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This paper investigates factors that influence Zambian higher education lecturer's attitude towards integrating ICTs in research and teaching. To determine these factors, we used online administered survey with a total of 163 respondents from public and private higher education institutions in Zambia. Quantitative data analysis based on descriptive and inferential statistics was used in this study. Inferential analysis was employed to investigate the relationships among variables using Pearson correlation analysis and completely randomized design. The results revealed that lack of adequate Internet bandwidth is among the major barrier or obstacle for assimilating ICTs in research and teaching for Zambian higher education lecturers. Additionally, training lecturers on the use of ICTs e.g. email, PowerPoint presentations, electronic boards and its advantages is the most important incentive to motivate lecturers to incorporate ICTs in research and teaching. Therefore, this study contributes to literature on factors that influence higher education lecturers' attitude towards integrating ICTs in research and teaching. This study also demonstrates a positive correlation between the important factors that influence lecturers in integrating ICTs in research and teaching.

Businesses are more concerned than ever about hate speech content as most brand communication and advertising move online. Different organisations may be incharge of their products and services but they do not have complete control over their content posted online via their website and social media channels, they have no control over what online users post or comment about their brand. As a result, it became imperative in our study to develop a model that will identify hate speechand, offensive language and detect cyber offence in online published content using machine learning. This study employed an experimental design to develop a detection model for determining which agile methodologies were preferred as a suitable development methodology. Deep learning and HateSonar was used to detect hate speech and offensive language in posted content. This study used data from Twitter and Facebook to detect hate speech. The text was classified as either hate speech, offensive language, or both. During the reconnaissance phase, the combined data (structured and unstructured) was obtained from kaggle.com. The combined data was stored in the database as raw data. This revealed that hate speech and offensive language exist everywhere in the world, and the trend of the vices is on the rise. Using machine learning, the researchers successfully developed a model for detecting offensive language and hate speech on online social media platforms. The labelling in the model makes it simple to categorise data in a meaningful and readable manner. The study establishes that in fore model to detect hate speech and offensive language on online social media platforms, the data set must be categorised and presented in statistical form after running the model; the count indicates the total number of data sets imported. The mean for each category, as well as the standard deviation and the minimum and maximum number of tweets in each category, are also displayed. The study established that preventing online platform abuse in Zambia requires a comprehensive approach that involves government law, responsible platform policies and practices, as well as individual responsibility and accountability. In accordance with this goal, the research was effective in developing the detection model. To guarantee that the model was completely functional, it was trained on the English dataset before being applied to the local language dataset. This was because of the fact that training deep learning models with local datasets can present a number of challenges, such as limited, biased data, data privacy, resource requirements, and model maintenance. However, the efficacy of these systems varies, and there have been concerns raised about the inherent biases and limitations of automatic moderation techniques. The study recommends that future studies consider other sources of information such as Facebook, WhatsApp, Instagram, and other social media platforms, as well as consider harvesting local data sets for training machines rather than relying on foreign data sets; the local data set can then be used to detect offences targeting Zambian citizens on local platforms. [ABSTRACT FROM AUTHOR]

Identification and verification have always been at the heart of financial services and payments, which is even more the case in the digital age. So, while banks have long been trusted to keep money safe, is there a new role for them as stewards of digital identity? Governments should, in consultation with the private sector, develop a national identity strategy based on a federated-style model in which public and private sector identity providers would compete to supply trusted digital identities to individuals and businesses. Back then, when the world seemed smaller, slower and more local, physical identity documents were adequate for face-to-face transactions. However, the Internet changed everything. It shrank distances, created new business models and generally sped everything up. From the innovation lifecycle to access to information, processes and the clock-speed on risk, the Internet has accelerated everything. The use of Internet in doing business has grown over the years in Africa and Zambia in particular. As such, the incidences of online identity theft have grown too. Identity theft is becoming a prevalent and increasing problem in Zambia. An identity thief only requires certain identity information to decimate a victim's life and credit. This research proposes to identify and extract various forms of identity attributes from various sources used in the physical and cyberspace to identity users accessing the financial services through extracting identity attributes from the various forms of identity credentials and application forms. Finally, design a digital identity model based on Shannon's Information theory and Euclidean metric based Euclidean Distance Geometry (EDG) to be used for quantifying, implementation and validating of extracted identity attributes from various forms of identity credentials and application forms, in an effective way. [ABSTRACT FROM AUTHOR]

Good road transport with appropriate road safety measures promotes efficient movement of goods and services within and between countries. Good road infrastructure and high safety levels are inevitable and a major driving force in sustainable development in Africa. This study was carried out to determine the numbers of road traffic accidents and fatalities for the period 2008 - 2013. This is important because sustainable development in Africa depends on trade and commerce which also heavily relies on efficient road transportation systems. Results obtained showed a total of 139, 002 Road Traffic Accidents; 9,363 people killed; 26, 533 seriously injured and 39, 636 had slight injuries. Such high incidents of accidents hamper trade and commerce and sustainable development of the country and the African continent at large. Future studies should determine black spots and suggest a monitoring strategy to minimize accidents and improve road safety records. [ABSTRACT FROM AUTHOR]

Electricity consumption in developing countries such as Zambia continues to grow as the economy grows. As a result, it is important to study how the rate of electricity consumption affects the economy of a country. For this study, the economic variables that were used are the Gross Domestic Product and the Consumer Price index. The results from this study are that there is a unidirectional relationship between electricity consumption and the consumer price index where the rate of electricity consumption Granger causes the consumer price index. The study also showed that there is no causal relationship between electricity and GDP and that there was no causal relationship between electricity consumption and the Consumer Price Index. [ABSTRACT FROM AUTHOR]

The growth of Information Technology has spawned large amount of databases and huge data in numerous areas. The research in databases and information technology has given rise to an approach to store and manipulate this data for further decision making. In this paper certain data mining techniques were adopted to analyze the data that shows relevance with desired attributes. Regression technique was adopted to help us find out the influence of Agriculture, Service and Manufacturing on the performance of gross domestic product (GDP). Trend and time series technique was applied to the data to help us find out what trend of GDP with respect to service, agriculture and manufacturing sector for the past decade has been. Finally Correlation was also used to help us analyze the relationship among the variables (service, agriculture and manufacturing sector). From the three techniques analyzed, service value added variable was the most prominent variable which showed the strong influence on GDP growth rate. [ABSTRACT FROM AUTHOR]

The article discusses importance of information retrieval system to meet information needs of users and mentions challenges of predicting normalized discounted cumulative gain (nDCG) methods. Topics discussed include data collection and judgements of test collections and dataset creation, creation of research by computing performance measures and use of linear regression model from creation of voting ensemble.