26 research outputs found
Role of a 24-hour Ambulatory Internet of Things System in Preeclampsia Monitoring: Technologies, Challenges, and Future Path Survey
No full textThe Internet of Things (IoT) is a technology that integrates different sensor actuators, working together for data management towards efficient communication within the digital world. IoT has been applied in many sectors to achieve sustainable development goals. Massive devices and a huge amount of data have been the major components of the technology, which has presented new challenges. IoT has been applied in healthcare to improve several ways of managing health, including antenatal care. Worldwide, the cost of having preeclampsia monitoring has been a major concern. A 24-hour ambulatory IoT system, an integration of a smartwatch, a mobile device, and a cloud-based application, is one of the technologies used to help in preeclampsia monitoring. IoT and its functionalities have been evaluated in previous studies and assessments. However, they concentrated on its application in other areas, such as animal husbandry, and little on ambulatory care. The impact of a real-time ambulatory IoT system on preeclampsia monitoring are comprehensively and methodically examined in this paper, focusing on three categories: the challenges and its benefits in ambulatory care. The application’s effects, performance, and safety have been thoroughly described. Generally, this paper explores potential initiatives of the IoT system to address existing ambulatory care issues
A case of MP shah hospital, Nairobi.
No full textStrategic planning practices allow improvement of organizations performance by establishing guidelines in form of clear vision, mission statements and performance expectations including performance indicators. The study focused on the leadership and governance pillar of health system building block. The main objective of the study was to determine challenges of implementing strategic planning practices adopted by MP Shah Hospital to achieve its performance, with the specific objectives being how communication, staff participation and capacity building influence implementation of strategic planning practices in MP Shah Hospital. This was a descriptive case study limited to MP Shah Hospital with target population being key professional employees; sample of 139 out of 349 key professionals was used to collect both qualitative and quantitative data using structured questionnaires for key professional employees and key informant interview guide for the unit managers. Data was analysed using both descriptive and inferential statistics. The study took advantage of computer software SPSS for data analysis, correlation analysis determined the significance relationship between independent variables (communication, staff participation and capacity building) and dependent variable (organizational performance), Pearson product indicated a positive correlation. Regression analysis indicated r = 0.736 that changes in the dependent variable(organizational performance) were influenced by changes in independent variable(communication, staff participation and capacity building) by 73.6%, while other factors not investigated in the study contributed to 26.4% of organizational performance. The study found out that 91.3% were aware of strategic planning practices in MP Shah Hospital, while 51.4% agreed it was highly useful in determining performance. From the findings only 62% are involved in the implementation process, this was majorly affected by communication whereby majority felt communication was not timely (64%), support supervision was lacking since majority 69.6% did not know if it exists. The study also found out that MP Shah was performing above average in clinical performance, and customer retention. It was also noted that they needed to improve on developing effective strategies and establishing priorities in order to improve its performance. The study therefore recommended that strategic planning practices be emphasized in terms of capacity building in order to improve performance of health facilitie
Embracing counseling and psychotherapy in Kenya
No full textThis paper looks at the status of mental health in Kenya with specific focus on counseling and psychotherapy. It looks at the history of counseling and psychotherapy in Kenya, counselor educations programs, accreditation, licensure and certification, current counseling and psychotherapy theories, processes and trends, and research and supervision. Its purpose is to examine how counseling and psychotherapy is developing in Kenya. It explores how Western methods of psychotherapy are being incorporated in treatment of individuals with mental illness. It also looks at possible ways in which traditional methods of healing can be incorporated into the treatment of mental illness. This study is a literature review of recently published works by various professionals involved in mental health research and training in Kenya. The counseling profession in Kenya is in its formative years, but a lot of research and training is being implemented to meet the mental health needs of Kenyans
ASSESSMENT FRAMEWORK FOR INTERNET BANKING SYSTEM RELIABILITY IN KENYA
No full textINTERNET BANKING SYSTEM RELIABILIT
INTERNET OF THINGS BASED MODEL FOR PREECLAMPSIA MONITORING IN ANTENATAL CARE
No full textFULL TEXTIn the health sector, the health of women is a significant public health issue, which impacts the personal well-being, family reproduction, and societal development. Therefore, knowledge about the health of women has led to an emerging requirement for healthcare sectors to obtain the real-time status and data of various applications that can improve the performance and accuracy of the health production. Globally, it has been found that women die due to pregnancy and childbirth consequences. The major effects of maternal morbidity and mortality include haemorrhage, infection, high blood pressure, unsafe abortion, and obstructed labour. Some of the maternal challenges that cause long term effects when not controlled include preeclampsia, which is caused by hypertension, one of the leading identifiable risk factors in pregnancy. Hypertension also results in stillbirth, oedema, and even death. Hospitals in developing countries have been using several devices in the detection of blood pressure fluctuations, though not reading real-time data. This study, therefore, sought to implement an Internet of Things (IoT) based model for preeclampsia monitoring in antenatal care. To achieve this overall objective, the study identified suitable smart armband for measuring blood pressure based on functionality, hardware, software, and affordability. In addition, the study sought to develop IoT model and implement it to read pregnant mother’s real-time data that can be accessed by a Health care provider and family caregiver in case of an emergency. The developed IoT prototype was tested with fifty pregnant mothers who were selected using purposive and simple random sampling. The sample size selection was done using Cochran formula. The study was undertaken using mixed research design that involved exploratory, rapid prototyping approach and a quasi-experimental research designs. The respondents were selected from Thika level 5 hospital and Embu Level 5 hospital in Kiambu and Embu counties respectively. The study used consistency, response rate, accuracy, reliability, and output as metrics to evaluate IoT system performance. The T-test was used to determine the significance of performance metrics. The study found out that the IoT based model for preeclampsia monitoring was feasible and practical during the testing and also performed as expected during its evaluation. Based on the findings, the study recommends the approach to be scaled up and adopted in maternal health care to address preeclampsia conditions while addressing issues of cost in its adoption. In addition, the study recommends fabrication of suitable smart armband for measuring blood pressure in pregnant mothers
Using analytical CRM system to reduce churn in the telecom sector: A macine learning approach
No full textApplied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2019Customers are considered to be the most valuable assets of any business, and thus their loyalty
is key to profitability as they indulge in repeat purchases and attract their colleagues through
word-of-mouth. In competitive markets such as telecommunications, customers have a lot of
flexibility due to the variety of service providers available and the introduction of mobile
number portability (MNP) thus they can easily switch services and service providers. Customer
churn is, therefore, a major problem among telecommunication companies hence their quest to
reduce customer churn rate and retain an existing customer. Customer relationship management
systems have been used over the years to track patterns within the customer data, but this could
be improved notably with the technological advances hitting the universe on a daily basis. We
have moved past the age of innovations around steam engines, electricity, computers, mobile,
internet to the current technology trends in artificial intelligence and big data. We are at the
cusp of a new wave where enterprises have embraced the application of machine learning in
streamlining different business processes. Telecom companies have the advantage of mining
large customer datasets that can be leveraged on for predictive analysis using data science.
This project explores the use of analytical CRM system in reducing customer churn in the
telecom industry using machine learning algorithms to predict customer behavior in order to
retain them. Its goal is to analyze all relevant customer data and develop focused customer
retention programs. This is on the focus that if you could somehow predict in advance which
customers are at risk of leaving, you could develop focused customer retention programs to
reduce customer churn.Ashesi Universit
Holding Corporate Social Responsibility to account: its applicability in tourism development
No full textTourism was introduced in Elmina and Cape Coast, (Ghana, Africa) home to three World Heritage Sites (slave dungeons during transatlantic slave trade) as a means to poverty reduction. However, almost fifteen years later, this was not achieved. A participatory approach to research revealed that lack of Corporate Social Responsibility (CSR) from the government, tourism intermediaries and developmental institutions was the key factor behind this failure. So far within the tourism industry no tangible areas of responsibility for sustainable tourism development were found and not even the host governments expressed concern for it. However, it could be argued that the intelligent application of [C (SR)] can lead to poverty reduction if it is practiced in a holistic, responsible, transparent and accountable manner
Corporate Social Responsibility: An Application in Tourism Development in Ghana
No full textIn 1972 UNESCO recognized 1) the Elmina Castle alias St George’s Castle located in Elmina, 2) the Fort St Jago located in Elmina, and 3) the Cape Coast Castle alias Carolsburg Castle located in Cape Coast as World Heritage Sites (slave dungeons during transatlantic slave trade). Tourism was introduced in Elmina and Cape Coast in Ghana, West Africa, as a means to poverty reduction. However, almost fifteen years later this was not achieved. A participatory approach to research revealed that lack of Corporate Social Responsibility (CSR) from the government, tourism intermediaries and developmental institutions was the key factor behind this failure. So far within the tourism industry no tangible areas of responsibility for sustainable tourism development were found and not even the host governments expressed concern for it. However, it could be argued that the intelligent application of [C (SR)] can lead to poverty reduction if it is practiced in a holistic, responsible, transparent and accountable manner
Content Based Approach for Detecting Smishing Messages in Mobile Phones Using an Improved Convolutional Neural Networks Model
No full textSMS stands for Short Message Service (SMS). Short messaging service is a text messaging service where a user can send short messages via a mobile device. Short message service has evolved and become very popular as a communication medium in the last decade. It has become a more effective mode of communication compared to email. Unfortunately, smishing (SMS phishing) has emerged as the most common type of spam because traditional detection methods have difficulty understanding the informal nature of these messages. An improved class of CNN-based models targeted at accurate detection of smishing on mobile devices was developed. Deep learning theory was used in this work. (UCI) refers to University of California, Irvine (UCI). The UCI Machine Learning Repository contains datasets, domain theories, and data generators used by the machine learning community to empirically study machine learning algorithms. In this study, a research design was carried out and samples of the UCI Machine Learning Repository were used to build an experimental model. The analyzed dataset was a set of 5, 574 SMS messages from both spam and non-spam messages. The performance metrics used were Precision, Recall, F1-Score and Accuracy. The CNN model used for evaluation, had a bigger number of hidden layers for better detection. A higher accuracy of 99. 95% was achieved, indicating good performance and better detection of the SMS spams (SMS phishing). In the analysis mentioned in the text, the text preprocessing greatly contributes to improved detection accuracy and CNN outperforms the traditional detection methods. It shows that the sophisticated nature of smishing attacks make it necessary for advanced detection mechanisms to be applied to prevent future SMS threats. Although some authorities for implementation should allocate resources to implement these solutions, other authorities must define roles for different detection systems in order to realize the ideal and continuous performance of the detection tools. However, various authorities should also continuously enhance detection mechanisms by feature enhancement, data augmentation, and regular performance evaluation. The training for the staff and establishment of the performance benchmarks needs to be implemented. The users should also contribute with their comments, reports suspicious message, and raising awareness for each other, while all other stakeholders should make available their expertise and resources to support this work
Method level static source code analysis on behavioral change impact analysis in software regression testing
No full textThough a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons of software modifications. In assessing the impact of the changes made in software, static source code analysis plays a key role. However, static source code analysis can be a little complex depending on the reason for the expedition. Despite the work done so far, little focus has been made on the potential of changed methods analysis during static source code analysis in assessing the impact of the changes made in a software system. We propose and investigate a static source code analysis technique that would generate information on the modified methods in the source code. This study analyzes four aThough a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made.Java projects and shows that an analysis of the changed methods reveals the level of regression testing that ought to be conducted for the changes made
