In Tanzania, members of parliament rely on both traditional channels and social media to gather citizens’ concerns. However, limited access to internet-enabled devices, the unavailability of members of parliament, poor attendance at public meetings, and high internet costs create a communication gap in civic participation. Existing civic technology solutions often depend on internet-based platforms, excluding users who rely on feature phones and highlighting a technical gap in prior research and system design. This study develops a context-aware, inclusive digital communication system that bridges infrastructural and technological barriers to foster civic engagement in low-resource environments. Through a literature review, gaps in existing civic technology implementations were identified, and the system was developed using the Extreme Programming Agile Methodology to produce a hybrid platform combining a web application and a two-way messaging system. The resulting Public Participation Information System allows citizens to raise concerns offline via feature phones or online through a bilingual web application in Swahili and English. The system prioritizes accessibility for low-resource users and incorporates features such as availability scheduling, task delegation, and concern tracking. Feedback indicates that it effectively reaches under-connected populations and promotes more inclusive civic engagement. Overall, the system addresses the digital divide in civic communication and offers a scalable framework that integrates low-technology access with modern information systems to support democratic participation in resource-constrained settings

Mobile technology has created a platform that allows companies to reach their clients more quickly and tackle issues more efficiently. Habari node PLC handles and supports its customers using a web-based customer relationship management (CRM) system. The company receives customer support requests via phone calls, and due to its large customer base, the volume of these calls is high. Furthermore, the number of clients joining continues to grow daily. This study aimed to develop a ticket-tracking helpdesk system that enables quick and effective customer service. In the initial stage, the study examined the existing system to compile a list of requirements for the new system. The development and testing of the Ticket Tracking Helpdesk System (TTHS) were carried out using an agile extreme programming approach. The developed system enables clients to submit their requests and allows customer support staff to view these demands. The results indicate that the system facilitates the opening and assigning of tickets and tracks operations when closing tickets. Additionally, it provides SMS notifications between users. The TTHS enhances the efficiency of customer support services by decreasing response time, reducing phone calls, and preventing user data loss.

The growing volume of electronic waste (e-waste) in Tanzanian public institutions poses serious cybersecurity risks, as discarded devices often contain sensitive data vulnerable to unauthorized access. This study examines these risks across 11 public institutions, involving IT staff, e-waste handlers, policymakers, and environmental officers. It applies Routine Activity Theory, a framework that explains risks as arising when cybercriminals exploit unsecured e-waste due to weak regulations. Through interviews and focus group discussions, the research identifies key vulnerabilities: data leakage from improper sanitization, regulatory gaps, and risks from informal disposal methods like auctions. These findings highlight the need for stronger oversight to prevent data breaches. The study proposes a framework that categorizes devices by risk level and integrates secure sanitization protocols, such as data wiping or destruction. Policymakers and institutions must urgently adopt these protocols to protect sensitive data and promote sustainable e-waste management in Tanzania’s public sector.

Background: Time-series models forecasting plays key role in predicting TB cases. Despite of its importance some models consist of limitation that decrease its efficiency. To overcome this, adoption of optimal model with highly proficient forecasting is encouraged. Objective This study was aimed to adopt an optimal Time Series model for forecasting new and relapse Tuberculosis cases in Tanzania. Setting: The study use ARIMA, HWES and LSTM Time series models to find optimal modal that works efficiently on forecasting of TB cases. Methods: A cross-sectional study was conducted at Kibong’oto National Infectious Diseases Hospital, Moshi Tanzania from January 2021 to December 2024. Muilt- stage sampling was used to recruit 3911 TB cases registered from January 2015 to December 2020. A Microsoft Excel 2019 was used to create database with total of 2 columns and 72 row. Dataset was divided into training and testing cutoff points of 69% and 31% respectively to obtain optimal time series models as per Xu & Goodacre (2018). Tables and figures were used for interpretation of results. Results: A total of 3911 TB cases with annual average of 651.83. The periodic variations and declines were observed. The error metric values MAE, MAPE, and RMSE show ARIMA modal better performance on forecasting the TB cases due highest scores than others modals. Conclusion: The ARIMA model offers advanced predictions of TB cases, that help timely planning of prevention and control measures.

Radio frequency energy harvesting (RFEH) is considered an optimal and environmentally friendly solution for energizing sensor devices and prolonging the lifetime of wireless sensor networks (WSNs). Despite being studied and experimented in several environments where WSNs are used, studies and experiments related to RFEH in underground wireless systems are limited to near-field wireless power transfer (WPT), measurement of received signal strength, and current conduction. The goal of this study is to examine the possibilities and challenges of actualizing RFEH in wireless underground sensor networks (WUSNs). A radio-frequency (RF) spectral survey was conducted, and a comparison was performed with similar surveys conducted worldwide to determine the generally available ambient RF energy. Using the aboveground to underground (AG2UG) RF communication model, the signal path loss was analyzed under varying conditions. By relating the ambient RF power and AG2UG signal path loss, it was found nearly impossible to harvest ambient RF energy with the harvesting antenna buried within the soil, as the best-case environment will require a rectenna with sensitivity of at least -62.75dBm. However ambient RF energy can be harvested when the harvesting antenna is in free space, while the other components are underground and will require a high sensitivity of at least -40 dBm. Another possibility for underground RFEH is the use of a dedicated WPT device located 1m above the ground, transmitting at 20 dBm with the RF energy harvester 30 cm below the soil surface with a sensitivity of at least -30 dBm.

Deaths are caused by breathing oxygen-deficient air all around the world. Nitrogen gas displaces oxygen in the air, bringing the percentage of oxygen down below 21

Objectives: To identify the hidden patterns in the K-means clustered dataset for the Pangani Basin using the Apriori algorithm through frequent patterns and association rules to enrich cluster characteristics. Methods: Frequent patterns and association rule mining were used to discover the hidden attributes in the K-means clustered dataset. Measures of minimum support ranging from 0.5% to 5% and minimum confidence ranging from 50% to 100% were used to generate a manageable number of rules which were then filtered for redundancy. Lift value >1.0 was used to determine the rule's interestingness while Arules and ArulesViz in R were used to visualize generated rules. Findings: Clusters one to four generated 25, 31, 47, and 49 rules respectively at a minimum confidence of 50% and minimum support of 2% in the first two clusters and 1% in other clusters. Furthermore, water users in cluster one were observed to abstract more water than the three clusters, while their water use fee also reflected on the amount they abstracted. In clusters two and three, water users identified the same amount of water source capacity but differed in the amount requested and water use fee. Water users in cluster four were identified with less water source capacity and fewer amounts abstracted than other clusters. However, their water use fee identified was higher than those in cluster three, with high water source capacity and high amount requested. Such a difference is attributed to the type of water use for cluster three users being domestically supplied through community water supply entities to help villagers access water. In contrast, the water use for users in cluster four is domestic and commercial. Novelty: When aggregated with the clustering observations, the identified association rules mining results provide a broad understanding of water users' characteristics for better water allocation and rationing. Keywords: Association rule, Frequent Patterns, Apriori, Characterization, Pangani Basin

Technology is involved in different sectors to improve service delivery. Habari Node PLC (Public Limited Company), located in Arusha, Tanzania, offers Internet services and various additional ICT-based business solutions. The company has a website that is used to provide information related to the services they provide with their cost. However, the current website is not mobile user- friendly and is not integrated with an electronic payment to pay for those services because the fees are currently paid manually. This study aimed to develop a mobile-based application for e-services and e-payment which will allow the user to access all information related to the services provided by this company and be able to perform e-payment to the subscribes services. The payment will be made through mobile money or credit card, depending on the customer’s choice.

A forward collision avoidance system is an advanced driver assistance system that alerts the driver or maneuvers for safe motion in case of the occurrence of an imminent collision. In this research, an efficient reinforcement learning algorithm that actuates the car to move forward, steer left, right, and stop was designed for autonomous vehicles. Currently, forward collision avoidance systems are based on input commands from the sensors like Lidar and Camera to the system and the output is based on the commands initialized. With this model, the vehicle gathers data using an RGB Camera and collision sensor while moving on the road in a simulated environment. Scenarios are developed which include cars moving around corners, straight road, and in a more urban layout with other obstacles like cars within the environment. Reward flags are given for no collision and penalty for collision with obstacles within the environment. Model testing was done in Carla’s simulator and analysis of the model was done on a Tensor board and recorded simulation as the vehicle moves within the environment. An optimized deep Q-learning algorithm that relies on deep reinforcement learning was developed under constrained conditions in a Carla simulation environment with an overall accuracy of 64

Savings and Credit Co-Operative Societies (SACCOS) are seen as viable opportunities to promote financial inclusion and overall socioeconomic development. Despite the positive outlook for socioeconomic progress, recent observations have highlighted instances of SACCOS failures. For example, the number of SACCOS decreased from 4,177 in 2018 to 3,714 in 2019, and the value of shares held by SACCOS members in Tanzania dropped from Tshs 57.06 billion to 53.63 billion in 2018. In particular, there is limited focus on predicting SACCOS failures in Tanzania using predictive models. In this study, data were collected using a questionnaire from 880 members of SACCOS, using a stratified random sampling technique. The collected data was analyzed using machine learning models, including Random Forest (RF), Logistic Regression (LR), K Nearest Neighbors (KNN), and Support Vector Machine (SVM). The results showed that RF was the most effective model to classify and predict failures, followed by LR and KNN, while the results of SVM were not satisfactory. The findings show that RF is the most suitable model to predict SACCOS failures in Tanzania, challenging the common use of regression models in microfinance institutions. Consequently, the RF model could be considered when formulating policies related to SACCOS performance evaluation.

The rise of digital smart energy meters with advanced industrial communication protocols has availed opportunities to easily harvest utility energy data in the modern industry 4.0 era. Industrial utility load profile data aggregated over time can be used as a dataset for training of machine learning models for the prediction of future consumption and accrued energy bill costs in an industrial setup. This paper presents a smart industrial electrical energy analytics and forecasting system that utilizes ultra-modern machine learning techniques to predict energy consumption and estimated energy bill based on historical data. An electronic data acquisition unit that comprises a Raspberry Pi 4B, an industrial energy protocol converter, and a 3-phase smart energy meter was developed and deployed for data collection. Readings were stored locally on the Raspberry Pi every 5 min and synched to the cloud for redundancy purposes. Machine learning models were developed using the logged data to predict future energy consumption patterns. Two time-series machine learning forecasting algorithms, i.e., Facebook Prophet and Auto-Regressive Integrated Moving Average (ARIMA) were employed in training the model using the train dataset and exhibited Mean Absolute Percentage Error (MAPE) of 17.72 and 18.86, respectively, when tested with unseen data. A web dashboard was developed to visualize readings from the data acquisition unit as well as forecasted energy trends from which different energy analysis and insights can be generated.

The connection of devices in distributed environments produces and shares a vast amount of data useful for different organisational decision-making. In healthcare service organisations, for example, multiple e-health systems from different departments or facilities connect and share health data and information. During sharing, proper management is important to ensure the information is secure against intruders. Machine learning as a non-conventional security technique can be used along conventional techniques like firewalls, antivirus and intrusion detection systems to predict future network threats and other anomalies using historical backgrounds and other features. However, some machine learning algorithms have complex computation thus requiring resourceful systems in terms of network bandwidth, CPU power, memory, and storage capacity. In resource-constrained environments, therefore, special consideration is needed to ensure that the analysis of the big data is successful and that the benefits associated with them are effectively obtained. In this paper, a Machine Learning algorithm was selected among four algorithms whose performance was compared through various performance metrics. Classification accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Absolute Error (RAE) among other performance metrics were used to compare the ANN, Random forest, Decision trees, and Naïve byes classification algorithms using an extract from CICDDOS2019 dataset. Using the Weka version 3.8.6, the algorithms were compared to choose the best one to classify the data. By using three computers with different resources, the experiments were carried out to determine the performance of those machine learning algorithms. The result revealed that the random forest produced a good average classification performance in resource-limited systems since it surpassed other algorithms in classifying the data at an average of 99 per cent with a low average mean absolute error of 0.0001. Furthermore, as an ensemble that classifies with multiple decision trees algorithm, it likewise uses reasonable time to build and test the model therefore recommended for resource-limited systems.

Through a literature review, it has been observed that water scarcity results from increased demand due to population growth, economic progress, and climate change, leading to disparities between required and available water resources. Addressing this challenge requires segmenting water users into homogeneous groups and thoroughly examining their characteristics regarding water utilization to develop efficient and effective water governance strategies. This study employed data-driven multi-model validation techniques to characterize water users in Pangani Basin in Tanzania. The Kmeans, Agglomerative Hierarchical, and Fuzzy C-means clustering algorithms were used to ascertain the efficacy of the characterization. Cluster validation showed that K-means outperformed Agglomerative hierarchy by owning a high Calinski–Harabasz Index and low Davies–Bouldin Index of 692.3 and 1.8, respectively, compared to Agglomerative hierarchy with values of 578.2 and 1.9, respectively. The clustered dataset was tested for prediction accuracy by fitting the logistic regression. K-means showed a prediction accuracy of 98.2% over 97.5% of the Agglomerative Hierarchical method. The four clusters identified were large-scale irrigation water users, moderate irrigation water users, community water supply entities, and domestic water users. We argue that understanding users’ characteristics could efficiently and effectively add value to water governance along the basins.

Telecommunication towers are radio masts, typically tall structures designed to support antennas for telecommunications and broadcasting. Many telecommunication towers in Habari Node are installed in remote locations, on top of tall buildings, and sometimes on hilltop areas that are not easily accessible. These make them prone to natural hazards, equipment, fuel and battery theft, and electricity faults. In some cases, these issues can cause the malfunction of the aviation obstruction light and fire outbreaks. This challenge affects prompt mitigations during breakdown and the challenges of aviation obstruction light and fire outbreaks. However, technological inputs have been developed to tackle these challenges. However, many of these technologies are associated with low performance due to lack of real-time interventions and auto-report to the systems' concerns, awareness, and inadequate information. Hence, the study used qualitative methods of data collection which led to develop a cost-effective, versatile system that can detect, extinguish, and send early alerts about fire, aviation obstruction light, and electricity power issues. The proposed system was developed to monitor and control the telecommunication tower using ESP32 WROOM-32D as a microcontroller, fire sensor, buzzer, BH1750 ambient light, LDR darkness sensor, relays, Pzem-004t, ThingSpeak cloud, and the global service message module (GSM) to alert all tower’s technicians and firefighters. The results revealed the prompt performance of the system in detecting and extinguishing fire. Also, the it can monitor aviation light for tower safety and turn on the automatic voltage and current regulator (AVCR) during overcurrent or overvoltage. Furthermore, the designed system has the capacity to initiate and send short message service (SMS) and call as an alert to check through mobile and web-based application

In today's modern society, the use of radiation sources in a wide range of activities has increased rapidly. As a result, occupational exposure to ionizing radiation doses that cause health effects increases. Excessive doses of 20 millisieverts (mSv) per year cause acute effects such as sterility or cancer. The health effects of ionizing radiation fall in many countries around the world, including Tanzania. Tanzania Atomic Energy Commission (TAEC) manages dosimeters in order to reduce radiation hazards to radiation workers. Nonetheless, after dispatching those dosimeters, the TAEC management room is unable to determine whether or not each supposed wearer has worn the dosimeter. This originates from an incorrect assessment of an individual occupational radiation dose. Therefore, an Internet of Things-based Monitoring and Reporting System for Dosimeter Wearers in Radiation Areas is developed. Using internet of things (IoT) technology, this project presents an effective and affordable system for real-time remote monitoring and reporting staff wearing passive dosimeters while near or in the area; radiation exposure is too high. The scrum method, which is based on agile methodology, was used for system development. The system was run by an ESP32 microcontroller board that was programmed in C using the Arduino Integrated Development Environment. The ESP 32 microcontroller could send data to the weber server via its built-in Wi-Fi. Through the mapping web application, the end-user (TAEC Managerial Officer) monitored and visualized radiation workers. IoT enabled the dosimeter to be monitored and reported on at any time via the internet.

Information and Communication Technology (ICT) has changed the way we communicate and access information, resulting in the high generation of heterogeneous data. The amount of network traffic generated constantly increases in velocity, veracity, and volume as we enter the era of big data. Network traffic classification and intrusion detection are very important for the early detection and identification of unnecessary network traffic. The Machine Learning (ML) approach has recently entered the center stage in network traffic accurate classification. However, in most cases, it does not apply model hyperparameter optimization. In this study, gradient boosting machine prediction was used with different hyperparameter optimization configurations, such as interaction depth, tree number, learning rate, and sampling. Data were collected through an experimental setup by using the Sophos firewall and Cisco router data loggers. Data analysis was conducted with R software version 4.2.0 with Rstudio Integrated Development Environment. The dataset was split into two partitions, where 70% was used for training the model and 30% for testing. At a learning rate of 0.1, interaction depth of 14, and tree number of 2500, the model estimated the highest performance metrics with an accuracy of 0.93 and R of 0.87 compared to 0.90 and 0.85 before model optimization. The same configuration attained the minimum classification error of 0.07 than 0.10 before model optimization. After model tweaking, a method was developed for achieving improved accuracy, R square, mean decrease in Gini coefficients for more than 8 features, lower classification error, root mean square error, logarithmic loss, and mean square error in the model.

SACCOs are viewed as a feasible opportunity toward financial inclusion in an economy where most of the citizens are poor, as they are very essential for the socio-economic development of members, the community, and the world at large. However, SACCOs sometimes do not realize the expected socio-economic potential, especially when they fail. This study aimed to comprehensively assess financial and non-financial factors, at institutional and personal levels, that contribute to the failure of SACCOS in Tanzania. The data were collected using a questionnaire on 5,000 members of SACCOs, obtained using stratified random sampling. Data collected were analyzed using descriptive statistics and binary logistic regression. The findings showed that both financial and non-financial factors, at personal and institutional levels, had a statistically significant and positive relationship with the failure of SACCOs. Therefore, the performance of SACCOs and other Microfinance Financial Institutions (MFIs) should be addressed from a comprehensive view of both financial and non-financial factors, at personal or institutional levels. In other words, the failure of MFIs should be addressed from a holistic point of view.

Deep learning-based driver assistance systems (ADAS) have attracted interest from researchers due to their impact on improving vehicle safety and reducing road traffic accidents. In Uganda, road accidents have continued to soar with an increase of up to 42% in 2021 due to the growing road traffic density. To curb the high rates of road accidents, especially for heavy-duty vehicles, Kiira Motors Corporation a state-owned mobility solutions enterprise needs advanced driver assistance systems for improved safety of their market entry products- the Kayoola buses. This research presents an approach to vehicular safety enhancement through the integration of Lane Departure Warning (LDW) and Blind Spot Detection systems (BSD) using advanced deep learning algorithms. The resultant LDW and BSD system is realized on the Raspberry Pi platform, incorporating diverse sensors. By combining these advanced features, the study not only bridges an essential research void but also offers a practical resolution to pressing road safety concerns in the East African context. The integration of LDW and BSD systems through deep learning techniques marks a pivotal advancement in vehicular safety. The lane detection model was tested on DET and TuSimple datasets. Our model attained a mean F1 Score of 77.59% and a mean IoU of 65.26% on the Dataset for Lane Extraction (DET) and an overall accuracy of 97.96% on the TuSimple dataset. Our work presents an integrated lane departure warning and blind spot detection system that will be able to alert the driver using the graphical user interface, and auditory feedback. The anticipated real-world implementation is poised to substantiate the system’s effectiveness, thereby contributing to safer roads regionally and inspiring innovation in automotive engineering by leveraging artificial intelligence.

Modern technology drives the world, increasing performance while reducing labour and time expenses. Tanzania Atomic Energy Commission (TAEC) tracks employees’ levels of exposure to radiation sources using dosimeters. According to legal compliance, workers wear dosimeters for three months and one month at the workplace. However, TAEC has problems in tracking, issuing, and returning dosimeters because the existing tracking is done manually. The study intended to develop a Personal Dose Management System (PDMS) that processes and manages the data collected by dosimeters for easy and accurate records. During the requirements elicitation process, the study looked at the existing system. PDMS’ requirement gathering included document reviews, user interviews, and focused group discussions. Development and testing of the system were implemented by applying the evolutionary prototyping technique. The system provides a login interface for system administrators, radiation officers, and Occupational Exposed Workers. The PDMS grants TAEC Staff access to monitor individual exposed workers, prints individual and institutional reports and manages workers' information. The system reminds the users when to return dosimeters to TAEC, generates reports, and facilitates dispatching and receiving dosimeters effectively. PDMS increases efficiency and effectiveness while minimizing workload, paperwork, and inaccurate records. Although the existing systems are beneficial to their respective countries, they are designed based on the specific institution. The system developed simplifies the procedures for requesting dosimeters, reminding users when to return the dosimeter, and printing quarterly and annual reports for individuals and institutions. Therefore, based on the results obtained from the system, it is recommended to use the system to improve dosimeter data management at the institution.