Clustering in Recommendation Systems Using Swarm Intelligence

Ένα σύστημα συστάσεων είναι μία εφαρμογή που εκμεταλλεύεται πληροφορίες για να βοηθήσει τους χρήστες στη λήψη αποφάσεων προτείνοντας αντικείμενα που μπορεί να τους αρέσουν. Ένα σύστημα συστάσεων που βασίζεται στην τεχνική του συνεργατικού φιλτραρίσματος (collaborative filtering) δημιουργεί συστάσεις στους χρήστες με βάση τις προτιμήσεις παρόμοιων χρηστών. Ωστόσο, αυτός ο τύπος συστήματος συστάσεων δεν είναι τόσο αποτελεσματικός όταν τα δεδομένα αυξάνονται σε μεγάλο βαθμό (scalability) ή όταν δεν υπάρχει αρκετή πληροφορία (sparsity), καθώς δεν ομαδοποιούνται σωστά οι παρόμοιοι χρήστες. Αυτή η διπλωματική εργασία προτείνει τρείς υβριδικούς αλγορίθμους που ο καθένας συνδυάζει τον αλγόριθμο k-means με έναν αλγόριθμο ευφυΐας σμήνους για να βελτιώσει την ομαδοποίηση των χρηστών, και κατ’ επέκταση την ποιότητα των συστάσεων. Οι αλγόριθμοι ευφυΐας σμήνους που χρησιμοποιούνται είναι o αλγόριθμος τεχνητής κοινωνίας μελισσών (artificial bee colony), ο αλγόριθμος βελτιστοποίησης αναζήτησης κούκων (cuckoo search optimization) και ο αλγόριθμος βελτιστοποίησης γκρίζων λύκων (grey-wolf optimization). Οι προτεινόμενες μέθοδοι αξιολογήθηκαν χρησιμοποιώντας ένα σύνολο δεδομένων του MovieLens. Η αξιολόγηση δείχνει πως τα προτεινόμενα συστήματα συστάσεων αποδίδουν καλύτερα σε σύγκριση με τις ήδη υπάρχουσες τεχνικές όσον αφορά τις μετρικές του μέσου απόλυτου σφάλματος (mean absolute error - MAE), της ακρίβειας (precision), του αθροίσματος των τετραγωνικών σφαλμάτων (sum of squared errors - SSE) και της ανάκλησης (recall). Επιπλέον, τα αποτελέσματα της αξιολόγησης δείχνουν πως ο υβριδικός αλγόριθμος που χρησιμοποιεί την μέθοδο της τεχνητής κοινωνίας μελισσών αποδίδει ελαφρώς καλύτερα από τους άλλους δύο προτεινόμενους αλγορίθμους.A recommender system (RS) is an application that exploits information to help users in decision making by suggesting items they might like. A collaborative recommender system generates recommendations to users based on their similar neighbor’s preferences. However, this type of recommender system faces the data sparsity and scalability problems making the neighborhood selection a challenging task. This thesis proposes three hybrid collaborative recommender systems that each one combines the k-means algorithm with a different bio-inspired technique to enhance the clustering task, and therefore to improve the recommendation quality. The used bio-inspired techniques are artificial bee colony (ABC), cuckoo search optimization (CSO), and grey-wolf optimizer (GWO). The proposed approaches were evaluated over a MovieLens dataset. The evaluation shows that the proposed recommender systems perform better compared to already existing techniques in terms of mean absolute error (MAE), precision, sum of squared errors (SSE), and recall. Moreover, the experimental results indicate that the hybrid recommender system that uses the ABC method performs slightly better than the other two proposed hybrid algorithms

Apex and fuzzy model assessment of environmental benefits of agroforestry buffers for claypan soils

Contamination of surface waters with pollutants from agricultural land is a major threat to the environment. A field-size watershed study in Northeast Missouri showed that vegetated filter strips containing grass and grass+trees (agroforestry) buffers placed on contours reduced sediment and nutrient loadings by 11-35%. Watershed scale studies are overly expensive while computer simulated hydrologic models offer efficient and economical tools to examine environmental benefits of conservation practices. The current study used the Agricultural Policy Environmental eXtender (APEX) model and a fuzzy logic model to predict environmental benefits of buffers and grass waterways of three adjacent watersheds at the Greenley Memorial Research Center. During the second phase of the study, an automated computer technique was developed to optimize parameter sets for the APEX model for runoff, sediment, total phosphorous (TP) and total nitrogen (TN) losses. The APEX model was calibrated and validated satisfactorily for runoff from both pre- and post-buffer watersheds. The sediment, TP, and TN were calibrated only for larger events during the pre-buffer period (>50 mm). Only TP was calibrated by post-buffer models. The models simulated 13- 25% TP reduction by grass waterways, and 4-5% runoff and 13-45% TP reductions by buffers. The fuzzy model predicted runoff for the study watersheds and for watersheds 30 and 50 times larger in northern Missouri. A stepwise multi-objective, multi-variable parameter optimization technique improved calibration of sediments, TP, and TN after optimization for runoff parameters. The results of the study show that models can be used to examine environmental benefits provided long-term data are available

Ergonomic Models of Anthropometry, Human Biomechanics and Operator-Equipment Interfaces

The Committee on Human Factors was established in October 1980 by the Commission on Behavioral and Social Sciences and Education of the National Research Council. The committee is sponsored by the Office of Naval Research, the Air Force Office of Scientific Research, the Army Research Institute for the Behavioral and Social Sciences, the National Aeronautics and Space Administration, and the National Science Foundation. The workshop discussed the following: anthropometric models; biomechanical models; human-machine interface models; and research recommendations. A 17-page bibliography is included

Mathematical modelling of vanadium-based redox flow batteries

Electrochemical energy storage could facilitate the integration of intermittent renewable sources, such as wind and solar, allowing for a more stable, reliable and flexible electrical grid. Vanadium redox flow batteries (VRFBs) are an attractive technology due to their capability to decouple power and energy, however they have displays limited deployment, which has been limited by cost. Hybrid-type redox flow batteries such as a Regenerative Hydrogen-Vanadium Fuel Cell (RHVFC) could allow to overcome the cost dependency of all-vanadium systems with regards to the vanadium requirements. Modelling and simulation appear as an indispensable tool to support the design and optimisation of these systems, saving time and reducing costs. On the other hand, physical-based models can capture the dependency of the cell performance on the operating conditions and physico-chemical properties. This thesis investigates the performance behaviour of RHVFC by means of mathematical representations of the system. Firstly, the conventional approach involving unit cell modelling for VRFBs is studied and implemented to understand the interplay of different phenomena and the possible similarities with the system of interest. Then, a unit cell model for a RHVFC is proposed, giving special attention to the equilibrium and kinetics equation used in describing the limiting electrode. A complete Nernst equation is derived to estimate the equilibrium potential, while a Butler- Volmer kinetics including the effect of concentration of protons and mass-transport limitations is used to describe the cathodic kinetics. This model is then modified to include the crossover phenomena, by means of a simplified treatment of transport of species in the cation-exchange membrane by means of diffusive, convective and migration mechanisms. The transport of species across the membrane controls the loss in capacity of the cell when continuous cycling operation is tested. This model allowed for the characterisation of a laboratory scale cell of a hydrogen-vanadium system and the simulation of its performance, where extensive experimental data of single-cycle charge-discharge potential, power density and cycling performance was studied. It was observed that the crossover effect was not fully captured for a unit cell model, reproducing the trends during continuous operation but showing some discrepancies with the experimental results. These results indicated the need for a more complex model, such a continuum approach, to describe the transport of species across the electrodes and membrane. Therefore, a time-dependant model considering a Poisson-Nernst-Planck one-dimensional approach to describe the cathode and membrane of a RHVFC was implemented. Initial results allowed to assess the evolution of concentration and potential profiles across the model domains, capturing the interfacial behaviour that appears due to the selectivity of the membrane. These interfacial phenomena produced a steep change in the value of ionic potential and concentrations across a narrow thickness of nano-meters. The model was used to indicate the dependency of crossover fluxes of species across the membrane when the applied current density increases. The mass-transport limitations effects on the cell performance, which were strongly affected by the transport parameters of species, were displayed by the model. This initial crossover model is the first part of a more extensive study of crossover, which will include the testing of the model capability in predicting cell potential over continuous operation, as well as the assessment of alternative modelling approaches such a Donnan-Nernst-Planck model.Open Acces

The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks

This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Automatic message annotation and semantic interface for context aware mobile computing

This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.The Ministry of Higher Education and Scientific Research (IRAQ

Analytical protocols based on high-resolution mass spectrometry for characterizing emerging contaminants and their degradation products in foodstuff and environment

In this PhD thesis, the capability of analytical systems based on high-resolution mass spectrometry (HRMS) has been investigated for the determination of emerging contaminants in environmental matrices and foodstuff. Since the molecular structures of the emerging contaminants could be know as well as unknown, target, suspect and non-target analyses have to be developed in order to propose a “mass-based” advanced screening. Attention has been focused on the scale-up process in the identification confidence by developing different specific protocols. Two protocols based on HPLC/Q-TOF-MS have been developed for the simultaneous screening and confirmatory analysis of target and non-target cyanotoxins in freshwater intended for human consumption, PDE-5 inhibitors and analogues in food supplements marked as erectile dysfunction remedies. Both protocols have been optimized with the aim to obtain HRMS data of pseudomolecular ions and fragmentation patterns in tandem MS mode. In-house databases were implemented to simplify the data treatment. The application of these protocols in “non-target screening” mode has been attempted in real samples and in the frame of a collaborative trial organized by European NORMAN foundation as regard as the analysis of water contaminants. The exercise was complex and time consuming, and it has highlighted the strengths and weaknesses of the developed protocols. The crucial step in non-target screening was the assignment of reliable molecular formula to the m/z values. A specific workflow based on direct infusion and HRMS analysis by using an Orbitrap™ mass spectrometer has been developed for the characterization of PM2.5 organic fraction. The automatization of the data treatment using Mathematica based algorithms was accomplished for studying the chemical composition of PM2.5 organic fraction. Contextually, the possible use of the Atmospheric Pressure Photoionization source for characterizing PM2.5 organic fraction has been investigated on real samples