732 research outputs found
Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification
Chronic Kidney Disease (CKD) is a disorder that impairs kidney function. Early signs of CKD patients are very difficult until they lose 25% of their kidney function. Therefore, early detection and effective treatment are needed to reduce the mortality rate of CKD sufferers. In this study, the authors diagnose the CKD dataset using the Support Vector Machine (SVM) classification method to obtain accurate diagnostic results. The authors propose a comparison of the result on applying the feature selec- tion method to get the best feature candidates in improving the classification result. The testing process compares the Symmetrical Uncertainty (SU) and Multivariate Symmetrical Uncertainty (MSU) feature selection method and the SVM method as a classification method. Several experimental scenarios were carried out using the SU and MSU feature selection methods using the CKD dataset. From the results of the tests carried out, it shows that using the MSU feature selection method with 80%: 20% data split produces nine important features with an accuracy value of 0.9, sensi- tivity 0.84, specification 1.0, and when viewed on the ROC graph, the MSU method graph shows the true positive value is higher than the false positive value. So the classification using the MSU feature selection method is better than the SU feature selection method by 90% accurac
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Thumbs up, thumbs down:non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning
In this study, we present a transfer learning method for gesture classification via an inductive and supervised transductive approach with an electromyographic dataset gathered via the Myo armband. A ternary gesture classification problem is presented by states of âthumbs upâ, âthumbs downâ, and ârelaxâ in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a â20 Questionsâ game
Selection of CMIP5 GCM ensemble for the projection of spatio-temporal changes in precipitation and temperature over the Niger Delta, Nigeria
Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980â2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25â1.79 °C under RCP8.5 during the periods 2070â2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38â2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures
Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review
Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) â a branch of AI â have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area
Type-2 fuzzy logic system applications for power systems
PhD ThesisIn the move towards ubiquitous information & communications technology, an
opportunity for further optimisation of the power system as a whole has arisen.
Nonetheless, the fast growth of intermittent generation concurrently with markets
deregulation is driving a need for timely algorithms that can derive value from these
new data sources. Type-2 fuzzy logic systems can offer approximate solutions to
these computationally hard tasks by expressing non-linear relationships in a more
flexible fashion. This thesis explores how type-2 fuzzy logic systems can provide
solutions to two of these challenging power system problems; short-term load
forecasting and voltage control in distribution networks. On one hand, time-series
forecasting is a key input for economic secure power systems as there are many tasks
that require a precise determination of the future short-term load (e.g. unit
commitment or security assessment among others), but also when dealing with
electricity as commodity. As a consequence, short-term load forecasting becomes
essential for energy stakeholders and any inaccuracy can be directly translated into
their financial performance. All these is reflected in current power systems literature
trends where a significant number of papers cover the subject. Extending the existing
literature, this work focuses in how these should be implemented from beginning to
end to bring to light their predictive performance. Following this research direction,
this thesis introduces a novel framework to automatically design type-2 fuzzy logic
systems. On the other hand, the low-carbon economy is pushing the grid status even
closer to its operational limits. Distribution networks are becoming active systems with
power flows and voltages defined not only by load, but also by generation. As
consequence, even if it is not yet absolutely clear how power systems will evolve in
the long-term, all plausible future scenarios claim for real-time algorithms that can
provide near optimal solutions to this challenging mixed-integer non-linear problem.
Aligned with research and industry efforts, this thesis introduces a scalable
implementation to tackle this task in divide-and-conquer fashio
Application of advanced machine learning techniques to early network traffic classification
The fast-paced evolution of the Internet is drawing a complex context which
imposes demanding requirements to assure end-to-end Quality of Service. The
development of advanced intelligent approaches in networking is envisioning
features that include autonomous resource allocation, fast reaction against
unexpected network events and so on. Internet Network Traffic Classification
constitutes a crucial source of information for Network Management, being decisive
in assisting the emerging network control paradigms. Monitoring traffic flowing
through network devices support tasks such as: network orchestration, traffic
prioritization, network arbitration and cyberthreats detection, amongst others.
The traditional traffic classifiers became obsolete owing to the rapid Internet
evolution. Port-based classifiers suffer from significant accuracy losses due to port
masking, meanwhile Deep Packet Inspection approaches have severe user-privacy
limitations. The advent of Machine Learning has propelled the application of
advanced algorithms in diverse research areas, and some learning approaches have
proved as an interesting alternative to the classic traffic classification approaches.
Addressing Network Traffic Classification from a Machine Learning perspective
implies numerous challenges demanding research efforts to achieve feasible
classifiers. In this dissertation, we endeavor to formulate and solve important
research questions in Machine-Learning-based Network Traffic Classification. As a
result of numerous experiments, the knowledge provided in this research constitutes
an engaging case of study in which network traffic data from two different
environments are successfully collected, processed and modeled.
Firstly, we approached the Feature Extraction and Selection processes providing our
own contributions. A Feature Extractor was designed to create Machine-Learning
ready datasets from real traffic data, and a Feature Selection Filter based on fast
correlation is proposed and tested in several classification datasets. Then, the
original Network Traffic Classification datasets are reduced using our Selection
Filter to provide efficient classification models. Many classification models based on
CART Decision Trees were analyzed exhibiting excellent outcomes in identifying
various Internet applications. The experiments presented in this research comprise
a comparison amongst ensemble learning schemes, an exploratory study on Class
Imbalance and solutions; and an analysis of IP-header predictors for early traffic
classification. This thesis is presented in the form of compendium of JCR-indexed
scientific manuscripts and, furthermore, one conference paper is included.
In the present work we study a wide number of learning approaches employing the
most advance methodology in Machine Learning. As a result, we identify the
strengths and weaknesses of these algorithms, providing our own solutions to
overcome the observed limitations. Shortly, this thesis proves that Machine
Learning offers interesting advanced techniques that open prominent prospects in
Internet Network Traffic Classification.Departamento de TeorĂa de la Señal y Comunicaciones e IngenierĂa TelemĂĄticaDoctorado en TecnologĂas de la InformaciĂłn y las Telecomunicacione
A Smart Charging Assistant for Electric Vehicles Considering Battery Degradation, Power Grid and User Constraints
Der Anstieg intermittierender Stromerzeugung aus erneuerbaren Energiequellen erschwert zunehmend einen effizienten und zuverlĂ€ssigen Betrieb der Versorgungsnetze. Gleichzeitig steigt die Zahl der Elektrofahrzeuge, die zum Aufladen erhebliche Mengen an elektrischer Energie benötigen, rapide an. Energie- und MobilitĂ€tssektor sind somit unweigerlich miteinander verbunden, was zur Folge hat, dass zuverlĂ€ssige ElektromobilitĂ€t von einer robusten Stromversorgung abhĂ€ngt. DarĂŒber hinaus empfinden Fahrzeugnutzer ihre individuelle MobilitĂ€t als eingeschrĂ€nkt, da Elektrofahrzeuge im Vergleich zu Fahrzeugen mit Verbrennungsmotor derzeit eine geringere Reichweite aufweisen und mehr Zeit zum Aufladen benötigen.
In der vorliegenden Arbeit wird daher ein neuartiges Konzept sowie eine Softwareanwendung (Ladeassistent) vorgestellt, die den Nutzer beim Laden seines Elektrofahrzeuges unterstĂŒtzt und dabei die Interessen aller beteiligten Akteure berĂŒcksichtigt. DafĂŒr werden zunĂ€chst Gestaltungsmerkmale möglicher Softwarearchitekturen verglichen, um eine geeignete Struktur von Modulen und deren VerknĂŒpfung zu definieren. AnschlieĂend werden anhand realer Daten sowohl Energieverbrauchs- als auch Batteriemodelle entwickelt, verbessert und validiert, welche die Fahr- und Ladeeigenschaften von Elektrofahrzeugen abbilden. Die wichtigsten BeitrĂ€ge dieser Arbeit resultieren aus der Entwicklung und Validierung der folgenden drei Kernkomponenten des Ladeassistenten.
Als Erstes wird das individuelle MobilitĂ€tsverhalten der Nutzer modelliert und anhand von aufgezeichneten und halbsynthetischen Fahrdaten von Elektrofahrzeugen ausgewertet. Insbesondere wird ein neuartiger, zweistufiger Clustering-Algorithmus entwickelt, um hĂ€ufig besuchte Orte der Nutzer zu ermitteln. AnschlieĂend werden Ensembles von Random-Forest-Modellen verwendet, um die nĂ€chsten Aufenthaltsorte und die dort typischen Parkzeiten vorherzusagen.
Als Zweites wird gemischt-ganzzahlige stochastische Optimierung angewandt, um Ladestopps in einem zukĂŒnftigen Zeithorizont möglichst komfortabel und kostengĂŒnstig zu planen. Dabei wird ein graphenbasierter Algorithmus eingesetzt, um den Energiebedarf und die Eintrittswahrscheinlichkeit von MobilitĂ€tsszenarien eines Elektrofahrzeugnutzers zu quantifizieren. Zur Validierung werden zwei alternative Ladestrategien definiert und mit dem vorgeschlagenen System verglichen.
Als Drittes wird ein nichtlineares Optimierungsschema entwickelt, um vorhandene Zeit- und EnergieflexibilitĂ€t in LadevorgĂ€ngen von Elektrofahrzeugen zu nutzen. Die Integration eines detaillierten Batteriemodells ermöglicht eine genaue Quantifizierung der Kosteneinsparungen aufgrund einer geringeren Batteriealterung und dynamischer Stromtarife. Anhand von Daten aus realen LadevorgĂ€ngen von Elektrofahrzeugen können EinflĂŒsse auf die RentabilitĂ€t von Vehicle-to-Grid-Anwendungen herausgearbeitet werden. Aus der Umsetzung des vorgestellten Ansatzes in einer realistischen Umgebung geht ein Architekturentwurf und ein Kommunikationskonzept fĂŒr optimierungsbasierte intelligente Ladesysteme hervor. Dabei werden weitere Herausforderungen im Zusammenhang mit standardisierter Ladekommunikation, Eingriffen der Energieversorger und Nutzerakzeptanz aufgedeckt
A New Feature Selection Method Based on Class Association Rule
Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a featureâs relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may be impractical for multiple reasons such as the experience required in the application domain, care, accuracy, and time. In this research, we propose a new hybrid Filtering method called Class Association Rule Filter (CARF) that deals with the aforementioned issues by identifying relevant features through the Class Association Rule Mining approach and then using these rules to define weights for the available features in the training dataset. More crucially, we propose a new procedure based on mutual information within the CARF method which suggests the subset of features to be retained by the end-user, hence reducing time and effort. Empirical evaluation using small, medium, and large datasets that belong to various dissimilar domains reveals that CARF was able to reduce the dimensionality of the search space when contrasted with other common Filtering methods. More importantly, the classification models devised by the different machine learning algorithms against the subsets of features selected by CARF were highly competitive in terms of various performance measures. These results indeed reflect the quality of the subsets of features selected by CARF and show the impact of the new cut-off procedure proposed
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