Master Dissertations

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Effective usage of social media for environmental awareness: a case of Rombo district council-Tanzania
(The University of Dodoma, 2022) Hermenegild, Hendry
The environment is the fundamental for all living things, and land conservation is of greater apprehension for the sustainable economic development. To attain higher level of land conservation there should be greater effort in creating awareness to people on land degradation conservation. This study intended to model the usage of social media for land degradation awareness in reducing environmental squalor. In this study crossectional research design, mixed research approach and a sample of 92 respondents were included. Also, data were analyzed by using descriptive analysis and PLS-SEM. The usage of social media in creating land degradation awareness include: creating conversation through social media on air, water, and waste disposal as like as ozone layer depletion and protection, sharing materials with peers related to land degradation and conservation, posting or introducing various campaign regarding Urban Sprawl and economic/trade related activities, allotment of problem-solving skills on land harms, sharing land humiliation problems and solutions, making people aware about the economic importance of the plants in the form of ethno botanical and ethno medicinal importance, leverage peoples’ lifestyles and waste related problems on trends and breaking news and sending message to keep people aware on water deprivation and shortages, loss of biodiversity and waste management. Apart from that, the factors influencing the usage of social media in creating land degradation awareness at Rombo district involves: social media increase level of attention, reasoning, understanding and happiness when reading comments given by viewers on peoples altitude toward land, getting a lot of information including global warming and land degradation when using social media, having many friends attached through social media whom usually posts various matters including land conservation which create gratification, using social media simplifies interaction with others/many to share matters on peoples altitude on land conservation, social media provides an easy and efficient way to interact with pears and share education on various matters involving waste management, gaining much awareness on water shortage and food problems issues through interaction with mates on social media, social media usage is a part and parcel of life. Further, a modified SEM model of social media usage facilitating the land degradation awareness in Rombo district was developed. The model obtained in the present study will be applicable in Rombo district-Kilimanjaro region; further, the Government should design the mechanism to enhance the model on provision of knowledge about the land degradation using innovative social media. Also, internet cost should be reduced, and strengthening public campaign program in social media to improve public awareness by enhancing the factors for land degradation awareness identified.
Evaluating performance of REST web service development frameworks
(The University of Dodma, 2021) Macha, Abraham G.
Nowadays web service Application Programming Interface (API) serves as a common method for integrating heterogeneous systems. REST architectural style is one of the most used web service implementation technologies. The presence of frameworks functional similarity imposes challenges in selecting appropriate frameworks for a particular project. This study aimed to evaluate the performance of existing REST web service development frameworks and propose a framework for assisting the selection of REST web services development frameworks. To achieve the objective, design science research methodology was employed. A thorough literature review was conducted to identify REST frameworks and evaluate performances using Apache JMeter automated software testing tool. Based on identified frameworks, similar function web service prototypes were developed and evaluated for both stress and load performance testing. In load testing, frameworks were tested under database interaction and without database interaction. In stress testing, prototypes were overwhelmed with huge requests to oversee their performance. Based on the research findings, a framework for assisting the appropriate selection of REST web service development frameworks was proposed and evaluated using ex-ante Design Science Research Methodology (DSRM) evaluation. Generally, the contribution of this study is not only the proposed framework but also a methodological contribution on how DSRM can be used to devise artifacts, particularly in the software development field.
Performance analysis of mobile networks communications for LTE in unlicensed spectrum
(The University of Dodma, 2021) Kadoke, Kadoke Marco
Long Term Evolution is the mobile technology that has been designed for high-speed internet connection. Long Term Evolution was due to previous mobile technologies to provide unsatisfactory data rate expectancy. Long Term Evolution was designed to operate in a licensed spectrum but not in unlicensed spectrum. Unlicensed spectrum is the spectrum that has higher, free and, larger bandwidth on which wireless devices operate. Increasing wireless capacity demand as well as bandwidth demand from mobile subscribers, the mobile operators now are increasingly concerned with deploying and utilizing unlicensed spectrum for mobile network communication. Nevertheless, due to the capacity demand, which has gone up due to the increase in iPad, Tablets, Smartphones, and other wireless devices usage in licensed spectrum, the channel capacity has now becomes a challenge. Therefore, to increase the channel capacity for high data rate communication, the Long Term Evolution technology to operate in unlicensed spectrum has been proposed for use as an alternative for this problem, and this challenge has put Long Term Evolution into consideration to operates in unlicensed spectrum. In this study, systematic literature reviews from different journal papers, Books and, simulation by using Matlab, Atoll and, Mentum Planet have been used to achieve the objectives of the. From the simulation, Long Term Evolution in the unlicensed spectrum, the throughputs were 100Mbps compared to 98Mbps in licensed spectrum. This improvement is equal to 2%. Therefore, an improvement of Long Term Evaluation performance in unlicensed spectrum when compared with LTE in licensed spectrum. But this is due to availability for larger free bandwidth, hence increase of channel capacity for mobile communication.
Viability of IOT in optimizing time spent searching for car parking in Dar es salam: a case study of Kisutu ward
(The University of Dodoma, 2020) Mzee, Abdulkarim Omary
Nowadays, searching for parking areas has been a headache matter for many drivers worldwide as drivers spend a lot of time searching for parking areas. This is contributed by significant increase of number of cars in the cities. Thus, as the number of cars increases in the cities, the demand for parking areas also increases, so as with no information to drivers of where they can park their cars, drivers spend long time finding parking areas. Therefore, to tackle this problem the study experimented the viability of IoT in optimizing the time used by drivers when searching for car parking slots in Dar es Salaam city specifically at Kisutu ward. The research adopted a case study research design where by semi-structured interviews and observation were used to acquire a good understanding of the parking problem. An experiment of random acquired respondents was conducted at Kisutu ward, whereby a parking system was deployed at JMall parking area. The system was developed by considering an IoT technique that was identified through systematic literature review. The time “before” and “after” intervention of IoT technique was recorded. Thirty (30) respondents (drivers) were enrolled for the experiment; 18 were males and 12 were females. The results in systematic literature review showed that, majority of studies used Ultrasonic sensor, Infrared sensors and radio frequency identification as technological technique for addressing parking problems in different part of the world. Furthermore, many of the reviewed literature didn’t mention what were the best technology for addressing parking problems especially in Dar es Salaam city, though IoT technologies have been successfully applied in various studies in identifying free parking spaces and sending that information to drivers through mobile devices and other communication channels. Hence, it was found that, a combination of the two technology (ultrasonic and infrared sensors) could have positive effect on addressing parking problems at Kisutu ward in Dar es Salaam. Majority (80%) of car drivers at Kisutu used their own experience in searching for car parking slots whereby about 87% of car drivers mentioned that, finding car parking slots is an issue at Kisutu ward. Of the two (2) conducted experiments (before and after), “before experiment duration was 28.1 minutes and “after experiment” duration was 8.37 minutes in average searching for parking slots. After experiment responded positively by 19.73 minutes lesser than before experiment in searching for car parking slots. Therefore, the study conclude that, although the technological technique used in this study was the combination of two sensors which seemed to suite the environment of Kisutu ward in Dar es Salaam, does not mean other researchers could not use more than two sensors. Hence, the study recommends that, future studies could use a combination of more than two sensors for addressing parking problems
Forecasting hydro-power generation using ARIMA model: a case of Mtera dam in Tanzania
(The University of Dodoma, 2020) Mnguu, Elihuruma Eliufoo
Electricity cannot be stored efficiently in the grid, resulting in a need for demand and supply to be in balance. The Transmission System Operator operates a real-time electricity market to acquire extra supply or load. Traditionally Presently, the power demand is growing fast every day, which need more resources and different grid constructions. Tanzania is one of the developing countries which needs to acquire and make enough sources, must achieve load cracking to accomplish stability of the power system. This study aims to achieve accurate, real-time and interpretable forecasting of the electricity generation. This study identifies the best fit time series model for forecasting electricity generation in Tanzania. This underpins the development of a time series model for forecasting electricity generation. Several time series models including SARIMA, SVM and ANN were fitted to the data, and it emerged that the most adequate model for the data was ARIMA. The nonlinear relation of electric net power generation is explored by historical monthly recorded data, this relation can help Tanzania Electric Supply Company Limited to predict net electric generation for the next month. Experimental results show that our proposed electric generation forecasting method based on ARIMA can get a suitable prediction model and achieve high predicted precision, which is in accordance with the real data in the record. There will be no increases in electricity generation in the Mtera dam over the next 2 years. It is recommended that TANESCO should use the model and its forecasted figures in its operational and planning activities.
Cloud computing framework for resource sharing among Tanzanian higher learning institutions
(The University of Dodoma, 2020) Omary, Bally
Higher Learning Institutions world-wide have been depending highly on Information Technology for their business requirements and service delivery. They need substantial investment for procuring and maintaining software as well as hardware. Due to the growing needs and financial crisis, universities are facing challenges in acquiring high-performance computing facilities to support not only advanced analysis but also scientific researches. This study intended to develop a cloud computing framework which can be used for sharing high performance computing resources of higher learning institutions. The study deployed both qualitative and quantitative approaches. Literature review and observation were used as data collection methods. The study furthermore used Microsoft Excel to analyse collected data. After the designing process, the proposed cloud framework was evaluated using two performance metrics. The parameters used for assessment were: response time and processing time. The study findings revealed that, increasing data centres’ power and speed is not always a factor for improving cloud computing performance but sometimes can add more cost. Furthermore, the findings discovered that the use of closest data centres provides better performance than placing them in separate locations. The study suggested the developed cloud computing framework model to be deployed and used by Tanzanian higher learning institutions to reduce procurement and running costs of high-performance computing resources.
ICT adoption framework for improved operational performance of hospitality and tourism industry: a case study of Zanzibar island hotels
(The University of Dodoma, 2020) Mohamed, Yussuf Abdulrahman
The present study had a focus on developing ICT adoption framework for the operational performance of the tourism and hospitality industry (HI) to solve the low adoption of ICT facing the Small Medium Size Hotels (SMSHs), which fall under the category of Small and Medium Enterprises. Zanzibar Island was used as a case study area. The study focused on major three objectives. The first objective was to analyze components for ICT adoption framework so as to match to the selected environment. The second was to design ICT adoption framework. Third was to validate the designed ICT adoption framework. The process of validation was in two forms, one is by gathering workshop of all stakeholders categories (ICT heads, managers, receptionists, and servers which were waiter and waitress) having the focus group discussion about the designed framework validation. The Second validation was by inferential statistical techniques such as regression variance and correlation. The framework comprised of components and the determinants of ICT adoption in tourism and HI. The study used a quantitative approach. The study employed descriptive statistics technique in analyzing data to extract the components of the ICT adoption framework by using frequency distribution. Also, the study employed inferential statistic applied to extract the determinants of ICT adoption framework, this was done by using regression analysis. The Unified Theory of Acceptance and Use of Technology (UTAUT) were used as a model to support the regression analysis with its respective constructs. The dependent construct was behavioral intention to adopt ICT with other six independent constructs. The findings revealed that all constructs were having a strong positive association with behavioral intention to adopt ICT except the social influence construct. On the other hand, components were analyzed according to the rank of its frequency and percentage indicating its importance. The tool used to analyze components and determinants was Statistical Package for Social Science 23.0 (SPSS). Finally, the ICT adoption framework was developed based on findings comprising of three important things namely, components, determinants, and the adoption pattern. Hence, this proposed framework was designed based on the study area and can be adapted to any other place as an exemplary guiding framework .
SACCOS credit rating prediction in Tanzania by using machine learning approach: a case of KKKT Arusha road SACCOS LTD
(The University of Dodoma, 2020) Ngimbwa, Aderitus
Financial institutions play a vital role to enhance the socio-economic development of any country. Despite being a key player in socio-economic development, large group of people is still isolated in accessing formal financial services. Saving and Credit Cooperative Societies (SACCOS) are type of Microfinance Financial Institutions (MFIs) that came to solve the problem of isolating many people in attaining financial services. Loan default is a big challenge that causes weak performance of many SACCOS and eventually collapses. In resolving the problem of loan default, this study analysed SACCOS credit rating in Tanzania by using machine learning approach. The secondary data of borrowers’ information from 2015 to 2019 were collected from KKKT Arusha Road SACCOS Ltd. The experiment was conducted in Anaconda environment with Python 3.8. Seventy percent (70%) of data were used for training and thirty percent (30%) for testing. The predictive variable influencing SACCOS members’ credit ratings were analysed by using Random Forest and Logistic Regression algorithms. The foremost variables found by the Random Forest algorithm in influencing SACCOS members’ credit ratings were age, interest rate and membership years while the least variables were marital status and gender. The foremost factors found by Logistic Regression include age, loan period and interest rate while the least factors include membership years and marital status. In evaluating the relationship between factors of SACCOS members and their associated credit rating the best results were obtained when the random forest algorithm was fitted with eleven features which include age, interest rate, membership years, loan amount, disbursement month, expired month, loan cycle, purpose, loan period, gender and marital status. The evaluation scores were 95%, 98%, 97%, 98% for Accuracy, Precision, Recall and F1-Score respectively. Also, for logistic regression, the best performance was obtained when the algorithm was fitted with three features which include loan period, interest rate and gender. The evaluation scores were 74%, 98%, 74%, 85% for Accuracy, Precision, Recall and F1-Score respectively. Overall machine learning provided the best results in analysing SACCOS credit ratings.
Knowledge and practice of wound care and their influence on surgical site infection among post caesarean section women in Dodoma region
(The University of Dodoma, 2020) Peter, Elizabeth Gabriel
Background: Tanzania is experiencing post-caesarean surgical site infections, which increases maternal morbidity and mortality. Poor wound care is reported to contribute to these infections and yet there is scanty research to inform about the community knowledge on wound care practice among post cesarean section women. This study attempts to address this gap by assessing the knowledge and practice of wound care and their influence on the presence of surgical site infections among post cesarean section women in Dodoma. Methods: This was a hospital-based cross-sectional analytical study that was conducted from May 2020 to July 2020 in Dodoma Region. Simple random sampling procedure was employed to select 183 post caesaren section women who were discharged within two weeks. An interviewer-administered questionnaire, observation and laboratory investigation were employed to generate the data from the sample. Statistical Package for Social Sciences (SPSS v.23) Software was used for data analysis. Descriptive statistics were employed to describe the distribution of all the study varibles while the inferential statistics helped to assess the influence of reported wound care practice and physical home environmental condition on the occurrence of wound infection. Chi-square test, odds ratio, adjusted odds ratio, and confidence interval were reported, and the level of significance was set at two sides of less than 0.05. Results: One hundred and eight three respondents participated in the study and the response rate was 100%. The age of research respondents ranged between 16 and 45 years with a mean (±SD) age 27.73± 7.25 years. Among 183 respondents, 20.8% had developed post-caesarean surgical site infections. Majority (98.9%) had inadequate knowledge on the diet facilitate wound healing. Less than half (48.6%) of the respondents had poor reported wound care practices. Those with poor reported wound care practices were (AOR: 5.959, 95%CI: 1.760-20.125179; P-value=0.004), to have developed post caesarean section surgical site infection Conclusion: More than half of post caesarean section women had good reported wound care practice. Majority had inadequate knowledge on the importance of diet to facilitate wound healing Furthermore post-discharge surgical site infection in Dodoma Region influenced by poor reported wound care practices.
Investigating the performance of ryu software defined network controller on different network topologies
(The University of Dodoma, 2020) Adam, Fumbuka A.
With the increasing complexity of networks, Software Defined Networks have been developed to logically control all networking devices such as switches and bridges centrally through the use of software defined network controllers. Controllers include beacon, opendaylight, floodlight, pox, ryu and many more. Ryu SDN controller is one of the most popular python developed controller. Just as many other controllers’ Ryu may become a bottleneck since all other network controller controls network devices. Desired performance in any network cannot be achieved if a controller is used without any guidance. Therefore, ensuring proper usage of a controller by knowing under what circumstances ryu controller performs efficiently prior to deployment of the network is an important course. SDN network topologies affect the performance of a controller basing of the complexity of the topology and type of the topology. This study aimed on investigating performance of Ryu software defined network controller on different network topologies. Ryu controller performance was evaluated by using throughput and latency performance metrics and openflow 1.3 was used as a southbound protocol. Throughput was measured with the help of iperf tool and latency was measured using ping command to determine the round trip time. Performances of Ryu in tree, ring, mesh, torus and hypercube topologies were measured and comparison was done. Significance of results obtained was also determined to see to what extent do network topology affect the performance of Ryu SDN controller. Results shows that default network topologies had a better performance than custom network topologies with the highest average throughput recorded of 9.57Gbits/sec on torus topology of 9 switches and lowest average latency recorded was 0.077ms on mesh topology. Furthermore, the results show that except for mesh topology all other topologies can give a desired performance in terms of latency at a scalability of up to 64 switches. Finally, a framework that guides the use of Ryu controller on different network topologies was developed with the help of the results obtained after the performance evaluation.
Investigating the impact of FTM and IDM-IM mobility models on the performance of voice CODECS in VANET
(The University of Dodoma, 2020) Michael, James Silayo
Vehicular Ad-hoc Network (VANET) is a subclass of Mobile Ad-hoc Network where by vehicles on the road network are able to exchange data. Many automobile manufacturers make vehicles which are pre-equipped with devices such as Geographical Position System (GPS), Radar and Lidar, Bluetooth, Onboard LTE module. Therefore, in the future, Self-driving cars will be available in many countries and voice communication between vehicles will present many services ranging from safety to non-safety. Several researches have been conducted to evaluate the impact of voice CODECs in MANET. However, the results in MANET cannot be applied in VANET due to the unique characteristics such as high mobility of vehicles, frequently disconnection of network, rapid topology change and high node density. In this study, the impact of IDM-IM and FTM mobility models on the performance of G.711, G.723.1, G.729A, GSM.AMR and GSM.EFR voice CODECs was investigated. VanetMobiSim 2.2 and NS-2 were used as traffic simulator and network simulator respectively. The simulation results were presented using four performance metrics (average End-to-End delay, average jitter, packet delivery ratio and Mean Opinion Score). The results showed that G.729A provides good voice quality under IDM-IM mobility model. This is due to fact that, G.729A provides acceptable delay, packet delivery ratio and MOS as recommended by International Telecommunication Union for packet-based voice communication.
A comparative study on performance of support vector machine and convolution neural network on Tanzania sign language translation using image recognition
(The University of Dodoma, 2020) Myagila, Kasian
Sign language has been used by Speech impaired people for communication purposes. Despite being an effective form of communication for speech impaired people, still there is a challenge for people who are unaware of sign language especially those with no such impairment to communicate with speech impaired people. Since Sign Language is a visual based language, several machine learning techniques have been used in sign language translation for better performance results. However, sign languages are different and no study has been found in Tanzania Sign Language, which is a language used by speech impaired people in Tanzania. Moreover, no study has revealed whether there is significant difference in performance between Support Vector Machine and Convolution Neural Network despite the fact that literature show that both have significant performance in different sign languages. This study aimed at comparing the performance of Support Vector Machine and Convolution Neural Network on translating Tanzania Sign Language through image recognition. The study employed Tanzania Sign Language images as datasets whereby 30 words were chosen from the context of education. The study used dataset of 3000 images that were taken using a camera. To reduce the dimension of datasets, the study adopted Principal Component Analysis to perform feature extraction. Furthermore, the study employed a Combined 5x2cv F test to compare the techniques to determine the significant difference in the performance of the algorithms. The findings revealed that both techniques have significant rate of both accuracy, precision and recall. Convolution Neural Network scored 96% in all of the parameters while the SVM with Histogram Oriented Gradient feature scored similar rate in precision but lag on recall and accuracy by 1%. Additionally, the results of using Combined 5x2cv F test yield a p-value of 0.0258 which shows that there is a significant difference in the performance of the two techniques when used to translate the Tanzania Sign Language. Therefore, this study recommends the use of Convolution Neural Network since it has high accuracy and it can provide a significant higher rate of performance compared to Support Vector Machine.
Prediction of annual revenue collection by using data mining techniques: a case of local government authorities in Tanzania
(The University of Dodoma, 2020) Rajabu, Yusuph
Local Government Authority have to pay increasing attention to the importance and need of annual revenue prediction due to financial, economic and political stress. Currently, judgmental models are used for LGAs revenue prediction with poor accuracy. Due to increasing importance; the aim of this study is to develop a model for predicting annual revenue collection of LGAs in Tanzania with the help of agricultural weather condition, exchange rate, national GDP, Council population, number of council enterprise, previous annual collection, and physical person income tax by using support vector regression. The data used for this paper was from 1ST July 2009 to 30 June 2019 hardly ten year data. Support vector regression and artificial neural networks are the algorithm which are used for predicting because of their competences of pattern recognition and machine learning. In this study the two algorithm ANN and SVR were used to develop a model for predicting the annual revenue collection for LGAs and their performance has been compared for evaluation so as to get the best performer. According to the results there are high similarities between predicted and actual data for both SVR and ANN. Predicted results of this study shows that SVR score 94.2% model accuracy as compared to 85% model accuracy of ANN. Because of this high accuracy and outperforming of SVR, LGAs in Tanzania can be able to apply SVR model as a revenue predictive tool in upcoming fiscal year and able to bridge a gap between revenue predicted versus actual revenue collection.
Performance prediction in mathematics using educational data mining techniques: a case of Mzumbe University in Tanzania
(The University of Dodoma, 2020) Mushi, Paul Kavishe
Nowadays, Higher Learning Institutions (HLIs) store a large amount of students’ data. However, those data are not widely used to solve the academic problems of the students that are available at the HLIs such as poor performance of the students in some of the courses. Educational Data Mining (EDM) is the technology that can be applied to predict the performance of the students on the available dataset at the HLIs. This study intended to solve the problem of poor performance in Mathematics for degree management students at HLIs using EDM techniques. The purpose of the study was to predict Management degree Students’ performance in Mathematics using EDM taking Mzumbe University (MU) as a case study. The quantitative research approach was applied in this study basing on the design science steps. Secondary data were collected to create the dataset through document review from examination office (final examination (FE), course work (CW) and Remarks), admission office (age, gender, entry category and ordinary level mathematics grades), accounts office (sponsorship details), department of mathematics and statistics (number of instructors) and accommodation office (living location) at MU including Main campus Morogoro and Mbeya Campus. Different Machine Learning (ML) algorithms were applied on training set (60%) such as K-Nearest Neighbor (K-NN), Random Forest (RF), Decision Tree (DT), Support Vector Classification (SVC) and Multilayer Perceptron (MLP). ML algorithms were validated using 10-fold cross-validation and validation dataset (20%) and the best algorithms were RF, DT and K-NN. During the evaluation of the three best ML algorithms using 20% of the dataset, RF ML algorithm was found to be the best for model development in mathematics performance prediction in this study with the accuracy of 99% and F1-scores of 99% and 100% for fail and pass class respectively. Moreover, DT was able to generate rules that were applied to recommend the minimum grade of D for ordinary level mathematics in admission to degree management students to reduce the failure rate at HLIs.
Performance analysis of mitigation techniques for electromagnetic interference between fm band broadcasting services and aeronautical communication systems
(The University of Dodoma, 2020) William, Kulwa
A means of providing radio broadcasting services by using Frequency Modulation (FM) technique is called FM Broadcasting. Aeronautical Communication Service refers to radio communication service intended for the safety operations of aircrafts. Aeronautical communication service is categorized by International Telecommunication Union (ITU) as a primary service. This service needs to be offered under frequency interference free environment for ensuring aircrafts navigation safety. However, different studies have shown that there exist frequency interferences on this service which are caused by Frequency Modulation (FM) Band Broadcasting services through frequency intermodulation products. Performances of different Electromagnetic Interference (EMI) mitigation techniques were analyzed by simulation method using Python, a high level multipurpose programming language. Performance analysis of different EMI mitigation techniques was based on Signal – to – Interference-plus – Noise Ratio(SINR) metric as a criterion. The results show that by using reduction of transmitter power technique at the transmit power of 40dBm,a dipole antenna and corner reflector antenna give SINR values of 6.72 dB and 13.05 dB respectively. In reducing transmitter power to 30 dBm, the SINR values for a dipole and a corner reflector antenna were-3 dB and 2.98 dB respectively. In this technique a dipole antenna performed better as compared to corner reflector antenna. On the other hand, by using antenna beam tilt technique, a SINR value of 14 dB is achieved at 0.5 degree of tilt angle and atilt angle of 1 degree gives 22 dB as SINR value when a corner reflector antenna is used. A dipole antenna gave SINR value of 7.5 dB at 0.5 degree and 16.4 dB at 1 degree. In this technique, a dipole antenna performed better than a corner reflector. In both techniques a dipole antenna had better performance in EMI mitigation effect.
Framework for improved security on usage of mobile money application based on iris biometric authentication method in Tanzania
(The University of Dodoma, 2020) Mega, Bakari
The usage of personal identification number (PIN) as an authentication method in accessing mobile money services (MMSs) conveys many security flaws as it is easy to be forged, not being hidden when entered, and easy to be shared among users, all of these may cause unauthorized access to MMSs. This study focused on proposing a framework to improve security on the usage of MMS by using two-factor authentication (2FA) of PIN and iris biometric authentication method in Tanzania. With this framework secured access to MMS can be archived. The study used the mixed research approach to achieve the study objectives, observation, and literature review applied in designing the framework to improve security level in accessing MMS based on iris recognition biometric authentication method (IRBAM) and develop mobile money applications based on the designed framework. Questionnaires were used to obtain the user’s perceptions of evaluating the effectiveness of the framework based on the usage of developed mobile money application. The rapid application development (RAD) approach is used in the development of mobile money application based on the proposed framework. Use-case and flow chart diagrams were used to depict the flow of information between mobile money users (MMU) and mobile network operators (MNO). Different tools and programming languages were used in a development environment, for example, Microsoft Visio, MySQL database, Android studio, PHP, JSON, and java. The results obtained from the evaluation of the effectiveness of the developed mobile money application have concluded that 46.1% and 51.9% of customers and agents respectively have strongly agreed that the implementation of the proposed framework will eliminate unauthorized access to MMS. Furthermore, 85.5% of customers and 71.6% of agents have accepted to use the developed mobile money application based on the proposed framework. The implementation of the proposed framework using 2FA of PIN and iris biometric authentication method has shown to remove unauthorized access to MMSs. This study recommends MNO and other stakeholders to use the findings of this study as a roadmap to help guide them in improving security in accessing MMS.
Investigating instructional design practices in e-learning environment in Tanzanian public universities
(The University of Dodoma, 2020) Mwaluanda, Mevis Steven
The research work is positioned within Instructional designs and E-learning field with a contribution to the understanding of how the instructional materials are designed specifically in the e-learning environment. Instructional design is essential in E-learning as it involves the systematic process of exploiting the philosophies of teaching and learning so as to construct the most required effective and equitable knowledge. Furthermore, the instructional design allows content developers to design and develop effective instructional materials for learning purpose. When properly followed, instructional design models resulting to a well-organized learning material. However, some of the investigated universities they use a single model while excluding some of the phases from the model in instructional materials designing. In E-learning environment, such kind of instructional materials designing may impose difficult in the learning process. The research work was basically qualitative with some aspects of a quantitative approach especially in the analysis phase. The investigated universities were Open University of Tanzania (OUT), University of Dar es salaam (UDSM) and Muhimbili University of Health and Allied Science (MUHAS). A sample of twenty one (21) courses (seven (7) courses from each university) was purposefully selected while a total of fifteen (15) experienced instructional designers and instructors were interviewed. Data were collected through in-depth interviews, document review and artifacts review. To perform data analysis, Nvivo software was used. Furthermore, Activity theory (AT) was used as a lens for examining the actions performed by the instructional designers and instructors. Additionally, AT has been used to elaborate how does instructional designers and instructors collaborate to achieve their common goal. The study analyzed the strength and weakness of the designed (posted) instructional materials from the investigated universities. The results revealed that in one out of three universities the reviewed courses are accompanied with all key features for Eleaning instructional material. It is observed that, the practices in place for instructional materials designing are directed by; Instructional design template, Instructional design guideline and instructional design models. Furthermore, the study recommends the best ways to be used in designing and developing effective vi instructional materials. For the existing instructional materials to fit in the e-learning environment, the designed materials should cover all of the key features which are participant engagement, inclusion of problem-based learning tasks, contents with media richness, instant feedback, numerous assessment activities and interactivity.
Optimization of WISN algorithm for human resource management at health facilities in Tanzania a case of Dodoma city council (DCC)
(The University of Dodoma, 2020) Salum, Nassoro
Tanzania and other developing countries use a model called Workload Indicators for Staffing Need (WISN). The use of the WISN model in Health Human Resource planning and management is limited by several factors including budget, prospected retirees, and strategic priority. Also, the WISN users find the methodology, especially the implementation of its technical steps complex and laborious, thus makes the tool difficult to use. The algorithm used needs to be optimized or improved to overcome different challenges like budget and prospected retirees as reported by different researchers. This study aimed to investigate the additional components necessary for optimizing WISN in the context of Tanzania health facilities. The study employed a qualitative and quantitative research strategy and adopted an interpretive approach for data analysis. The findings were obtained through the use of case studies conducted in two ministries, President's Office Regional Administration and Local Government (PO-RALG) and Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDEC) in Tanzania. Methods such as key informants’ interviews and document reviews were used in collecting data for the study. Besides, during this study development of optimized WISN software was done and demonstrated to department officials to obtain their views on the system performance by comparing with the current WISN system. The findings originated from this study it suggested the inclusion of the other components like budget constraints, strategic priorities, staff allocation, and the number of prospected of retirees to the existing WISN. The study concludes by suggesting the optimized WISN can further be improved to accommodate teacher’s allocation
Detection of DDoS attacks and flash events occuring simultaneously in network traffic using deep learning techniques
(The University of Dodoma, 2020) Mihanjo, Carl Eginald
Recently, the advancement of technology and internet contributes to the increase of the network traffic over the globe. It improves digital services delivery over the global network such as online shopping, television, and streaming. However, as digital services become one of the de facto applications over the internet, a number of attacks on them have been increasing which raise security concerns. Some of the major attacks are Distributed Denial of Service (DDoS) and Flash Events (FE). One hand DDoS attacks mainly focus on disrupting the legitimate users to access the internet. On the other side, FE occurs when there is a rapid growth of legitimate users that access the service over the internet and overload the system. DDoS attacks and FE have similar behaviour however, they need different countermeasures. The major challenge lies in detection the attacks especially when DDoS and FE happen simultaneously. The study proposed a model to detect the FE and DDoS attacks when occurring simultaneously in network traffic using deep learning techniques with three different hidden layers and two optimizers. Validations of the models were tested with data from the real network traffics and the model with high performance was selected which was a model with three hidden layer and Adam optimizer. The result shows a proposed model achieved a good accuracy of99% and less than 1% false alarm.
Comparative study of pagerank and hits algorithms for reciprocal link prediction in online social networks
(The University of Dodoma, 2020) Pallangyo, Brian Somi
Online Social Networks (OSN) provides active space for digital human interaction and are used daily. Human engagement is reflected by exploiting the dynamics of OSN, where the fundamental problem is to infer future interactions on the network, called link prediction. Most studies have employed classical algorithms which consider node similarity but neglected the link analysis algorithms which consider topological structure. This study focused on the comparative study of predicting reciprocal interaction from para-social interaction using algorithms. Particularly, this study selected PageRank and HITS, which are considered famous link analysis algorithms with high order heuristics. Network simulation was performed to understand the performance of the algorithms when used to predict reciprocal link formation by employing machine learning techniques. For the experiment, two datasets were used to ensure the reliability of the results. Initially, the publicly available secondary dataset of Twitter was used followed by primary dataset crawled from Mayocoo, both of which are directed networks. The resulting networks from both datasets adhere to power-law distribution. Resource allocation was used as the baseline for the study after outperforming Adamic-Adar, Jaccard Coefficient, and Preferential Attachment. The result of this study showed that both PageRank and HITS surpassed the baseline in performance of prediction. Thus, PageRank has an accuracy improvement of 1.8% with precision and recall of 4.8% and 1.1%, respectively. Furthermore, this improvement comes with a balance of 3% (f1-measure). When HITS is used, there is an improvement accuracy by 5%, with 15.1% (precision), 7.9% (recall) and 11.5% (f1-measure). These empirical results demonstrate that HITS outperforms PageRank in prediction performance. Also, the results from the computational test showed that PageRank uses less computational resources compared to HITS. This study suggests the use of link analysis algorithms over classical algorithms for reciprocal link prediction in OSN. Furthermore, the use of HITS is recommended when prediction performance is vital compared to computational cost, otherwise, PageRank in cases were computational resources are minimal.

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