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1 – 10 of 427Femi Emmanuel Ayo, Olusegun Folorunso, Friday Thomas Ibharalu and Idowu Ademola Osinuga
Hate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with…
Abstract
Purpose
Hate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with social media data has witnessed special research attention in recent studies, hence, the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.
Design/methodology/approach
This study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data. The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency (TF-IDF) for word-level feature extraction and Long Short Term Memory (LSTM) which is a variant of recurrent neural networks architecture for sentence-level feature extraction. The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech, offensive language or neither.
Findings
The proposed method showed better results when tested on the collected Twitter datasets compared to other related methods. In order to validate the performances of the proposed method, t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection. Furthermore, Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.
Research limitations/implications
Finally, the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.
Originality/value
The main novelty of this study is the use of an automatic topic spotting measure based on naïve Bayes model to improve features representation.
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Keywords
The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in…
Abstract
Purpose
The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design.
Design/methodology/approach
An ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks.
Findings
The experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification.
Originality/value
The presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification
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This paper aims to propose a novel nature-inspired optimization algorithm, called whirlpool algorithm (WA), which imitates the physical phenomenon of whirlpool.
Abstract
Purpose
This paper aims to propose a novel nature-inspired optimization algorithm, called whirlpool algorithm (WA), which imitates the physical phenomenon of whirlpool.
Design/methodology/approach
The idea of this algorithm stems from the fact that the whirlpool has a descent direction and a vertex.
Findings
WA is tested with two types of models: 29 typical mathematical optimization models and three engineering problems (tension/compression spring design, welded-beam design, pressure vessel design).
Originality/value
The results shown that the WA is vying compared to the state-of-art algorithms likewise conservative approaches.
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Himanshu Goel and Narinder Pal Singh
Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market…
Abstract
Purpose
Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs.
Design/methodology/approach
The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex.
Findings
The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex.
Research limitations/implications
The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses.
Originality/value
The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.
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The purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and…
Abstract
Purpose
The purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.
Design/methodology/approach
The algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.
Findings
The proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.
Research limitations/implications
In general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.
Practical implications
It is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.
Social implications
Reliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.
Originality/value
The utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.
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Ranjitha K., Sivakumar P. and Monica M.
This study aims to implement an improved version of the Chimp algorithm (IChimp) for load frequency control (LFC) of power system.
Abstract
Purpose
This study aims to implement an improved version of the Chimp algorithm (IChimp) for load frequency control (LFC) of power system.
Design/methodology/approach
This work was adopted by IChimp to optimize proportional integral derivative (PID) controller parameters used for the LFC of a two area interconnected thermal system.
Findings
The supremacy of proposed IChimp tuned PID controller over Chimp optimization, direct synthesis-based PID controller, internal model controller tuned PID controller and recent algorithm based PID controller was demonstrated.
Originality/value
IChimp has good convergence and better search ability. The IChimp optimized PID controller is the proposed controlling method, which ensured better performance in terms of converging behaviour, optimizing controller gains and steady-state response.
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Zakaria Mohamed Salem Elbarbary and Mohamed Abdullrahman Alranini
Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP…
Abstract
Purpose
Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques.
Design/methodology/approach
A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance.
Findings
MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower.
Originality/value
In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.
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Wu Deng, Meng Sun, Huimin Zhao, Bo Li and Chunxiao Wang
This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one…
Abstract
Purpose
This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement.
Design/methodology/approach
In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period.
Findings
In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed.
Practical implications
The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a very meaningful work for airport gate assignment.
Originality/value
An efficient multi-objective optimization model for hub airport gate assignment problem is proposed in this paper. An improved ant colony optimization algorithm based on ant colony collaborative strategy and the pheromone update strategy is deeply studied to speed up the convergence and avoid to fall into the local optimal solution.
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Rama Rao A., Satyananda Reddy and Valli Kumari V.
Multimedia applications such as digital audio and video have stringent quality of service (QoS) requirement in mobile ad hoc network. To support wide range of QoS, complex routing…
Abstract
Purpose
Multimedia applications such as digital audio and video have stringent quality of service (QoS) requirement in mobile ad hoc network. To support wide range of QoS, complex routing protocols with multiple QoS constraints are necessary. In QoS routing, the basic problem is to find a path that satisfies multiple QoS constraints. Moreover, mobility, congestion and packet loss in dynamic topology of network also leads to QoS performance degradation of protocol.
Design/methodology/approach
In this paper, the authors proposed a multi-path selection scheme for QoS aware routing in mobile ad hoc network based on fractional cuckoo search algorithm (FCS-MQARP). Here, multiple QoS constraints energy, link life time, distance and delay are considered for path selection.
Findings
The experimentation of proposed FCS-MQARP is performed over existing QoS aware routing protocols AOMDV, MMQARP, CS-MQARP using measures such as normalized delay, energy and throughput. The extensive simulation study of the proposed FCS-based multipath selection shows that the proposed QoS aware routing protocol performs better than the existing routing protocol with maximal energy of 99.1501 and minimal delay of 0.0554.
Originality/value
This paper presents a hybrid optimization algorithm called the FCS algorithm for the multi-path selection. Also, a new fitness function is developed by considering the QoS constraints such as energy, link life time, distance and delay.
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Kuok King Kuok, Chiu Po Chan and Sobri Harun
Rainfall–runoff relationship is one of the most complex hydrological phenomena. A conventional neural network (NN) with backpropagation algorithm has successfully modelled various…
Abstract
Rainfall–runoff relationship is one of the most complex hydrological phenomena. A conventional neural network (NN) with backpropagation algorithm has successfully modelled various non-linear hydrological processes in recent years. However, the convergence rate of the backpropagation NN is relatively slow, and solutions may trap at local minima. Therefore, a new metaheuristic algorithm named as cuckoo search optimisation was proposed to combine with the NN to model the daily rainfall–runoff relationship at Sungai Bedup Basin, Sarawak, Malaysia. Two-year rainfall–runoff data from 1997 to 1998 had been used for model training, while one-year data in 1999 was used for model validation. Input data used are current rainfall, antecedent rainfall and antecedent runoff, while the targeted output is current runoff. This novel NN model is evaluated with the coefficient of correlation (R) and the Nash–Sutcliffe coefficient (E2). Results show that cuckoo search optimisation neural network (CSONN) is able to yield R and E2 to 0.99 and 0.94, respectively, for model validation with the optimal configuration of number of nests (n) = 20, initial discovery rate of alien eggs (
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