Introduction Diagnosis is a key step towards the provision of medical intervention and saving lives. However, in low- and middle-income countries, diagnostic services are mainly centralized in large cities and are costly. Point of care (POC) diagnostic technologies have been developed to fill the diagnostic gap for remote areas. The linkage of POC testing onto smartphones has leveraged the ever-expanding coverage of mobile phones to enhance health services in low- and middle-income countries. Tanzania, like most other middle-income countries, is poised to adopt and deploy the use of mobile phone-enabled diagnostic devices. However, there is limited information on the situation on the ground with regard to readiness and capabilities of the veterinary and medical professionals to make use of this technology. Methods In this study we survey awareness, digital literacy and prevalent health condition to focus on in Tanzania to guide development and future implementation of mobile phoned-enable diagnostic tools by veterinary and medical professionals. Data was collected using semi-structured questionnaire with closed and open-ended questions, guided in-depth interviews and focus group discussion administered to the participants after informed consent was obtained. Results A total of 305 participants from six regions of Tanzania were recruited in the study. The distribution of participants across the six regions was as follows: Kilimanjaro (37), Arusha (31), Tabora (68), Dodoma (61), Mwanza (58), and Iringa (50). Our analysis reveals that only 48.2% (126/255) of participants demonstrated significant awareness of mobile phone-enabled diagnostics. This awareness varies significantly across age groups, professions and geographical locations. Interestingly, while 97.4% of participants own and can operate a smartphone, 62% have never utilized their smartphones for health services, including disease diagnosis. Regarding prevalent health condition to focus on when developing mobile phone -enabled diagnostics tools for Tanzania; there was disparity between medical and veterinary professionals. For medical professionals the top 4 priority diseases were Malaria, Urinary Tract Infections, HIV and Diabetes, while for veterinary professionals they were Brucellosis, Anthrax, Newcastle disease and Rabies. Discussion Despite the widespread ownership of smartphones among healthcare providers (both human and animal), only a small proportion have utilized these devices for healthcare practices, with none reported for diagnostic purposes. This limited utilization may be attributed to factors such as a lack of awareness, absence of policy guidelines, limited promotion, challenges related to mobile data connectivity, and adherence to cultural practices. Conclusion The majority of medical and veterinary professionals in Tanzania possess the necessary digital literacy to utilize mobile phone-enabled diagnostics and demonstrate readiness to adopt digital technologies and innovations to enhance diagnosis. However, effective implementation will require targeted training and interventions to empower them to effectively apply such innovations for disease diagnosis and other healthcare applications.

Malnutrition tends to be one of the most important reasons for child mortality in Tanzania and other developing countries, in most cases during the first five years of life. This research was conducted todevelop machine learning model for predicting fetal nutritional status. Several machine learning techniques such as AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, Decision Tree, K-nearest neighbor and Stochastic Gradient Descent, were used to categorize the children in the test dataset as "malnourished" or "nourished". The accuracy, sensitivity, and specificity of these algorithms' prediction abilities were comparedusing performance measures such as accuracy, sensitivity, and specificity. Results show that malnutrition status can be predicted using Random Forest machine learning technique which was about 98% and brings positive impact to the society. The study findings indicated a need for more attention on nutrition to expected mothers and children under five to be well administered with the government and the society at large by putting relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for the future society.

Common bean plays a crucial role in the agricultural sector in Tanzania. To most smallholder farmers, the crop serves as a principal source of protein and an essential source of income. Despite its significance, common bean production is often affected by diseases, particularly bean rust and bean anthracnose, resulting in low yields and diminished economic returns. To address this challenge, a comprehensive dataset of common bean leaf images has been collected by using smartphone cameras to capture the visual characteristics of healthy and diseased leaves. The dataset contains more than 59072 labeled images, offering a valuable resource for developing machine learning models and user-friendly tools capable of early detection and diagnosis of bean rust and bean anthracnose diseases. The aim of generating this dataset is to facilitate the development of machine learning tools that will empower agricultural extension officers, smallholder farmers, and other stakeholders in agriculture to promptly identify and diagnose affected crops, enabling timely and effective interventions before causing significant economic loss. By equipping farmers with the knowledge and tools to combat these diseases, we can safeguard bean production, enhance food security, and strengthen the economic well-being of smallholder farmers in Tanzania and other parts of Africa.

The second annual ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO'22) was held from October 6-9 at George Mason University in Arlington, VA, USA. This was the first in-person version of the conference: the first event was held virtually in 2021. The conference builds on a line of workshops on Mechanism Design for Social Good (MD4SG) held previously at the ACM Conference on Economics and Computation, and is affiliated with the broader MD4SG initiative. In both 2022 and 2021, SIGecom was a sponsor of the conference. EAAMO aims to highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from the social sciences and humanistic studies, can help improve equity and access to opportunity for historically disadvantaged and underserved communities. A key goal of the conference is to bridge research and practice in this area. Accordingly, we aimed to foster an interdisciplinary community, including researchers from a computer science, operations research, economics, policy, and more, as well as practitioners and policymakers working in areas related to inequality. The conference featured contributed papers and posters as well invited keynote talks, a panel discussion, a doctoral consortium, and community-building activities and social events.

Quality of Maternal, Neonatal and Child (MNCH) care is an important aspect in ensuring healthy outcomes and survival of mothers and children. To maintain quality in health services provided, organizations and other stakeholders in maternal and child health recommend regular quality measurement. Quality indicators are the key components in the quality measurement process. However, the literature shows neither an indicator selection process nor a set of quality indicators for quality measurement that is universally accepted. The lack of a universally accepted quality indicator selection process and set of quality indicators results in the establishment of a variety of quality indicator selection processes and several sets of quality indicators whenever the need for quality measurement arises. This adds extra processes that render quality measurement process. This study, therefore, aims to establish a set of quality indicators from a broad set of quality indicators recommended by the World Health Organization (WHO). The study deployed a machine learning technique, specifically a random forest classifier to select important indicators for quality measurement. Twenty-nine indicators were identified as important features and among those, eight indicators namely maternal mortality ratio, still-birth rate, delivery at a health facility, deliveries assisted by skilled attendants, proportional breach delivery, normal delivery rate, born before arrival rate and antenatal care visit coverage were identified to be the most important indicators for quality measurement.

The fisheries sub-sector in Tanzania is challenged with limited use of Information and Communication Technology (ICT) for information gathering and dissemination. Fishers obtain fisheries information from extension officers and their fellow fishers through mainly word of mouth in physical meetings. Despite the growth in access and availability of ICT channels on Mobile phones and Internet in recent years, the fisheries sub-sector decision-makers mainly use conventional media (radio, television, personal communications) in gathering and disseminating fisheries information. Understanding the characteristics of communication channels and their effectiveness in fisheries information gathering and dissemination is of great importance. A comprehensive comparison of the six ICT channels (short message services, cellular phone call, television, radio, mobile application, and website) was done in this study using effectiveness probability. The findings from this study indicated that, short message service (SMS) and cellular phone calls are most effective for fishers. Mobile application, cellular phone calls, websites, and SMS are effective for fish traders and fisheries officers. However, the cellular phone call was not cost-effective compared to mobile applications and websites. This study recommends the development of multi-channel (SMS, web-based, and mobile application) fisheries information system to enhance fisheries information gathering and dissemination process to meet realistic information needs of all fisheries stakeholders.

Poultry health is imperative for the continued growth of poultry and increased production. Environmental conditions such as temperature, humidity, and ammonia gas have an impact on poultry health. They affect the respiratory system and eventually cause death. In Tanzania, most smallholder farmers use charcoal and kerosene stoves to control the environmental parameters since they have limited access to low-cost, secure, and user-friendly poultry house monitoring systems. However, these traditional methods are unreliable, difficult to manage, not environmentally friendly, and inaccurate. Data were collected from 120 poultry farmers in Arusha and Kilimanjaro regions using convenience and snowball sampling techniques. Of the respondents, 43% revealed that smallholder farmers do not adopt automated systems despite being available because they are expensive. In this study, a system based on the Internet of Things (IoT) was developed for environmental conditions monitoring in poultry houses for low-resourced smallholder farmers in Tanzania. The system saves time (84%) and labour costs (66.7%) compared to the traditional system, as the farmer can monitor and control the conditions securely, reliably, and remotely. The study also proposes an algorithm for the system to work online and offline (i.e.,, synchronizing with the cloud server when internet access is available).

Coccidiosis, Salmonella, and Newcastle are the common poultry diseases that curtail poultry production if they are not detected early. In Tanzania, these diseases are not detected early due to limited access to agricultural support services by poultry farmers. Deep learning techniques have the potential for early diagnosis of these poultry diseases. In this study, a deep Convolutional Neural Network (CNN) model was developed to diagnose poultry diseases by classifying healthy and unhealthy fecal images. Unhealthy fecal images may be symptomatic of Coccidiosis, Salmonella, and Newcastle diseases. We collected 1,255 laboratory-labeled fecal images and fecal samples used in Polymerase Chain Reaction diagnostics to annotate the laboratory-labeled fecal images. We took 6,812 poultry fecal photos using an Open Data Kit. Agricultural support experts annotated the farm-labeled fecal images. Then we used a baseline CNN model, VGG16, InceptionV3, MobileNetV2, and Xception models. We trained models using farm and laboratory-labeled fecal images and then fine-tuned them. The test set used farm-labeled images. The test accuracies results without fine-tuning were 83.06% for the baseline CNN, 85.85% for VGG16, 94.79% for InceptionV3, 87.46% for MobileNetV2, and 88.27% for Xception. Finetuning while freezing the batch normalization layer improved model accuracies, resulting in 95.01% for VGG16, 95.45% for InceptionV3, 98.02% for MobileNetV2, and 98.24% for Xception, with F1 scores for all classifiers above 75% in all four classes. Given the lighter weight of the trained MobileNetV2 and its better ability to generalize, we recommend deploying this model for the early detection of poultry diseases at the farm level.

Background: The high maternal and neonatal mortality in developing countries is frequently linked to inadequacies in the quality of maternal, neonatal, and child health (MNCH) services provided. Quality measurement is among the recommended strategies for quality improvement in MNCH care. Consequently, developing countries require a novel quality measurement approach that can routinely facilitate the measurement and reporting of MNCH care quality. An effective quality measurement approach can enhance quality measurement and improve the quality of MNCH care. This study intends to explore the effectiveness of approaches available for MNCH quality measurement in developing countries. The study further proposes a machine learning-based approach for MNCH quality measurement. Method: A comprehensive literature search from Pub Med, HINARI, ARDI, and Google Scholar electronic databases was conducted. Also, a search for organizations' websites, including World Health Organization (WHO), USAID's MEASURE Evaluation Project, Engender Health, and Family Planning 2020 (FP2020), was included. A search from databases yielded 324 articles, 32 of which met inclusion criteria. Extracted articles were synthesized and presented. Findings: The majority of quality measurement approaches are manual and paper-based. Therefore are laborious, time-consuming and prone to human errors. Also, it was observed that most approaches are costly since they require trained data collectors and special data sets for quality measurement. It is further noticed that the complexity of the quality measurement process and extra funds needed to facilitate data collection for quality measurement puts an extra burden on developing countries that always face constraints in health budgets. The study further proposes a machine learning-based approach for measuring MNCH quality. In developing this model, financial and human resource constrain were considered. Conclusion: The study found a variety of quality assessment approaches available for quality assessment on MNCH in developing countries. However, the majority of the existing approaches are relatively ineffective. Measuring MNCH quality by a machine learning-based approach could be advantageous and establish a much larger evidence base for MNCH health policies for Tanzania.

High maternal and child deaths in developing countries are frequently linked to poor health services provided to pregnant women and children. To improve the quality of maternal, neonatal and child health (MNCH) services, the government and other stakeholders in MNCH emphasize the importance of quality assessment. However, effective quality assessment approaches are mostly lacking in most developing countries, particularly in Tanzania. This study, therefore, aimed at developing a quality assessment approach that can effectively assess and report on the quality of MNCH services. Due to the need for a good quality assessment approach that suits a resource-constrained environment, machine learning-based approach was proposed and developed. K-means algorithm was used to develop a clustering model that groups MNCH data and performs cluster summarization to discover the knowledge portrayed in each group on the quality of MNCH services. Results confirmed the clustering model’s ability to assign the data points into appropriate clusters; cluster analysis with the collaboration of MNCH experts successfully discovered insights on the quality of services portrayed by each group.

Student dropout is among the challenges that face most schools in developing countries particularly in Africa. In addressing the student dropout problem, a thorough understanding of the fundamental causative factors is essential. Several researchers have identified and proposed causes, methods and strategies that will help to reduce or stop the student dropout problem, however, most of the proposed solutions did not show promising results and the dropout trend continue to increase over time. Machine learning on the other hand has gained much attention when addressing society’s problems in different sectors including education. This is attributed by the fact that, machine learning models when accurately trained, provide convenient and reliable results as compared to the traditional approaches. This study focused on developing a machine learning model that will help to predict and identify students who are at risk of dropping out of school. Three datasets from Tanzania, Kenya and Uganda were used to develop the model and disclose the best classifier from the three commonly used i.e. Multilayer Perceptron, Logistic Regression and Random Forest. Classifiers were evaluated using Geometric Mean and F-measure to examine their performance. Results revealed that, Logistic Regression achieved the highest performance as compared to the other two. The study, therefore, recommends the developed model to be used by relevant authorities in identifying and predicting students who are at risk of dropping out of schools, and make informative decisions on addressing the student dropout problem.

In Tanzania, many poultry farms are considered to have ineffective poultry management practices mainly due to the lack of adequate systems and procedures to assist poultry farmers in making decisions. However, in a range of industries and agricultural sectors, including poultry farming, information is widely recognized as a crucial component for good decision-making. Furthermore, many researchers agree that using mobile decision-support systems to assist farmers in making better decisions is an effective technique. The goal of this research was to create a mobile-based decision support system that will assist small-scale poultry farmers in Tanzania in obtaining trustworthy poultry farming information that would enable them to make informed decisions about their farming operations. Decision Support Systems (DSS) are well-known in this context as interactive computer based systems that assist individuals in problem-solving and decision-making using information technology, data, documents, and knowledge. This study outlines how a data-driven strategy was utilized to construct a decision-support system for Tanzanian poultry farmers. The systematic approach used in this study comprised of the following steps: The first step was to perform a thorough literature analysis to identify and assess the information management needs of Tanzanian small-scale poultry farmers. Poultry farmers commonly seek information about chicken health, housing, egg production, chicken diets, and chicken breeds, among other things. Second, the gathered data was processed and used to create user requirements for the decision support system. Finally, after determining user needs, the implementation of the mobile-based decision support system began. Using Android Studio and RASA, an open-source machine learning framework for developing chat and voice context assistants, a conversational, mobile-based decision support application that offers information to farmers based on their needs through a text-based chat conversation was developed. The study findings identified that majority of the small-scale poultry lack reliable sources to obtain poultry management information like poultry diseases, poultry feeds, housing, and breed types. Furthermore, after conducting a user acceptance test, the study findings indicated that the developed system will be very helpful to the small-scale poultry farmers, and therefore it was recommended that the extension officers and small-scale poultry farmers should be made aware of the developed mobile-based decision support system (KaPU) for productive poultry management practices.

The dataset of poultry disease diagnostics was annotated using Polymerase Chain Reaction (PCR). Polymerase Chain Reaction (PCR) is a molecular biology technique for rapid diagnostics. We gathered both the fecal images and fecal samples from layers, cross and indigenous breeds of chicken from poultry farms in Arusha and Kilimanjaro regions in Tanzania between September 2020 and February 2021. Each fecal sample collected was coded to its corresponding image during data collection. PCR method is used for detection and identification of pathogens through amplification of DNA sequences unique to the pathogen. We used existing primers from literature to amplify the target DNA/RNA on the poultry fecal samples for PCR. The targets were Coccidiosis, Newcastle disease and Salmonella. We used the primers for PCR diagnostics at the molecular laboratory of the Nelson Mandela African Institution of Science and Technology (NM-AIST). The fecal samples were stored at -80 degrees celsius. The PCR diagnostics were conducted using reagents and kits from Zymo Research and the protocol is summarized in these five stages: 1. DNA sample loading 2. DNA extraction 3. Amplification; 4. Quantification and 5. Detection. All the PCR annotated fecal images are in the .zip files; “pcrcocci.zip” has 373 images, “pcrhealthy.zip” has 347 images, “pcrsalmo.zip” has 349 images, "pcrncd.zip" has 186 images. A total of 1,255 image files are labeled. The research project is funded by the Organization for Women in Science for the Developing World (OWSD) with Grant Award Number: 4500406715.

Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses.

The advancement in technology has positively impacted human department working conditions in the processing industries domain that require many employees to accomplish certain tasks. For reliable management of employee’s attendance, leave and payroll which are critical operations in the human resource department of every institution, the use of the latest technology includes high-level language PHP, JQUERY, BOOTSTRAP, HTML, CSS, MYSQL, TCPDF, and Xamp server have been used to efficiently store and retrieve data, process data, generate reports. To accomplish all these tasks effectively, a computer web-based application has been designed, developed, and deployed as well. It keeps attendance and leave records for every employee, and generates monthly payroll to be deposited at the employee bank account after calculating allowances, deductions, and taxes. Individual pay slip is generated out as receipt. Compared to manual or excel sheet methods for processing attendance, leave and payroll, this system processes data accurately at the level of 100%. The developed software was tested for any error and it is suitable to use in any industry.

Document digitization is the process of converting information into a digital format, that is the computer-readable format. Traditionally way of handling documents is challenging and has a lot of disadvantages for people. Due to the increase of the number of people using the internet and smartphone in Tanzania where the number of mobile subscribers in Tanzania has raised to 42% of the population subscribing to mobile service in 2018, this makes it easy for people to adapt to the technology. Famous digital scanner technology most have limitations which include digital advertisement, also have limited features which require to pay the extra money and have watermarks which reduce the sense of ownership of the document. To create a competitive advantage we have used experimental procedure to find the validity of our application and through observation of other document digitization application to get valid primary data. Despite the presence of other document digitization applications, most features are limited, paid, and possess advertisement which may cause discomfort to users. There are different kinds of application developed in android phones which help people digitize their documents easily. In this project work, a mobile application is developed for digital scanning and converting the documents in PDF format, extracting text from the images, wireless printing ability, and sharing the documents into social media. Through this application, users will experience a lightweight document digitization application compared to other applications, a resource utilized application, and enhanced limited functionalities from other applications.

Kiira Motors Corporation seeks to avail customer satisfaction, by providing noteworthy passenger experience on its market entry product, the Kayoola EVs bus through deploying a passenger security and safety system to curtail rampant snags like passenger insecurity, loss of passenger property, shortcomings in management and accountability as well as the spread of contagious sicknesses like COVID-19 which are not alien occurrences on commuter taxis and buses in African cities. On this project, a comprehensive system was designed for remote CCTV video surveillance, video analysis for people detection, passenger count and social distance analysis, as well as digital contact tracing to solve the challenges. It denotes significant potential to improve the security of property and passengers, shrink the risk of the spread of contagious diseases, enable timely capture of contact tracing records and lessen the burden of management, monitoring and accountability for the numbers of passengers on buses for fleet owners.

With technology advancement, the application of technology in conglomerate companies is crucial for company performance.Technology utilization in industry in developing countries is a challenge. Often there is a crisis of equipment, goods, and items destruction or loss in warehouses. The enhancement of the warehouse management system for the company helps to utilize resources effectively, thus improving company performance.This study aimed to enhance the management of warehouses through the use of information communication and technology. The study developed mobile applications for customer registration, order management, and stock management. The study also extended the web applications for account management, order management, invoice generation, client registration, and stock management. The study was conducted at AtoZ Textiles Company Limited located in the Kisongo area, Arusha Region.

Sanger sequencing remains the cornerstone method for Deoxyribonucleic Acid (DNA) sequencing due to its high accuracy in targeting smaller genomic regions in a larger number of samples. The analysis of Sanger sequence DNA data requires powerful and intelligent software tools. Most of the preferred tools are proprietary licensed tools that offer a user-friendly interface and have many features, however, their affordability, especially to individual scientists or students, is limited. On the other hand, a few free and open-source licensed tools are available but have limited features. This study focuses on the usability testing of the developed Sanger Sequence Automatic Analysis Tool (SSAAT), a free and open-source web tool for Sanger sequence analysis. Usability tests were conducted with potential users and the results demonstrate that the participants were able to use the tool easily and accomplish the test tasks at the given time. Moreover, the participants were excited with the easy-to-use interface and agreed that most users could use the tool with no need for technical assistance. However, the participants also identified some issues that require more development effort.