Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances in an effort to mitigate the consequences. While techniques to autom...
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ISBN:
(纸本)9781538646595
Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.
The increasing need for reducing the costs and the environmental impact of the energy supply renewed the interest on distributed generation, also favoured by the recent EU directives. Anyway, the solely installation o...
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The increasing need for reducing the costs and the environmental impact of the energy supply renewed the interest on distributed generation, also favoured by the recent EU directives. Anyway, the solely installation of efficient small and medium-size pieces of equipment is not sufficient to achieve the expected targets, being their proper scheduling and management, of course based on the fluctuations of both the loads pattern and the energy prices, the fundamental issue determining their effectiveness. In recent times, several techniques have been proposed with the purpose of optimizing the operation of the installed generators on the basis of load predictions;even if these latter ones heavily affect the performance of the plant management, especially when considering a single prosumer whose behaviour is scarcely predictable with a good accuracy, often this aspect is neglected. The present study aims to analyse how inaccurate load predictions affect the performance of an energy plant whose generators are scheduled by an optimization tool working considering a time span of one day. Different "structures" of error will be modelled and analysed, taking as benchmark load profiles the acquired data for different periods of the year from an office building plant, equipped with a PV plant, two micro-CHP system combined with an absorption chiller, an electric chiller, a gas boiler and a reversible electric heat pump, with thermal storages. The focus will be on the comparison of both the economic and CO2 emission impact, by considering on one side the loads prediction as perfect and on the other side with different entity of errors, with the target to stress the importance of the correct loads prediction issue. (C) 2017 The Authors. Published by Elsevier Ltd.
Artificial bee colony (ABC) algorithm is one of the efficient meta-heuristic optimization algorithm, based on bee behavior for food searching. In order to improve the exploitation ability of ABC algorithm, an elitism ...
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ISBN:
(纸本)9781509064717
Artificial bee colony (ABC) algorithm is one of the efficient meta-heuristic optimization algorithm, based on bee behavior for food searching. In order to improve the exploitation ability of ABC algorithm, an elitism based exploitation strategy is incorporated in ABC and the proposed variant is named as Elitism Based ABC algorithm (EbABC). In the proposed search strategy, the step sizes of the solutions during the solution search process depend on a weighted component, calculated by using three best solutions of the swarm. Further, in order to compensate the exploration, a global search ability is introduced in scout bee stage. To analyze the performance of EbABC, it is applied on 15 standard benchmark functions and the results are compared with some significant variants of ABC. The analysis of the results shows that the proposed EbABC algorithm is a competitive variant of ABC algorithm.
This paper proposes an optimization algorithm named Join Order algorithm Using Predefined Optimal Join Order or is called JAPO algorithm to optimize join cost. Optimal join order solutions for all possible join patter...
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ISBN:
(纸本)9781538619797;9781538619780
This paper proposes an optimization algorithm named Join Order algorithm Using Predefined Optimal Join Order or is called JAPO algorithm to optimize join cost. Optimal join order solutions for all possible join patterns are predefined and stored in a file using Dynamic Programming with Memorization technique or is called DPM algorithm. JAPO algorithm searches join order solutions from the predefined optimal join orders using hash function instead of traversing all search space. Experiments are conducted and join costs obtained by JAPO algorithm are compared with DPM algorithm and greedy algorithm named GOO. The experimental results show that JAPO algorithm with polynomial time complexity obtains almost 100 percent of optimal join order solutions. DPM algorithm obtains 100 percent of optimal join order solutions with factorial time complexity. GOO algorithm with polynomial time complexity obtains sub-optimal solutions and number of optimal solutions obtained by GOO algorithm decreases when number of relations to be joined is increased.
In this study, the convergence speed and fitness function accuracy have been compared with the original algorithm by developing on the Stochastic Fractal Search (SFS) algorithm. Seven classical mathematical benchmark ...
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ISBN:
(纸本)9781538618806
In this study, the convergence speed and fitness function accuracy have been compared with the original algorithm by developing on the Stochastic Fractal Search (SFS) algorithm. Seven classical mathematical benchmark functions used in testing the optimization algorithms in the literature were used in comparison process. In the original SFS algorithm, the Gaussian walk function is used to find new solution points in diffusion process. The step length in this walk decreases as the iteration progresses and a function depending on generation value is used to provide for a more local search. The improvement in this work is the process of adding chaotic map values to this function. According to simulation results, it is observed that seven chaotic map improves the original algorithm from ten chaotic maps applied to SFS algorithm.
Demand Side Management (DSM) strategies are often associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side management is technically dependent on remote and...
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ISBN:
(纸本)9781538626993
Demand Side Management (DSM) strategies are often associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side management is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum solution to the problem of scheduling of household demand side management in the presence of PV generation under a set of technical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid.
The objective of this paper is to demonstrate the opportunities of topology optimization applied to additive technology which will permit to design ultra-light structures practically without regard to the technologica...
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The objective of this paper is to demonstrate the opportunities of topology optimization applied to additive technology which will permit to design ultra-light structures practically without regard to the technological limits. The article gives a brief historical overview of the mutual influence of structures, materials and manufacturing technologies. The additive technology seems to have the broadest opportunities for producing existing structures using conventional materials without design changes. A hypothetical variable density material provides the means to solve an auxiliary problem of optimal material distribution considering stress or stiffness constraints. The special optimization algorithm allows the optimal topology layout to be found which will have a minimum value of the integral characteristic, called "load-carrying factor" (LCF). The LCF is a powerful tool for estimating the perfection limit of any structural and technological solution. Along with the optimal structure, such solutions are difficult for manufacturing using conventional materials and technologies. A creation of real material with the characteristics of hypothetical material is considered as one of the nearest areas of additive technology's development for finding optimal structures. (C) 2017 Published by Elsevier Ltd.
Aiming at the problems in parameter identification of an electronic throttle, this paper proposes a novel hybrid optimization algorithm to search the optimal parameter values of the plant. The parameter identification...
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Aiming at the problems in parameter identification of an electronic throttle, this paper proposes a novel hybrid optimization algorithm to search the optimal parameter values of the plant. The parameter identification of an electronic throttle is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and the optimal parameter values of the plant are searched by using a hybrid optimization algorithm. The proposed hybrid optimization algorithm, effective combination of parallel chaos optimization algorithm (PCOA) and simplex search method, preserves both the global optimization capability of PCOA and the accurate search ability of simplex search method. Simulation and experiment results have shown the good performance of the proposed approach.
Speckle phenomenon in projected images is effectively reduced in a holographic projection display using the method of integration of negatively correlated images implemented by a phase-only spatial light modulator (SL...
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ISBN:
(纸本)9781509062935
Speckle phenomenon in projected images is effectively reduced in a holographic projection display using the method of integration of negatively correlated images implemented by a phase-only spatial light modulator (SLM) with window partition.
The Gravitational search algorithm (GSA) is categorized in swarm intelligence optimization techniques, based on the Newton's law of gravity and motion. In GSA, solution look process relays on the velocity, which i...
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ISBN:
(纸本)9781509047086
The Gravitational search algorithm (GSA) is categorized in swarm intelligence optimization techniques, based on the Newton's law of gravity and motion. In GSA, solution look process relays on the velocity, which is an element of acceleration that decides the step size of the solutions. Due to this component, sometimes the global search process may skip the global optima. So, to avoid this situation, this paper presents a local exploitation based gravitational search algorithm (LEGSA), in which acceleration represented in the form of gravitational field and it reduces iteratively. Due to this, solutions are motivated to exploit more desirable in search space. Further two control parameter, Kbest and gravitational constant are also modified, to give a proper balance amongst exploration and exploitation capabilities. The proposed LEGSA are examined on the 16 different benchmark functions. A Local Exploitation Based GSA (LEGSA) has obvious advantages in comparison with the traditional Gravitational search algorithm (GSA) and Biogeography-based optimization (BBO) algorithm.
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