyzed the prediction accuracy of the simple exponential smoothing method by considering the freshness of a data; i.e., how the accuracy varies in the initial time period of prediction compared to later ones. To achieve this, we divided the cloud QoS dataset into sets of input values that range from 100 to 500 intervals in sets of 1-100, 1-200, 1-300, 1-400, and 1-500. From the analysis, we observed that different prediction methods behave differently based on the control parameter and the nature of the dataset. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results to manage SLA intelligently and avoid violation penalties

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources

Forecasting with Machine Learning Techniques

The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in huge quantities of real-time data [...

Guest editorial : Machine learning applied to quality and security in software systems [Editorial]

No abstract available

E-learning: Closing the Digital Gap between Developed and Developing Countries

Abstract: As there are many gaps between developed and developing countries, Digital Gap is one of them. Research has raised the idea and question of e-learning closing this gap. Research has identified, compared, evaluated and reviewed the issue from both the angels of literature and quantitative research. The focus has been to assess the e-learning potential to provide quality education though electronic means and review to what extent this is going to be feasible. ICT infrastructure, channels of communication, learning styles, the role of teacher and classroom and blended learning has been discussed

Assessing cloud QoS predictions using OWA in neural network methods

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy

Medical photography using mobile devices

The limitations of image quality in mobile device technology raise the question whether smart phones are suitable for medical photography. The answer is it depends. There are many factors to consider, in particular the purpose of capture and whether a standardised or non-standardised approach is required. A correlation study comparing on-site wound evaluation versus remotely viewed digital images in plastic and reconstructive emergency surgery concluded that efficiency in clinical decision making is less based upon the quality of imaging but on the timing and method of delivery.4 On the other hand, a case-control study evaluating the importance of standardisation in preoperative and postoperative photographs concluded that poor photographic technique can result in potentially significant error and misrepresent surgical outcomes.5 Low quality images may therefore still result in high accuracy and concordance rates where standardisation is not a prerequisite for assessment, but may be misleading in instances where standardisation is paramount, such as demonstrating preoperative and post-surgical facial aesthetic surgery

Hydrolysis, Microstructural Profiling and Utilization of Cyamopsis tetragonoloba in Yoghurt

The present study investigates the hydrolysis, microstructural profiling and utilization of guar gum (Cyamopsis tetragonoloba) as a prebiotic in a yoghurt. Guar galactomannans (GG) was purified and partially depolymerized using an acid, alkali and enzyme to improve its characteristics and increase its utilization. The prebiotic potential of hydrolyzed guar gum was determined using Basel and supplemented media. Crude guar galactomannans (CGG), purified guar galactomannans (PGG), base hydrolyzed guar galactomannans (BHGG), acid hydrolyzed guar galactomannans (AHGG) and enzymatic hydrolyzed guar galactomannans (EHGG) were analyzed using scanning electron microscope (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Yoghurt was prepared with a starter culture and incorporating guar gum, its hydrolyzed forms (0.1, 0.5 and 1%) and Bifidobacterium bifidum. The results showed that PHGG significantly improved the viability of B. bifidum. SEM revealed a significant change in the surface morphology of guar gum after acidic and enzymatic hydrolysis. Enzymatic hydrolysis developed a well-defined framework within guar gum molecules. The XRD pattern of CGG, PGG and AHGG presented an amorphous structure and showed low overall crystallinity while EHGG and BHGG resulted in slightly increased crystallinity regions. FTIR spectral analysis suggested that, after hydrolysis, there was no major transformation of functional groups. The addition of the probiotic and prebiotic significantly improved the physiochemical properties of the developed yoghurt. The firmness, cohesiveness, adhesiveness and syneresis were increased while consistency and viscosity were decreased during storage. In sum, a partial hydrolysis of guar gum could be achieved using inexpensive methods with commercial significance