MSIT Theses and Dissertations (2017)

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A Public key infrastructure based model for verification of title deeds in Kenya
(Strathmore University, 2017) Kariuki, Fanon Kimani
The Kenya lands administration process faces many challenges arising from the use of a paper based system. Over the years, this system has come under scrutiny because of inconsistencies, irregularities and the bulk of physical records that when tampered with undermine the integrity of records kept. This problem makes it difficult to quickly and accurately verify the validity of any given title deed and has also caused an influx of counterfeit title deeds which throws ownership of land into dispute. In order to verify the authenticity of a title deed, one has to fill out forms and pay a fee at the lands offices, and then go through a slow and inefficient process due to the voluminous records associated with the current paper based system. Despite digitization efforts in 2014 aimed at streamlining service provision, title deed verification still remains a critical challenge. Financial institutions are therefore reluctant to issue loans with land as collateral because it is a slow and often inaccurate process to ascertain true ownership of land, and this may lead to loss of potential customers. Based on the challenges of identifying true land ownership by authenticating title deeds, this study will adopt the Public Key Infrastructure (PKI) model which incorporates the use of a trusted third party (Certification Authority) to create digital certificates. The Nairobi Lands registry will act as the Certification Authority in this study, within which there will also be a registration function (Registration Authority or RA) and a key generator to generate a public-private key pair. The RA will confirm title land owners' details, and the CA will issue digitally signed certificates, which will be used by financial institutions to compare to physical title deeds for discrepancies, and thus authenticate them. The PKI model's ability to provide digital certificate validation, time stamping, data confidentiality and authenticity makes it a suitable choice to eliminate the authentication related challenges experienced currently. This study aims to develop a prototype using a waterfall software development model to validate the proposed title deed verification solution. The prototype will be tested to validate its accuracy and efficiency in authenticating sample title deeds.
A Mobile application for HIV education and stigma level measure: a case of Nairobi
(Strathmore University, 2017) Thumbi, Cameline Mukami
The world wants to reduce HIV and AIDS spread by the year 2030 as part of the sustainable development goals (SGDs). HIV and AIDS has become the world’s most devastating epidemic especially in developing countries like Kenya. Many people have died because of HIV and AIDS related illnesses since it was first reported in Kenya in 1984. To be able to achieve the sustainable development goals, HIV education and stigma reduction would be essential. The problem being studied is people living with HIV not being able to access service due to discrimination and stigma. HIV education and awareness programs have been implemented by various governmental and private bodies for which have improved the HIV prevalence rate. While the prevalence rate has improved, more needs to be done to advance access to the right information and reduce the stigma associated with HIV. The general objective of this research is to develop a mobile based application for PLHIV that will assist in information access as a means of education. The stigma is expected to go down by using anti-stigma messages and tagging of the same. The secondary objective is to reduce stigma associated with HIV through education on HIV and anti –stigma messages geo tagging. Among the main reasons for not seeking treatment include discrimination, stigma, wrong information, lack of support and drugs. Data collection involved both primary and secondary methods which included use of questionnaire, observation and literature review. The data was analysed using thematic analysis. This research sought to find how best to increase access to the right information as a means of education on HIV through use of the mobile phone. The software development life cycle was used for the development of the application. An android based mobile application was developed as a proof of concept for access to information on HIV care and geo tagging of anti-stigma messages.
A Prototype for project selection: a case of Uwezo fund in Kenya
(Strathmore University, 2017) Karanja, Anthony Ng’ang’a
Selection of the right project is very crucial in any institution as it enables it to select high priority projects aligned with their strategies. Stakeholders evaluate each project idea and select projects of the highest priority. Despites overwhelming evidence of project selection techniques superiority in selecting projects that yield higher success results, many organization do not apply them to select projects. Poor project selection reduces the benefits and outcomes derived from projects. The was study aimed at developing a project selection prototype that would help select high priority projects at Uwezo fund for funding. The selection criterion used for selecting projects are based on the project requirements. The research is a form of applied research and utilized quantitative research design. The sample size was computed through convenience non-probability sampling. The prototype was developed using Rapid Application Development (RAD) Methodology as it is designed to take advantage of powerful development software’s like CASE tools and prototyping and enable speedy prototype development. The prototype developed provided a more effective and efficient way of selecting projects for funding that had high priority and success probability.
A Model for improving interoperability of healthcare systems in a distributed environment
(Strathmore University, 2017) Ogutu, Benson Wandera
Access to patient health record for a patient from one healthcare institution to another has had its fair share of challenges. The two healthcare institutions under study have got their own distributed healthcare systems but none of these institutions can access or share their patient health records across. This has hugely been down to the complexity of the healthcare domain, standardization challenges, legacy systems, legal challenges, resistance to change, privacy and security. The study developed an interoperability model for improving patient health records access and sharing across distributed healthcare systems. The modelled application allows two or more distributed healthcare systems to access and share patients’ health records. This model tries to work around the challenges identified above making the system an open system such that any healthcare system can be plugged into it and facilitate data sharing and access. The study applied agile software methodology as it allows for faster iterations and frequent release while factoring in user feedback. The modelling of interoperable distributed healthcare systems is of great importance as it allows for ease of access, portability of data, data confidentiality, integrity and security, capture of data in different formats, file sharing, reduction of costs both to the patients and healthcare institutions and makes the systems robust and scalable.
A Sandboxing based security model to contain malicious traffic in smart homes
(Strathmore University, 2017) Nkinyili, Tiberius Tabulu
The Internet of Things (lOT) is a developing Next Generation Network (NGN) paradigm that aims to have more devices connected to the Internet and the possibility of these devices to autonomously communicate with each other. These devices mainly use wireless links to communicate, with little or no flow control, error checking or security monitoring. While this helps support mobility and optimize performance, the compromise in flow control and security monitoring, renders them more vulnerable to potential attacks from malicious users. This poses security threats to data exchanged between devices especially in a smart home environment. This necessitates having mechanisms to provide security against malicious messages and unauthorized modification of information to limit potential attacks on integrity and confidentiality of data. Isolation mechanisms would be ideal to cushion devices and the entire lOT network. Sandboxing involves isolating suspect data, processes, applications or devices from the rest of the system. This restricts access to more system resources hence ensuring continuity and availability of the entire system. This research work thus proposed a model to ensure comprehensive data security in a smart home by using sandboxing. The model proposed mechanisms to provide an isolating environment to contain malicious traffic by evaluating levels of authorization, and restricting communication nodes to what they were allowed to. This thus ensured a proactive data security approach in lOT networks within a smart home environment. Linux security Module implementations were used to provide a custom sandbox from the Kernel level. Instant Contiki, a virtual version of the lOT operating system Contiki, was used to emulate lOT communication with Cooja as the emulating module.
A Collaborative model for supporting shared healthcare in Kenya
(Strathmore University, 2017) Heroe, Mariam Salim
Shared care is the joint participation of primary and secondary healthcare professionals in a planned care delivery informed by an enhanced information exchange. To effectively practice shared care both the primary and secondary healthcare professionals must be able to collaborate effectively in order to coordinate their activities and assume complementary roles in formulating and carrying out patients’ care plans. Currently, the collaborative shared care schemes implemented in Kenya use inefficient collaboration tools which have made healthcare professionals face various challenges that can results in medical errors, misuse of resources, poor patient outcomes and unnecessary or even harmful services that ultimately raise the healthcare costs and degrade the quality and continuity of healthcare services. This study aimed to come up with a web based collaborative model for supporting shared healthcare. The model was integrated with an analytical back-end system for generating reports on patients, healthcare professionals, diagnosis and referrals as requested by the users. The model was developed using V-process methodology since the user requirements were clear and well defined and the technical expertise needed was available. The research results showed that the proposed model enabled effective collaboration between health professionals. Therefore, if implemented, the model can contribute significantly to the improvement of the quality and continuity of healthcare as well as the patients’ safety.
Multi-sensor fire detection system using an Arduino Uno microcontroller
(Strathmore University, 2017) Obanda, Zephaniah Shiwalo
Untimely response, constrained navigation due to poor urban planning and traffic jams, highly flammable construction materials, insufficient capacity by the fire department and lack of access to automated fire detection systems by residents due to purchasing costs are among the factors that affect fire-fighting services in Kenya and across the African continent. The aftermath of a fire outbreak could very acute leading to widespread loss of property and loss of lives. Residential areas contain numerous flammable materials such as clothing, books, wooden cabinets, beddings and plastics while also housing sources of ignition that include cooking gas and electronic devices thus are prone to severe fire accidents. Fire outbreaks have an inception period of about 3 to 5 minutes which is the optimal time to detect it and put it out after which it might get out of control.This implies that timely identification of a potential fire outbreak is crucial to managing it.Currently, most residential establishments as well as business premises are not fitted with fire detection systems owing to lack of awareness, high purchasing costs and inefficiency of the devices given the high false alarm rates which have a cost attached to them such as the unnecessary deployment of fire-fighting personnel. The fire detection devices are highly susceptible to false alarms because reliance on one sensor that reads only one percept from the environment for instance smoke or heat. However, the advancement of the Internet-of-Things has led to the development of ‘smart’ technologies where multiple sensors can be incorporated into objects like fire detectors additionally enabling them to communicate wirelessly with other objects and carry out programmed tasks. This research aimed at proposing a prototype of a fire detection system using a multi-sensor approach. This research applied rapid prototyping methodology for development of the prototype. Data was collected from secondary sources and experimentation.The prototype used an MQ2 gas sensor, a Grove temperature sensor, a Grove light sensor and an Arduino microcontroller, a GSM and GPS shield. In the event of a fire outbreak, the device will be able to send an SMS alert to the home owner as well as the firefighting department with GPS coordinates of the residence. The prototype recorded 83% success rate and 17% false alarm rate based on 6 test cases of which only one failed.
A Prototype for authentication of secondary school certificates: case of the Kenya Certificate of Secondary Examination certificates
(Strathmore University, 2017) Kaibiru, Raphael Mutua
More often Universities and training institution in Kenya enroll students who want to further their education. Due to increased demand for educated labor force, the number of individuals reported to have used illegitimate KCSE certificates to join these Universities has increased. Perpetrators of this crime have succeeded despite the fact that there are measures to verify and authenticate KCSE certificates. The study examined common forms of document fraud as well as current features used to secure paper documents. Therefore, the aim of this research was to develop a prototype based on digital signature and QR-code technique which would assist institutions in verification of certificates. Agile software development methodology was adopted in developing the prototype. This involved requirements gathering, architecture and design, development and testing. This research was conducted in Nairobi County, targeting a population of thirty seven accredited Universities. In the study both public and private universities were considered in order to eliminate any form of biasness. Data collection tools such as questionnaires were used to gather both qualitative and quantitative data. This data was analyzed qualitatively and quantitatively and presented in pie charts and bar graphs and frequency tables with the aid of statistical tool SPSS. More than 87% of respondents said that the current features were not sufficient in preventing document fraud. In addition, 98% confirmed that a computer based system would greatly contribute towards detecting fake certificates. Consequently, after the prototype was developed and tested 78% of the respondents agreed that a digital system was leveraging on the current security measures and authentication processes.
A Model for early detection of potato late blight disease: a case Study in Nakuru County
(Strathmore University, 2017) Toroitich, Patrick Kiplimo
The agricultural sector has been a key backbone to Kenya’s economy. Agriculture has played a key role in the economy through agricultural farm produce exports and job creation hence improving and maintaining good farming practices is critical in ensuring agricultural yields. Potato (Solanum tuberosum L.) is a major food and cash crop for the country, widely grown by small-scale farmers in the Kenyan highlands. However, early detection of potato diseases such as potato late blight still remains a challenge for both farmers and agricultural extension officers.Consequently agricultural extension officers who play a critical role in training and creating awareness on sound agricultural practices are few and often lack sufficient knowledge and tools.Current techniques used for determining and detecting of crop diseases have heavily relied upon use human vision systems that try to examine physical and phenotypic characteristics such as leaf and stem color. This technique is indeed important for diagnosis of crop diseases, however the use of this technique is not efficient in supporting early detection of crop diseases. This study proposed use of sensors and back propagation algorithm for the prediction of potato late blight disease. Temperature and humidity sensor probes placed on the potato farms were instrumental in monitoring conditions for potato late blight disease. These parameters constituted abiotic factors that favor the development and growth of Phytophthora infestants. Back propagation neural network model was suitable for the prediction of potato late blight disease. In designing the potato late blight prediction model, historical weather data, potato variety tolerance on late blight disease was used to build an artificial neural network disease prediction model.Incoming data streams from the sensors was used to determine level and risk of blight. This study focused on a moderate susceptible cultivator of potato in developing the model. The algorithm was preferred due to its strengths in adaptive learning. The developed model achieved an accuracy of 93.89% while the precision obtained was 0.949. The recall ratio from the neural network was 0.968 and an F-measure of 0.964.
A HIV/AIDS viral load prediction system using artificial neural networks
(Strathmore University, 2017) Tunduny, Titus Kipkosgei
Human Immunodeficiency Virus (HIV) has been affecting people since it was first discovered in 1986. This is as a result of the HIV virus being present in the patient bloodstream for the remainder of their normal life, as there is no cure that exists as of now. HIV, if left unmanaged would end up developing into Acquired Immune Deficiency Syndrome (AIDS), a syndrome that weakens a patient’s immune system and leaves them susceptible to other opportunistic infections. Antiretroviral therapy (ART) has been successfully used in managing the progression of the HIV virus in the human body. However, poor adherence attributable to ignorance, adverse drug effects, and age have derailed the attainment of viral load suppression amongst the HIV positive people. The progression of the virus is tracked by counting Cluster of Differentiation 4 positive cells, and the amount of virus in the blood (viral load) every 6 months. This research introduces the use of multi-layer artificial neural networks with backpropagation to predict the HIV/AIDS viral load levels over a given period of time (in weeks). The Data-driven Modelling methodology was used in the development of the model. This methodology was ideal since the model relied solely on pre-existing data, and supports artificial neural networks. The model developed performed at an accuracy level of 93.76% and a mean square error of 0.0323. The results showed that the neural network can be used as a suitable algorithm for HIV/AIDS viral load level prediction. The learning rate used in the study was 0.005 and the momentum was 0.9. The iterations for the training, testing and validation varied.
Wireless baby tracking system to curb child kidnapping
(Strathmore University, 2017) Mwangi, Caroline Muthoni
Child kidnapping is common case, not only in Kenya but also all over the world. Infants are more vulnerable due to the fact that they are helpless and can easily be carried away without anyone noticing that something is amiss. Over time, different approaches have been developed to track children especially in public places. Many of these solutions involve GPS tracking but not many organizations and events involving gatherings of many children have been in a position to adopt such solutions. This is because they are expensive and do not integrate easily with other systems. The study seeks to come up with a solution to track babies within the workplace. It puts into consideration the “babies at work” policy, which has been adopted many companies including some in Kenya. The solution is based on RFID and basically combines active RFID tags with the existing wireless LAN. RFID has been greatly improved and the use of active RFID tags, which broadcast a signal to the access point, enables companies with an existing Internet connectivity to make use of their bandwidth without having to purchase RFID readers. The work is meant to provide a cheap and scalable solution that can traverse different scenarios, such as, public baby day-care centres, Churches and other social events. The proposed solution will be based on prototyping from the analysis phase to the implementation phase. The solution will then be tested to check on its reliability, performance and accuracy. The language to be used in development of the solution is PHP, Python, HTML, CSS, Javascript and MySQL database.
Optimized terasort algorithm for data analytics: case of climate data analysis
(Strathmore University, 2017) Matu, Fiona Mugure
Weather forecasting has proven valuable in unravelling the causes of the occurrence of natural phenomena and predicting of future climatic conditions. Subsequently, better preparation and policy making regarding these occurrences can be done using resultant information from techniques employed in weather forecasting. Analysis of vast amounts of data are characteristic of climatology hence require computing intensive techniques such as numerical weather prediction (NWP). This has made climate modelling a preserve of high performance computing (HPC) until the recent entrance of big data analytics. It is therefore necessary to optimize the algorithms used in the big data environment so as to give comparable performance to that offered by HPC environments. The study aimed at improving the big data MapReduce framework of analysis by optimizing the TeraSort benchmark algorithm. The algorithm proposed employed classical sort techniques and incorporated quantum computing mechanisms. Historical weather data collected at weather stations across the world was gathered and converted into organised, human readable format to suffice as input to the program. The proposed algorithm constituting of a map, sort and reduction phase transformed the bulky observational data into a compact summary of monthly temperature averages in linear complexity. This is a significant improvement in performance in comparison to the TeraSort algorithm on a single node. The study concludes by suggesting areas that may be explored for further optimization with emphasis on quantum computing capabilities.
A Model for the classification of student neediness using artificial neural networks
(Strathmore University, 2017) Manyasi, Eunice Engefu
Financial aid has been used worldwide to assist students at higher learning institutions finance their education. The aid has majorly been offered by the government, private companies and non-governmental institutions in form of loans, grants, scholarships and work study programs. It has made great progress in increasing the enrolment rate of students to higher learning institutions. The aid is usually given to applicants who have been selected after applying for the aid, and a committee ensuring that they have meet the set criteria to be awarded. Currently the number of applicants applying for financial aid has increased leading to challenges of errors and bias in the selection and award process due to overwhelming data which becomes too complex for the committees to analyse. This has led to some more deserving students not receiving the financial aid due to inaccuracies. Artificial intelligence has been applied in various fields for the analysis and classification of huge amounts of data. It has been applied in finance to predict the credit rating of customers which uses a similar concept in classification of applicants. The research sought to apply machine learning to in the selection and award process of needy students. Historical financial aid data which was labelled as awarded and not awarded, was used to train the feed forward neural network learning model. The inputs used included parents occupation and income, family income and family spending. The research employed experimental research to determine the variables that best identified the needy students and qualitative research to get the ideas and opinions of participants with regards to the study. The model accurately classified 2955 instances as true positives and 18 instances as true negative out of 3043 instances, giving it a 97.6% accuracy.
Automatic power meter reading based on Arduino micro-controller unit: case of the Kenya power and Lighting Company
(Strathmore University, 2017) Keere, Samuel Ondieki
Automatic Meter Reading (AMR), is the technology of automatically collecting data from metering devices (electric, water, gas) and transferring that data to a central database for billing and/or further analysis. Power utility firms have an obligation to bill their customers based on actual meter readings taken. However, provision of these services have had challenges. This leads to the issue of estimated readings and an inconvenient billing method that is based on incorrect readings. This is normally evident during the power meter-reading period when some power consumers influence clerks to evade paying their power bills. A common phenomenon in some cases is either where the same field clerks take wrong readings or end up over/underestimating the customer’s consumption. This ends up inconveniencing customers budgeting. In a bid to address this issue, Kenya Power Company has resorted to retrofitting the existing conventional post-paid meters with prepaid meters. This is however expensive and would take a long time to implement. This study proposes a solution for automatic meter reading by use of an ATmega328P MCU. This will entail an ACS712 current sensor that will be connected onto a power meter coupled to a LoRa gateway through a LPWAN for transmission of data to a cloud server for subsequent upload and analysis. Agile software methodology was used which allowed faster iteration and more frequent release with subsequent user feedback. This solution would not require replacement of the existing meters, making it cost effective and fast to implement. This study provides a solution that can enable the users have information on their readings, rate of billing and be able to report on any power issues affecting them. This will also help the power providers have their customers’ advice, which would aid in decision-making processes.
Applying decision tree-based model in tender evaluation: case of Technical University of Mombasa
(Strathmore University, 2017) Mandale, Samuel Kumbu
Unfair tender evaluation and contract award in public procurement is prevalent in Kenya. This has contributed to low quality of goods, services and projects. Successful implementation of building projects is heavily impacted by taking the right decision during tendering processes. Manning tender procedures can be complex and uncertain, involving coordination of numerous tasks and persons with different priorities and objectives. Bias and inconsistent decision are inevitable if the decision-making process is wholly dependent on intuition, subjective judgement or emotions. In making transparent decision and beneficial competition tendering, there is need for a flexible tool that could facilitate fair decision making. The purpose of this research was to present a model of an IT solution integrating the concepts of supervised machine learning techniques in the context of tender evaluation in public procurement. A dataset of 100 instances comprising of 53 positive and 47 negative examples was used to train J48 decision tree classifier to build the model. After attribute selection in a WEKA environment, 4 of the 7 attributes of the dataset were used as independent variables (inputs) namely, Experience, Capacity, Number of personnel and Professionalism. A set criteria was used to determine the values of the independent variables. The dependent variable (output) was a category class with either “PASS” or “FAIL” values. To determine the class of an entity the J48 model considers all the values of the independent variables based on set rules. This algorithm was preferred due to its relatively simple model among other benefits stated herein. The dataset from TUM was divided into test data and training data for the model. The performance appraisal of the model was based on the accuracy of the classification, the precision, recall ratio, ROC curve and the F- Measure. The model was proven to be impressively accurate with an accuracy of 91.1765 % while the precision obtained was 0.857. The recall ratio was 1 and an F-measure of 0.923.
A Dimensional student enrollment prediction model: case of Strathmore University
(Strathmore University, 2017) Alaka, Benard Ochieng
The rate of student admissions within most Kenyan Universities has thus far been met with a corresponding uncertainty in budgetary allocation. Additionally, the increase of most applicants not being enrolled has led to lower institution yield. Due to the uncertainty of the quantity of students to be enrolled, planning and budgetary issues have arisen as stated earlier. Departments in charge of recruiting students are left to speculate the numbers likely to turn up. This in most cases is not accurate since it results into gaps in the allocated budgets and straining of resources. Currently, in Kenya, there is no institutions of higher learning that has a reliable means of predicting the expected institutional yield. Rather, academic management systems exist and are used to manage daily academic routines. These systems are served by transactional databases which are subject to being edited frequently and as such lack the capability of archiving histories of instances of the data within these databases; which makes them unsuitable for carrying out analysis on enrollment prediction. As such, a dimensional enrollment prediction model is proposed so as to aid in forecasting; not only of how many admitted students will be enrolled but also particular individuals who could show up for the purposes of follow-up activities. The inputs to the proposed enrollment prediction system were sourced from dimensional data stored in a data warehouse regarding to student details as per the admission as well as snapshot data of third party satisfaction index from accredited sources. The proposed system then transforms this data into dimensional data by adding a time variant to it and then passing the data through a neural network. The resultant model is then to be used in predicting students’ enrollment. The proposed model was tested for accuracy using the precision, recall ratio and the F-score Measure. The model’s accuracy was considerably high with an accuracy of 71.39% with a precision of 0.72. The average recall ratio was 0.71 and while F-score of 0.71 as well was obtained. In relation to some of the works reviewed the proposed model was a bit lower accuracy due the dataset used that was noisy as fetched from real student transactional databases.
Vision based model for identification of adulterants in milk
(Strathmore University, 2017) Kobek, Jacklyne Atieno
Milk adulteration is a social problem that exists in both developed and developing countries. This is due to lack of regulations or enforcement, proper refrigeration techniques, high yields with no market and hence the use of high levels of different adulterants to elongate the shelf life, prevent spoilage, increase thickness and whiteness. This research proposes the use of a mobile phone application, to determine the intensity and type of adulterant used in milk, specifically water adulterant, by use of back propagation artificial neural network (ANN). A scanned image of milk spiked with acid-base indicator (bromothymol blue) was taken, after it changed color. Using ANN, the image was classified in terms of color descriptors such as mean of red (R), green (G), blue (B), luminosity (L, which is the sum of R, G, and B). After classification, partial least squares regression (PLSR) and principal component regression analysis (PCR) model, was used to predict the adulteration intensity in milk using the intensity of adulteration as a dependent variable.
Prototype for tracking voluntary blood donors in enhancing emergency medical response: case Aga Khan Hospital
(Strathmore University, 2017) Mwangi, Patrick Kariuki
The World Health Organization recommends in its Global Database on Blood Safety that all activities related to blood collection, screening, processing, storage and distribution should be coordinated at the national level through effective organization and a national blood policy and system. The researcher developed a prototype that comprised of an android mobile application, and a front end application. The researcher used prototyping methodology to develop the application. The prototype developed in this research aims at making use of the existing telecommunication infrastructure while merging this with the blood bank’s information technology systems. The target population comprised of employees working within the Aga Khan University Hospital Blood Transfusion Center and voluntary non remunerable donors. Both quantitative and qualitative techniques were used to evaluate the information collected. The study concludes that it is difficult to get the domicile location of, voluntary donors. By having system that tracks voluntary donor movement, it is possible to enhance emergency medical response that requires blood transfusion. The diverse activities in the blood donation and distribution service can be streamlined as a result of data mining capabilities.
Rapid discharge failure prediction model for solar charged lithium-ion batteries
(Strathmore University, 2017) Mutiso, Matthew Mutee
Lithium-ion batteries are continually being deployed in many appliances. This is due to their high energy density and cost effectiveness. Most of these have been around for years in portable devices such as mobile phones. With the onset of smartphones, there is an ever increasing need to have batteries with superior performance. This can be viewed from the context of the need for fast charging and an ability to support a fully multitasked smartphone. Lithium-ion batteries have become the defacto battery type in many of these and similar applications due to their inherent characteristics. They have found use in not just mobile phones but also in innovative products designed to light homes as well provide for mobile phone charging in rural Africa. These products include a battery pack of Lithium-ion batteries cells charged by solar panels. There are a number of challenges facing the companies dealing with such products. There is a need to provide a superior product while at the same time ensure efficiency in the production line so as to bring down costs. All these need to be done while maintaining the elusive customer loyalty. One of the major issues faced is accelerated degradation which cannot be noticed using conventional approaches. Currently the main mode of triage for failure is visualization of graphs from data collected from the sensors attached to the batteries and observing for irregularities in the charge and discharging patterns. Existing literature talks about models used on linear data for forecasting in various fields of research. It also proposes an approach to predict battery life in batteries used on various applications such as hybrid electric vehicles. The proposed method will take advantage of predictive analytics in time series analysis to predict failure based on data from the batteries. Data from the batteries spanning 30 days was used to generate gradients of daily charging gradients. These were used as the training data with a binary class of faulty and good. We are able to train a model using the nearest neighbor algorithm to obtain over 80% accuracy with only a sample of 200 batteries data.
Confidentiality protection model for securing data in cloud computing
(Strathmore University, 2017) Mwanyika, James Mwasela
Cloud storage providers store the data in multiple servers maintained by hosting companies. This increases the risk of unauthorized access to the private data. Even though the cloud continues to gain popularity in usability and attraction, the problems lies with data confidentiality, loss of control, lack of trust, data theft and the fact that user data is stored in unencrypted format such as in the case of amazon 3 cloud storage servers. This research focuses on internal threats presented by cloud service providers. Using encryption techniques, the risk of unauthorized access can be controlled. In the proposed methodology, a user encrypts files with secret keys before uploading them into the cloud. Once encrypted, the file is stored in an encrypted format in the cloud. For a user to download files form the cloud, the file owner first accepts a request by an authorized user, and an application server provides an Access key. Using an access key, a user downloads data and uses a secret key to convert cipher text into a plain text. This technique ensures end-to end encryption and completely hides the data from cloud service providers hence maintain confidentiality. Implementation involved building an encryption application algorithm, for deployment on the user computer. The algorithm consists of a single encryption and hybrid encryption modules. A user selects either a single or hybrid encryption module from the application based on security level requirements of data to be uploaded to the cloud. The model consists of registration/login module, encryption module, uploading module, downloading module and decryption module. This research contributes to providing security to the data stored in the cloud, by encrypting the data before uploading it into the cloud. Data owner controls key management where generation, storage and distribution remain in his control. Data owners lack the courage to strategically outsource data storage to the cloud. However, once the trust issues between data owners and cloud service providers are addressed through the deployment of this model, there shall be some attitude change on the side of data owners towards the adoption of cloud storage usage and therefore bridging the trust issues existing between data owners and cloud service providers.

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