Climatology-aware health management information system to enhance cholera epidemic analysis and prediction in Tanzania

This research article published by the International Journal of Advanced Technology and Engineering Exploration (IJATEE), Volume-6 Issue-55 June-2019The cholera epidemic remains a public health threat in many developing countries including Tanzania. It affects vulnerable populations living with an unreliable water supply and sub-standard sanitary conditions. Various studies have found that the occurrence of cholera has strong linkage with environmental factors such as climatology aspects and geographical location. In addition, climatology has been strongly linked to the creation of weather patterns that favor the transmission and growth of Vibrio cholerae, which causes the disease. There are several studies that have been conducted to integrate environmental factors into the existing health management information systems (HMISs) in order to enhance the analysis of cholera epidemics in Tanzania. This work explored how well climatology factors have been integrated into these existing HMISs and the potential of the systems in enhancing cholera epidemics analysis. We found that most of the existing HMISs have not explicitly integrated environmental and climatology features for effective analysis of diseases. We thus proposed the design and development of an effective Climatology-aware HMIS. Then, evaluate it with clinical and environmental data such as; geographical location, weather, conditions of the day, and date on set, of 22 medical students staying in the Mweka district in Tanzania. The results of system evaluation showed that 87% provided positive feedback on the capacity of the developed system, towards enhancing the cholera epidemic analysis and prediction linked with environmental factors particularly the climate change variables. The study recommends the review of systems and policies in the health sectors in order to adapt climatology factors

A study of users’ compliance and satisfied utilization of biometric application system

This research article published by Taylor & Francis Online, 2020Currently, the adoption rate of biometric technology has speedily grown in all applications. The technology is considered as an effective measure for the protection against crime. However, there is a concern that it violates the privacy and rights of the individuals. For instance, the possibility of fraud, identity theft, civil liberty violations, and inaccuracy of data. As a result, create the conflicts between service provider and public as they may be accused of a crime or become a victim of discrimination. This study constitutes exploratory research and is restricted to the usage of the biometric application system within the passport. It aims at discovering the substantial acceptance of users in implementing the biometric application for the East African passport (Uganda). Factor influencing users’ opinions regarding the acceptance of the biometric application, User willingness, trust and techniques for securing the biometric information are presented. Strategies aimed at regulating the protection of biometric data on the usage of the application are explained. The findings suggested encryption techniques as the most favorable tactic of protecting the biometric data application. Therefore, best practices such as individual desirability, practical accurateness, and eagerness are required

Big Data Analytics Framework for Childhood Infectious Disease Surveillance and Response System using Modified MapReduce Algorithm

This research article published by International Journal of Advanced Computer Science and Applications,Vol. 12, No. 3, 2021Tanzania, like most East African countries, faces a great burden from the spread of preventable infectious childhood diseases. Diarrhea, acute respiratory infections (ARI), pneumonia, malnutrition, hepatitis, and measles are responsible for the majority of deaths amongst children aged 0-5 years. Infectious disease surveillance and response is the foundation of public healthcare practices, and it is increasingly being undertaken using information technology. Tanzania however, due to challenges in information technology infrastructure and public health resources, still relies on paper-based disease surveillance. Thus, only traditional clinical patient data is used. Nontraditional and pre-diagnostic infectious disease report case data are excluded. In this paper, the development of the Big Data Analytics Framework for Childhood Infectious Disease Surveillance and Response System is presented. The framework was designed to guide healthcare professionals to track, monitor, and analyze infectious disease report cases from sources such as social media for prevention and control of infectious diseases affecting children. The proposed framework was validated through use-cases scenario and performance-based comparison

Developing an Algorithm for Securing the Biometric Data Template in the Database

This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019In the current technology advancement, biometric template provides a dependable solution to the problem of user verification in an identity control system. The template is saved in the database during the enrollment and compared with query information in the verification stage. Serious security and privacy concerns can arise, if raw, unprotected data template is saved in the database. An attacker can hack the template information in the database to gain illicit access. A novel approach of encryption-decryption algorithm utilizing a design pattern of Model View Template (MVT) is developed to secure the biometric data template. The model manages information logically, the view shows the visualization of the data, and the template addresses the data migration into pattern object. The established algorithm is based on the cryptographic module of the Fernet key instance. The Fernet keys are combined to generate a multiFernet key to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and securely preserved in the database. In the event where an attacker tries to access the biometric data template in the database, the system alerts the user and stops the attacker from unauthorized access, and cross-verify the impersonator based on the validation of the ownership. Thus, helps inform the users and the authority of, how secure the individual biometric data template is, and provided a high level of the security pertaining the individual data privac

The Challenges of Adopting M-Learning Assistive Technologies for Visually Impaired Learners in Higher Learning Institution in Tanzania

This research article published by the International Journal of Emerging Technologies in Learning (iJET), 2020In the past decades, the world has experienced major changes in the advancement of learning technologies which has enabled learners to engage in their learning activities anywhere. The penetration of mobile phone internet users in Tanzania has been increasing from 2 million in 2011 to 23mil in 2017 The adoption of mobile-based learning (M-learning) for students who are visually impaired in Tanzania has become a major bottleneck since most of the e-learning contents assume that learners have sight and thus include a lot of visualizations. This causes visually impaired students in higher learning Institutions (HLIs) to face challenges such as technical knowledge gaps. Lack of skills and inaccessibility of online contents, which then lead to drop out of the university. The aim of this study is to determine the awareness and usage levels of existing mobile assistive technologies for visual impairment, and the remaining challenges that visually impaired students face, when using such tools on smartphones to access m-learning content from HLIs. in Tanzania. The research was conducted an observational and contextual inquiry study at three major HLIs. We found that 67% of respondents did not have knowledge of m-learning assistive technologies, and their technology barriers for visually impaired students. Also, knowledge, accessibility of Assistive technology and affordability can hinder the adoption of m-learning in Higher Learning Institution

Characteristics of smallholder dairy farms by association rules mining based on apriori algorithm

This research article published International Journal of Society Systems Science (IJSSS), Vol. 11, No. 2, 2019Characteristics of smallholder dairy farmers across regions are highly similar. However, introduction of improved farm management practices and extension support can be effective if specific constraints are identified for each farm typology. So far, approaches used to formulate farm types and characterise farming systems are not tailored to studying hidden patterns from farm datasets. Using the apriori association rules mining algorithm, characteristics of four smallholder dairy farm types are studied. Applying the power of the ArulesViz package, frequent items were visualised. These visuals which display some hidden attributes, solidified understanding on the key determinants for change in the studied farm types. The hidden smallholder farm characteristics were identified in addition to those given by cluster analysis in preliminary studies. Characterising smallholder farm data by using association rules mining is recommended in order to understand such systems in terms of what/how the majority practice rather than basing on cluster averages

Research Data Management Among Researchers in Higher Learning Institutions of Sub-Saharan Africa

This book chapter published by IGI Global, 2020Advancement in information and communication technologies has made it easier for researchers to capture and store myriad data at a higher level of granularity. Higher education institutions (HEIs) worldwide are incorporating research data management (RDM) services to enable researchers to work with their data properly. This chapter focuses on creating awareness amongst researchers on how researchers and HEIs can form strategies, design and restrict data management plan (DMP), integrate research data life cycle, and ensure quality data sharing, as well as integrate with developed RDM policies and guidelines to curb challenges prohibiting the practice of RDM in HEIs

Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry

This research article published by SAGE Journal, 2020Peer-to-peer learning paradigm is seldom used in studying how farmers can increase yield. In this article, agent-based modelling has been applied to study the chances of dairy farmers increasing annual milk yield by learning better farming strategies from each other. Two sets of strategies were considered; in one set (S), each farmer agent would possess a number of farming strategies based on their knowledge, and in a second set (S'), farmer agents would possess farming strategies that they have adopted from their peers. Regression models were used to determine litres of milk that could be produced whenever new strategies were applied. By using data from Ethiopia and Tanzania, 28 and 25 determinants for increase in milk yield were fitted for the two countries, respectively. There was a significant increase in average milk yield as the farmer agents interacted and updated their S'– from baseline data, average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. The peer-to-peer learning approach details an inexpensive method manageable by the farmers themselves. Its implementation could range from physical farmer groups to online interactions