A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing technologies that underlie the conception, design and utilization of intelligent systems. Several works have been done where engineers and scientists have applied intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a concurrent fuzzy-neural network approach combining unsupervised and supervised learning techniques to develop the Tactical Air Combat Decision Support System (TACDSS). Experiment results clearly demonstrate the efficiency of the proposed technique

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

The Role of Machine Learning in Managing Uncertainty in Projects – A View on Early Warning Systems

Machine learning techniques deals with, among other things, pattern recognition in large amounts of data to identify trends and possible events in the future regarding a given topic of interest. Machine learning methods are useful for addressing challenges in and creating new benefits for organisations. This paper looks at how machine learning can contribute to manage projects effectively. Many organisations apply the concept of project. A part of them are purely project-based organisations, and a part of them carry out projects in addition to their mass-production activities and permanent operations. Within the realm of project management, this paper sets its focus on studying the role of machine learning in handling unexpected events and uncertainty in projects. One of the ways to deal with unexpected events and uncertainty is to capture early warning signs that can predict unexpected events. A major failure of projects can be seen as a combined effect of a series of small failures, negative results or problems that have occurred over a period of time. Project teams may not notice or just ignore early warning signs of these problems and choose to work further in the project. This could finally lead to a major failure, at which point no preventive actions could save the project from the major failure. Several researchers have researched on early warning signs and systems within the context of projects. Early warning signs can be seen as some kind of a pattern recognition from a pool of relevant data. This paper aims to answer the following two interrelated research questions: (1) What role does machine learning have in early warnings in projects? (2) How can machine learning contribute to effective project management (for example, handling uncertainty in projects)? This is a conceptual paper, based on literature study.publishedVersio

Predictive models as early warning systems for student academic performance in introductory programming

Computer programming is fundamental to Computer Science and IT curricula. At the novice level it covers programming concepts that are essential for subsequent advanced programming courses. However, introductory programming courses are among the most challenging courses for novices and high failure and attrition rates continue even as computer science education has seen improvements in pedagogy. Consequently, the quest to identify factors that affect student learning and academic performance in introductory computer programming courses has been a long-standing activity. Specifically, weak novice learners of programming need to be identified and assisted early in the semester in order to alleviate any potential risk of failing or withdrawing from their course. Hence, it is essential to identify at-risk programming students early, in order to plan (early) interventions. The goal of this thesis was to develop a validated, predictive model(s) with suitable predictors of student academic performance in introductory programming courses. The proposed model utilises the Naïve Bayes classification machine learning algorithm to analyse student performance data, based on the principle of parsimony. Furthermore, an additional objective was to propose this validated predictive model as an early warning system (EWS), to predict at-risk students early in the semester and, in turn, to potentially inform instructors (and students) for early interventions. We obtained data from two introductory programming courses in our study to develop and test the predictive models. The models were built with student presage and in progress-data for which instructors may easily collect or access despite the nature of pedagogy of educational settings. In addition, our work analysed the predictability of selected data sources and looked for the combination of predictors, which yields the highest prediction accuracy to predict student academic performance. The prediction accuracies of the models were computed by using confusion matrix data including overall model prediction accuracy, prediction accuracy sensitivity and specificity, balanced accuracy and the area under the ROC curve (AUC) score for generalisation. On average, the models developed with formative assessment tasks, which were partially assisted by the instructor in the classroom, returned higher at-risk prediction accuracies than the models developed with take-home assessment task only as predictors. The unknown data test results of this study showed that it is possible to predict 83% of students that need support as early as Week 3 in a 12-week introductory programming course. The ensemble method-based results suggest that it is possible to improve overall at-risk prediction performance with low false positives and to incorporate this in early warning systems to identify students that need support, in order to provide early intervention before they reach critical stages (at-risk of failing). The proposed model(s) of this study were developed on the basis of the principle of parsimony as well as previous research findings, which accounted for variations in academic settings, such as academic environment, and student demography. The predictive model could potentially provide early warning indicators to facilitate early warning intervention strategies for at-risk students in programming that allow for early interventions. The main contribution of this thesis is a model that may be applied to other programming and non-programming courses, which have both continuous formative and a final exam summative assessment, to predict final student performance early in the semester.Ohjelmointi on informaatioteknologian ja tietojenkäsittelytieteen opinto-ohjelmien olennainen osa. Aloittelijatasolla opetus kattaa jatkokurssien kannalta keskeisiä ohjelmoinnin käsitteitä. Tästä huolimatta ohjelmoinnin peruskurssit ovat eräitä haasteellisimmista kursseista aloittelijoille. Korkea keskeyttämisprosentti ja opiskelijoiden asteittainen pois jättäytyminen ovat vieläkin tunnusomaisia piirteitä näille kursseille, vaikka ohjelmoinnin opetuksen pedagogiikka onkin kehittynyt. Näin ollen vaikuttavia syitä opiskelijoiden heikkoon suoriutumiseen on etsitty jo pitkään. Erityisesti heikot, aloittelevat ohjelmoijat tulisi tunnistaa mahdollisimman pian, jotta heille voitaisiin tarjota tukea ja pienentää opiskelijan riskiä epäonnistua kurssin läpäimisessä ja riskiä jättää kurssi kesken. Heikkojen opiskelijoiden tunnistaminen on tärkeää, jotta voidaan suunnitella aikainen väliintulo. Tämän väitöskirjatyön tarkoituksena oli kehittää todennettu, ennustava malli tai malleja sopivilla ennnustusfunktioilla koskien opiskelijan akateemista suoriutumista ohjelmoinnin peruskursseilla. Kehitetty malli käyttää koneoppivaa naiivia bayesilaista luokittelualgoritmia analysoimaan opiskelijoiden suoriutumisesta kertynyttä aineistoa. Lähestymistapa perustuu yksinkertaisimpien mahdollisten selittävien mallien periaatteeseen. Lisäksi, tavoitteena oli ehdottaa tätä validoitua ennustavaa mallia varhaiseksi varoitusjärjestelmäksi, jolla ennustetaan putoamisvaarassa olevat opiskelijat opintojakson alkuvaiheessa sekä informoidaan ohjaajia (ja opiskelijaa) aikaisen väliintulon tarpeellisuudesta. Keräsimme aineistoa kahdelta ohjelmoinnin peruskurssilta, jonka pohjalta ennustavaa mallia kehitettiin ja testattiin. Mallit on rakennettu opiskelijoiden ennakkotietojen ja kurssin kestäessä kerättyjen suoriutumistietojen perusteella, joita ohjaajat voivat helposti kerätä tai joihin he voivat päästä käsiksi oppilaitoksesta tai muusta ympäristöstä huolimatta. Lisäksi väitöskirjatyö analysoi valittujen datalähteiden ennustettavuutta ja sitä, mitkä mallien muuttujista ja niiden kombinaatioista tuottivat kannaltamme korkeimman ennustetarkkuuden opiskelijoiden akateemisessa suoriutumisessa. Mallien ennustusten tarkkuuksia laskettiin käyttämällä sekaannusmatriisia, josta saadaan laskettua ennusteen tarkkuus, ennusteen spesifisyys, sensitiivisyys, tasapainotettu tarkkuus sekä luokitteluvastekäyriä (receiver operating characteristics (ROC)) ja näiden luokitteluvastepinta-ala (area under curve (AUC)) Mallit, jotka kehitettiin formatiivisilla tehtävillä, ja joissa ohjaaja saattoi osittain auttaa luokkahuonetilanteessa, antoivat keskimäärin tarkemman ennustuksen putoamisvaarassa olevista opiskelijoista kuin mallit, joissa käytettiin kotiin vietäviä tehtäviä ainoina ennusteina. Tuntemattomalla testiaineistolla tehdyt mallinnukset osoittavat, että voimme tunnistaa jo 3. viikon kohdalla 83% niistä opiskelijoista, jotka tarvitsevat lisätukea 12 viikkoa kestävällä ohjelmoinnin kurssilla. Tulosten perusteella vaikuttaisi, että yhdistämällä metodeja voidaan saavuttaa parempi yleinen ennustettavuus putoamisvaarassa olevien opiskelijoiden suhteen pienemmällä määrällä väärin luokiteltuja epätositapauksia. Tulokset viittaavat myös siihen, että on mahdollista sisällyttää yhdistelmämalli varoitusjärjestelmiin, jotta voidaan tunnistaa avuntarpeessa olevia opiskelijoita ja tarjota täten varhaisessa vaiheessa tukea ennen kuin on liian myöhäistä. Tässä tutkimuksessa esitellyt mallit on kehitetty nojautuen yksinkertaisimman selittävän mallin periaatteeseen ja myös aiempiin tutkimustuloksiin, joissa huomioidaan erilaiset akateemiset ympäristöt ja opiskelijoiden tausta. Ennustava malli voi tarjota indikaattoreita, jotka voivat mahdollisesti toimia pohjana väliintulostrategioihin kurssilta putoamisvaarassa olevien opiskelijoiden tukemiseksi. Tämän tutkimuksen keskeisin anti on malli, jolla opiskelijoiden suoriutumista voidaan arvioida muilla ohjelmointia ja muita aihepiirejä käsittelevillä kursseilla, jotka sisältävät sekä jatkuvaa arviointia että loppukokeen. Malli ennustaisi näillä kursseilla lopullisen opiskelijan suoritustason opetusjakson alkuvaiheessa

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method. The proposed model was compared to other commonly used ensemble models in a real-world urban drainage system in the UK. The results show that the proposed model achieves a higher hit rate compared to other benchmark models, with a hit rate of around 85% vs 70 % for the next 3 h of forecasting. Additionally, the proposed smart model can accurately classify various timesteps of flood or non-flood events without significant lag times, resulting in fewer false alarms, reduced unnecessary risk management actions, and lower costs in real-time early warning applications. The findings also demonstrate that two features, "antecedent precipitation history" and "seasonal time occurrence of rainfall," significantly enhance the accuracy of flood forecasting with a hit rate accuracy ranging from 60 % to 10 % for a lead time of 15 min to 3 h