In this paper, we use the forward recursion BCJR algorithm to estimate the symmetric information rate for Two Dimensional Magnetic Recording (TDMR) channels. In particular, we consider a TDMR read/write channel whose all components, including recording medium, write and readback processes are modeled in software. Since the primary source of noise in TDMR arises from irregularities in the recording medium and leads to highly colored and data-dependent jitter, the pattern dependent noise predictive (PDNP) algorithm is implemented to improve the accuracy and performance of SIR estimation. Furthermore, we study the performance gain of using the PDNP algorithm in SIR estimation through simulations over the Voronoi based media model for different TDMR channel configurations.

The automotive industry is continuously innovating and adapting new technologies. Along with that, the companies work towards maintaining the quality of a hardware product and meeting the customer demands. Before delivering the product to the customer, it is essential to test and approve it for the safe use. The concept remains the same when it comes to a software. Adapting modern technologies will further improve the efficiency of testing a software. The thesis aims to build a machine learning algorithm for the implementation during the software testing. In general, the evaluation of a generated test report after the testing consumes more time. The built algorithm should be able to reduce the time spent and the manual effort during the evaluation. Basically, the machine learning algorithms will analyze and learn the data available in the old test reports. Based on the learnt data pattern, it will suggest the possible root causes for the failed test cases in the future. The thesis report has the literature survey that helped in understanding the machine learning concepts in different industries for similar problems. The tasks involved while building the model are data loading, data pre-processing, selecting the best conditions for each algorithm and comparison of the performance among them. It also suggest the possible future work towards improving the performance of the models. The entire work is implemented in Jupyter notebook using pandas and scikit-learn libraries.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Predictive analytics in healthcare has become one of the major focus areas in healthcare delivery worldwide. Due to the massive amount of healthcare data being captured, healthcare providers and health insurers are investing in predictive analytics and its enabling technologies to provide valuable insight into a large variety of healthcare outcomes. One of the latest developments in the field of healthcare predictive modelling (PM) was the launch of the Heritage Health Prize; a competition that challenges individuals from across the world to develop a predictive model that successfully identifies the patients at risk of admission to hospital from a given patient population. The patient-admission predictive algorithm (PAPA) is aimed at reducing the number of unnecessary hospitalisations that needlessly constrain healthcare service delivery worldwide. The aim of the research presented is to determine the feasibility and value of applying PAPAs in the South African healthcare system as part of a preventive care intervention strategy. A preventive care intervention strategy is a term used to describe an out-patient hospital service, aimed at providing preventive care in an effort to avoid unnecessary hospitalisations from occurring. The thesis utilises quantitative and qualitative techniques. This included a review of the current and historic PM applications in healthcare to determine the major expected shortfalls and barriers to implementation of PAPAs, as well as the institutional and operational requirements of these predictive algorithms. The literature study is concluded with a review of the current state of affairs in the South African healthcare system to, firstly, articulate the need for PAPAs and, secondly, to determine whether the public and private sectors provide a suitable platform for implementation (evaluated based on the operational and institutional requirements of PAPAs). Furthermore, a methodology to measure and analyse the potential value-add of a PAPA care intervention strategy was designed and developed. The methodology required a survey of the industry leaders in the private healthcare sector of South Africa to identify, firstly, the current performance foci and, secondly, the factors that compromise the performance of these organisations to deliver high quality, resource-effective care. A quantitative model was developed and applied to an industry leader in the private healthcare sector of South Africa, in order to gauge the resultant impact of a PAPA care intervention strategy on healthcare provider performance. Lastly, in an effort to ensure the seamless implementation and operation of PAPAs, an implementation framework was developed to address the strategic, tactical, and operational challenges of applying predictive analytics and preventive care strategies similar to PAPAs. The research found that the application of PAPAs in the public healthcare sector of South Africa is infeasible. The private healthcare sector, however, was considered a suitable platform to implement PAPAs, as this sector satisfies the institutional and operational requirements of PAPAs. The value-add model found that a PAPA intervention strategy will add significant value to the performance of healthcare providers in the private healthcare sector of South Africa. Noteworthy improvements are expected in the ability of healthcare provider’s to coordinate patient care, patient-practitioner relationships, inventory service levels, and staffing level efficiency and effectiveness. A slight decrease in the financial operating margin, however, was documented. The value-add methodology and implementation support framework provides a suitable platform for future researchers to explore the collaboration of preventive care and PM in an effort to improve healthcare resource management in hospitals. In conclusion, patient-admission predictive algorithms provide improved evidence-based decision making for preventive care intervention strategies. An efficient and effective preventive care intervention strategy improves healthcare provider performance and, therefore, adds significant value to these organisations. With the proper planning and implementation support, the application of PAPA care intervention strategies will change the way healthcare is delivered worldwide. / AFRIKAANSE OPSOMMING: Vooruitskattingsanalises in gesondheidsorg het ontwikkel in een van die mees belangrike fokusareas in die lewering van kwaliteit gesondheidsorg in ontwikkelde lande. Gesondheidsorgverskaffers en lewensversekeraars belê in vooruitskattingsanalise en ooreenstemmende tegnologieë om groot hoeveelhede gesondheidsorg pasiënt-data vas te lê, wat waardevolle insigte bied ten opsigte van ʼn groot verskeidenheid van gesondheidsorg-uitkomstes. Een van die nuutste ontwikkelinge in die veld van gesondheidsorg vooruitskattingsanalises, was die bekendstelling van die “Heritage Health Prize”, 'n kompetisie wat individue regoor die wêreld uitdaag om 'n vooruitskattingsalgoritme te ontwikkel wat pasiënte identifiseer wat hoogs waarskynlik gehospitaliseer gaan word in die volgende jaar en as bron-intensief beskou word as gevolg van die beraamde tyd wat hierdie individue in die hospitaal sal deurbring. Die pasiënt-toelating vooruitskattingsalgoritme (PTVA) het ten doel om onnodige hospitaliserings te identifiseer en te voorkom tem einde verbeterde hulpbronbestuur in gesondheidsorg wêreldwyd te bewerkstellig. Die doel van die hierdie projek is om die uitvoerbaarheid en waarde van die toepassing van PTVAs, as 'n voorkomende sorg intervensiestrategie, in die Suid-Afrikaanse gesondheidsorgstelsel te bepaal. 'n Voorkomende sorg intervensiestrategie poog om onnodige hospitaliserings te verhoed deur die nodige sorgmaatreëls te verskaf aan hoë-riskio pasiënte, sonder om hierdie individue noodwendig te hospitaliseer. Die tesis maak gebruik van kwantitatiewe en kwalitatiewe tegnieke. Dit sluit in 'n hersiening van die huidige en historiese vooruitskattings modelle in die gesondheidsorgsektor om die verwagte struikelblokke in die implementering van PTVAs te identifiseer, asook die institusionele en operasionele vereistes van hierdie vooruitskattingsalgoritmes te bepaal. Die literatuurstudie word afgesluit met 'n oorsig van die huidige stand van sake in die Suid-Afrikaanse gesondheidsorgstelsel om, eerstens, die behoefte vir PTVAs te identifiseer en, tweedens, om te bepaal of die openbare en private sektore 'n geskikte platform vir implementering bied (gebaseer op die operasionele en institusionele vereistes van PTVAs). Verder word 'n metodologie ontwerp en ontwikkel om die potensiële waarde-toevoeging van 'n PTVA sorg intervensiestrategie te bepaal. Die metode vereis 'n steekproef van die industrieleiers in die private gesondheidsorgsektor van Suid-Afrika om die volgende te identifiseer: die huidige hoë-prioriteit sleutel prestasie aanwysers (SPAs), en die faktore wat die prestasie van hierdie organisasies komprimeer om hoë gehalte, hulpbron-effektiewe sorg te lewer. 'n Kwantitatiewe model is ontwikkel en toegepas op een industrieleier in die private Stellenbosch gesondheidsorgsektor van Suid-Afrika, om die gevolglike impak van 'n PTVA sorg intervensiestrategie op prestasieverbetering te meet. Ten slotte, in 'n poging om te verseker dat die implementering en werking van PTVAs glad verloop, is 'n implementeringsraamwerk ontwikkel om die strategiese, taktiese en operasionele uitdagings aan te spreek in die toepassing van vooruitskattings analises en voorkomende sorg strategieë soortgelyk aan PTVAs. Die navorsing het bevind dat die toepassing van PTVAS in die openbare gesondheidsorgsektor van Suid-Afrika nie lewensvatbaar is nie. Die private gesondheidsorgsektor word egter beskou as 'n geskikte platform om PTVAs te implementeer, weens die bevrediging van die institusionele en operasionele vereistes van PTVAs. Die waarde-toevoegings model het bevind dat 'n PTVA intervensiestrategie beduidende waarde kan toevoeg tot die prestasieverbetering van gesondheidsorgverskaffers in die private gesondheidsorgsektor van Suid-Afrika. Die grootste verbetering word in die volgende SPAs verwag; sorg koördinasie, dokter-pasiënt verhoudings, voorraad diensvlakke, en personeel doeltreffendheid en effektiwiteit. 'n Effense afname in die finansiële bedryfsmarge word egter gedokumenteer. 'n Implementering-ondersteuningsraamwerk is ontwikkel in 'n poging om die sleutel strategiese, taktiese en operasionele faktore in die implementering en uitvoering van 'n PTVA sorg intervensiestrategie uit te lig. Die waarde-toevoegings metodologie en implementering ondersteuning raamwerk bied 'n geskikte platform vir toekomstige navorsers om die rol van vooruitskattings modelle in voorkomende sorg te ondersoek, in 'n poging om hulpbronbestuur in hospitale te verbeter. Ten slotte, PTVAs verbeter bewysgebaseerde besluitneming vir voorkomende sorg intervensiestrategieë. 'n Doeltreffende en effektiewe voorkomende sorg intervensiestrategie voeg aansienlike waarde tot die algehele prestasieverbetering van gesondheidsorgverskaffers. Met behoorlike beplanning en ondersteuning met implementering, sal PTVA sorg intervensiestrategieë die manier waarop gesondheidsorg gelewer word, wêreldwyd verander.

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