The world has been devastated by locust outbreaks since time immemorial. However, due to climate change, there has been an increase in instances of the spread of locusts around the world. This trend keeps on causing havoc to crops, pastures and livelihoods. This study focused on African migratory locust (Locusta migratoria migratoriodes) and Red locust (Nomadacris septemfasciata) species that are prevalent in the study area. Managing locust invasions in Sikaunzwe Agricultural Camp in Kazungula District of Zambia is faced with pest identification challenges. Elocust3, an early warning system, provided by Food and Agriculture Organisation (FAO) doesn’t have an automatic locust identification facility. Artificial Intelligence has not been exploited to full capacity in locust management due to lack of a consistent monitoring system in southern and central Africa regions. An automatic identification of both Locusta migratoria and Nomadacris septemfasciata using Convolution Neural Network (CNN) Single-Stage object detection MobileNet version 2 quantised model was used in the study. A custom dataset was used to train, test and validate the model using images collected from the study area. The dataset is composed of 1700 images captured using a Nikon D5300 camera in various light and background conditions and each image was annotated using Labelimg software. The results from the study shows that the model is able to identify Locusta migratoria and Nomadacris septemfasciata with an average precision of 91% and 85% respectively. The results are considered to be satisfactory despite the low quantised resources used in the study.KeywordsArtificial intelligenceDeep learningeLocust3Migratory LocustRed LocustLocusta migratoriaNomadacris septemfasciata

PREFACE The 2020 ZAPUC International Conference is an annual conference organised by the Zambia Association of Public Universities and Colleges (ZAPUC) who are the collaborations and partnerships in tertiary education. The conference received a total of 41 research papers and abstracts. Each submission was exposed to blind peer reviewing and was reviewed by a minimum of two experts. The EDAS Conference Management Systems was used to manage the peer reviewing process. Experts were drawn from the Southern and East African Region Universities. A total of 38 abstracts were accepted presented and published in the proceedings. This includes a total number of 26 full papers that were accepted, presented and published in the proceedings.

Internet has brought a lot of security challenges on the interaction, activities, and transactions that occur online. These include pervasion of privacy of individuals, organizations, and other online actors. Relationships in real life get affected by online mischievous actors with intent to misrepresent or ruin the characters of innocent people, leading to damaged relationships. Proliferation of cybercrime has threatened the value and benefits of internet. Identity theft by fraudsters with intent to steal assets in real space or online has escalated. This study has developed a metrics model based on distance metrics in order to quantify the credential identity attributes used in online services and activities. This is to help address the digital identity challenges, bring confidence to online activities and ownership of assets. The application forms and identity tokens used in the various sectors to identify online users were used as the sources of the identity attributes in this paper. The corpus toolkits were used to mine and extract the identity attributes from the various forms of identity tokens. Term weighting schemes were used to compute the term weight of the identity attributes. Other methods used included Shannon Entropy and the Term Frequency-Inverse Document Frequency scheme (TF*IDF). Standardization of data using data normalization method has been applied. The results show that using the Cosine Similarity Measure, we can identify the identity attributes in any given identity token used to identify individuals and entities. This will help to attach the legitimate ownership to the digital identity attributes. The developed model can be used to uniquely identify an online identity claimant and help address the security challenge in identity management systems. The proposed model can also identify the key identity attributes that could be used to identify an entity in real or cyber spaces.

Crime mapping is a strategy used to detect and prevent crime in the police service. The technique involves the use of geographical maps to help crime analysts identify and profile crimes committed in different residential areas, as well as crafting best methods of responding. The development of geographic information system (GIS) technologies and spatial analysis applications coupled with cloud computing have significantly improved the ability of crime analysts to perform this crime mapping function. The aim of this research is to automate the processes involved in crime mapping using spatial data. A baseline study was conducted to identify the challenges in the current crime mapping system used by the Zambia Police Service. The results show that 85.2% of the stations conduct crime mapping using physical geographical maps and pins placed on the map while 14.8% indicated that they don’t use any form of crime mapping technique. In addition, the study revealed that all stations that participated in the study collect and process the crime reports and statistics manually and keep the results in books and papers. To address the second objective, the results of the baseline study were used to develop the business processes and a crime mapping model, this was implemented successfully. The proposed model includes a spatial data visualization of crime data based on Google map. The proposed model is based on the Cloud Architecture, Android Mobile Application, Web Application, Google Map API and Java programming language. A prototype was also developed and the test results of the proposed system shows improved data visualization and reporting of crime data with reduced dependency on manual transactions. Keywords:Zambia Police, web application, Mobile application, cloud model, crime mapping, Spatial Data.