Advances in Data Science and Adaptive Analysis

The uneven distribution of classes in any dataset poses a tendency of biasness toward the majority class when analyzed using any standard classifier. The instances of the significant class being deficient in numbers are generally ignored and their correct classification which is of paramount interest is often overlooked in calculating overall accuracy. Therefore, the conventional machine learning approaches are rigorously refined to address this class imbalance problem. This challenge of imbalanced classes is more prevalent in big data scenario due to its high volume. This study deals with acknowledging a sampling solution based on cluster computing in handling class imbalance problems in the case of big data. The newly proposed approach hybrid sampling algorithm (HSA) is assessed using three popular classification algorithms namely, support vector machine, decision tree and k-nearest neighbor based on balanced accuracy and elapsed time. The results obtained from the experiment are considered promising with an efficiency gain of 42% in comparison to the traditional sampling solution synthetic minority oversampling technique (SMOTE). This work proves the effectiveness of the distribution and clustering principle in imbalanced big data scenarios.

The novel coronavirus COVID-19 (SARS-CoV-2) with the first clinical case emerged in the city of Wuhan in China in December 2019. Then it has spread to the entire world in very short time and turned into a global problem, namely, it has rapidly become a pandemic. Within this context, many studies have attempted to predict the consequences of the pandemic in certain countries. Nevertheless, these studies have focused on some parameters such as reproductive number, recovery rate and mortality rate when performing forecasting.

This study aims to forecast COVID-19 data in Turkey with use of a new technique which is a combination of classical exponential smoothing and moving average. There is no need for reproductive number, recovery rate and mortality rate computation in this proposed technique.

Simulations are carried out for the number of daily cases, active cases (those are cases with no symptoms), daily tests, recovering patients, patients in the intensive care unit, daily intubated patients, and deaths forecasting and results are tested on Mean Absolute Percentage Error (MAPE) criterion.

It is shown that this technique captured the system dynamic behavior in Turkey and made exact predictions with the use of real time dataset.

One of the most critical national infrastructures (CNIs) in Liberia on which critical e-government services are dependent is GovNet. GovNet is the acronym for Government of Liberia’s (GoL’s) Network, and it is the conduit for connectivity and gateway to the internet for all of the Government’s over 107 Ministries, Agencies, and Commissions (MACs), as well as its e-government programs. Most of the MACs connected to GovNet run siloed ICT environments with little or no cybersecurity mechanisms in place. This paper is an investigation conducted at six MACs that are members of GovNet. The investigation identified several cybersecurity deficiencies at those MACs, due to the absence of vital dimensions or pre-requirements of cybersecurity readiness including the infrastructure (digital infrastructure), capacity (education and skills) and, governance (legal and regulatory instruments). The investigation examines previous and extant literature, conducted interviews with stakeholders of GovNet, and leverages the vast experiences of the author, who is the immediate past Managing Director of LIBTELCO. Recommendations are made for the necessary actions to be taken to remedy those deficiencies in GovNet, and the study’s contribution to the body of knowledge is indicated in the Conclusion.

In this continuous changing world where the only constant is change, automation has become the part of intellectual contempory business world. Organizations are using software, hardwares and machines as helping tools that support them to achieve and complete tasks and processes automatically. This research paper reviews literature by following methodology given by Jesson et al. (2011). Doing Your Literature Review: Traditional and Systematic Techniques. Sage, Los Angeles]. Selected research papersthat are related to data, knowledge, types of knowledge, wisdom, intelligence, knowledge pyramid, knowldge management process, and knowledge automation are reviewed. The review of literature finds the linkage between knowledge automation and knowledge management process which ends up developing automated knowledge management model (AKMM).