Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

Contextual Information ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.1369 ◽

2012 ◽

Vol 253-255 ◽

pp. 1369-1373

Author(s):

Tie Jun Wang ◽

Kai Jun Wu

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Cloud Model ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Routing Problem ◽

Searching Ability

Multi-depots vehicle routing problem (MDVRP) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO’s premature convergence and slow astringe, a Cloud Adaptive Particle Swarm Optimization(CAPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In this paper, the algorithm is used to solve MDVRP, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽

10.3390/s20195609 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5609 ◽

Cited By ~ 2

Author(s):

Shahab S. Band ◽

Saeid Janizadeh ◽

Subodh Chandra Pal ◽

Asish Saha ◽

Rabin Chakrabortty ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Particle Swarm ◽

Pso Algorithm ◽

Gully Erosion ◽

Slope Aspect ◽

Swarm Optimization ◽

Proposed Model ◽

Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Modelling and Simulation in Engineering ◽

10.1155/2021/6644339 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yaxiong Li ◽

Xinglong Sun ◽

Xinxue Liu ◽

Jian Wu ◽

Qingguo Liu

Keyword(s):

Optimization Model ◽

Particle Swarm ◽

Pso Algorithm ◽

Convergence Result ◽

Optimization Models ◽

Time Interval ◽

Swarm Optimization ◽

Fuzzy Adaptive

On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.

Part-of-Speech Tagging for Arabic Text using Particle Swarm Optimization and Genetic Algorithm

Recent Advances in Computer Science and Communications ◽

10.2174/2666255814666210114120558 ◽

2021 ◽

Vol 14 ◽

Author(s):

Ahmad T. Al-Taani ◽

Fadi A. ALkhazaaleh

Keyword(s):

Language Processing ◽

Question Answering ◽

Particle Swarm ◽

Pso Algorithm ◽

Arabic Language ◽

Genetic Operators ◽

Swarm Optimization ◽

Pos Tagging ◽

Part Of Speech

Background: Part of Speech (POS) Tagging is a process of defining the suitable part of speech for each word in the given context such as defining if a word is a verb, a noun or a particle. POS tagging is an important preprocessing step in many Natural Language Processing (NLP) applications such as question answering, text summarization, and information retrieval. Objective: The performance of NLP applications depends on the accuracy of POS taggers since assigning right tags for the words in a sentence enables the application to work properly after tagging. Many approaches have been proposed for the Arabic language, but more investigations are needed to improve the efficiency of Arabic POS taggers. Method: In this study, we propose a supervised POS tagging system for the Arabic language using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as well as Hidden Markov Model (HMM). The tagging process is considered as an optimization problem and illustrated as a swarm which consists of group of particles. Each particle represents sequence of tags. The PSO algorithm is applied to find the best sequence of tags which represent the correct tags of the sentence. The genetic operators: crossover and mutation are used to find personal best, global best, and velocity of the PSO algorithm. HMM is used to find the fitness of particles in the swarm. Results : The performance of the proposed approach is evaluated on the KALIMAT dataset which consists of 18 million words and a tag set consists of 45 tags which covers all Arabic POS tags. The proposed tagger achieved an accuracy of 90.5%. Conclusion: Experimental results revealed that the proposed tagger achieved promising results compared to four existing approaches. Other approaches can identify only three tags: noun, verb and particle. Also, the accuracy for some tags are outperformed those achieved by other approaches.

An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1070-1072.297 ◽

2014 ◽

Vol 1070-1072 ◽

pp. 297-302

Author(s):

Zhi Kui Wu ◽

Chang Hong Deng ◽

Yong Xiao ◽

Wei Xing Zhao ◽

Qiu Shi Xu

Keyword(s):

Power System ◽

Real Time ◽

Wind Power ◽

Wind Farm ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Good Convergence ◽

A real-time dispatch (RTD) model for wind power incorporated power system aimed at maximizing wind power utilization and minimizing fuel cost is proposed in this paper. To cope with the prematurity and local convergence of conventional particle swarm optimization (PSO) algorithm, a novel adaptive chaos quantum-behaved particle swarm optimization (ACQPSO) algorithm is put forward. The adaptive inertia weight and chaotic perturbation mechanism are employed to improve the particle’s search efficiency. Numerical simulation on a 10 unit system with a wind farm demonstrates that the proposed model can maximize wind power utilization while ensuring the safe and economic operation of the power system. The proposed ACQPSO algorithm is of good convergence quality and the computation speed can meet the requirement of RTD.

An efficient sentiment analysis methodology based on long short-term memory networks

Complex & Intelligent Systems ◽

10.1007/s40747-021-00436-4 ◽

2021 ◽

Author(s):

J. Shobana ◽

M. Murali

Keyword(s):

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

Contextual Information ◽

Short Term ◽

Good Decision ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory ◽

Current Research Interest

AbstractSentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

Modeling COVID-19 in Iran using Particle Swarm Optimization algorithm

10.1101/2021.04.10.21255244 ◽

2021 ◽

Author(s):

Ebrahim Sahafizadeh ◽

MohammadAli Khajeian

Keyword(s):

Reproduction Number ◽

Particle Swarm ◽

Pso Algorithm ◽

Future Trend ◽

Cumulative Number ◽

Future Trends ◽

Short Term ◽

Swarm Optimization ◽

AbstractBackgroundThe first confirmed cases of COVID-19 in Iran were reported on February 19, 2020. The coronavirus expanded rapidly in all Iranian provinces and three waves of COVID-19 cases have been observed since the pandemic took effect and the fourth wave of Covid-19 cases will likely be observed soon. This study aimed to model the spread of COVID-19 in Iran and to estimate the epidemic parameters and to predict the short-term future trend of COVID-19 in Iran.MethodsWe proposed a modified SEIR epidemic spreading model and we used data from February 20, 2020, to April 9, 2021, on the number of cases reported by Iranian governments to fit the proposed model on the reported data. Particle Swarm Optimization (PSO) algorithm was employed to estimate the parameters of the proposed model and the numerical simulation results were obtained by Runge-Kutta method. The estimated parameters were employed to calculate the effective reproduction number and to predict the short-term future trends of COVID-19 cases.ResultsThe results indicated that the effective reproduction number has increased during Nowruz (Persian New Year) and it was estimated to be 1.28. Considering only two exposed cases as the initial cases in the model, the cumulative number of exposed cases was estimated to be 15,252,372 individuals since the beginning of the outbreak. The prediction of the short-term future trends of COVID-19 cases with different scenarios showed that another peak of the pandemic cases occurs in the next weeks. By immediate lockdown implementation the number of active infected cases was estimated to be 397,585.ConclusionDifferent scenarios of short-term prediction of the future trends of COVID-19 cases indicated that immediate strict social distancing policies need to be implemented to prevent a tremendous burden of the fourth major wave of COVID-19 infections on the health care system of Iran.

2019 International Conference on Sustainable Information Engineering and Technology (SIET) ◽

Milkfish Feed Optimization Using Adaptive Particle Swarm Optimization (PSO) Algorithm

10.1109/siet48054.2019.8986094 ◽

2019 ◽

Author(s):

Beryl Labique Ahmadie ◽

Wanda Athira Luqyana ◽

Wayan Firdaus Mahmudy ◽

Rio Arifando

Keyword(s):

Adaptive Particle Swarm Optimization

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

A Hybrid Global Local Adaptive Particle Swarm Optimization-Based Support Vector Machine Model for Human Facial Authentication

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.1.23 ◽

2020 ◽

Vol 55 (1) ◽

Author(s):

Salam Allawi Hussein ◽

Alyaa Abduljawad Mahmood ◽

Mohammed Iqbal Dohan

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Model ◽

Particle Swarm ◽

Support Vector ◽

Machine Model ◽

Swarm Optimization ◽

Inertia Weight ◽

A new facial authentication model called global local adaptive particle swarm optimization-based support vector machine, was proposed in this paper. The proposed model aimed to solve the problem of finding the preeminent parameters of support vector machine in order to come out with a powerful human facial authentication technique. The conventional particle swarm optimization algorithm was utilized with support vector machine to explore the preeminent parameters of support vector machine. However, the particle swarm optimization support vector machine model has some limitations in selecting the velocity coefficient and inertia weight. One of the best approaches, which is used to solve the velocity coefficient problem, is adaptive acceleration particle swarm optimization. Also, the global-local best inertia weight is used efficiently for selecting the inertia weight. Therefore, the global local adaptive particle swarm optimization-based support vector machine model was proposed based on combining adaptive acceleration particle swarm optimization, global-local best inertia weight, and support vector machine. The proposed model used the principal component analysis approach for feature extraction, as well as global local adaptive particle swarm optimization for finding the preeminent parameters of support vector machine. In the experiments, two datasets (YALEB and CASIAV5) were used, and the suggested model was compared with particle swarm optimization support vector machine and adaptive acceleration particle swarm optimization support vector machine methods. The comparison was via accuracy, computational time, and optimal parameters of support vector machine. Our model can be used for security applications and apply for human facial authentication.

A Tourist Flow Prediction Model for Scenic Areas Based on Particle Swarm Optimization of Neural Network

Revue d intelligence artificielle ◽

10.18280/ria.340403 ◽

2020 ◽

Vol 34 (4) ◽

pp. 395-402

Author(s):

Nan Chen ◽

Yi Liang

Keyword(s):

Neural Network ◽

Prediction Model ◽

Short Term Memory ◽

Service Industry ◽

Particle Swarm ◽

Swarm Optimization ◽

Flow Prediction ◽

Proposed Model ◽

Tourist Flow

In recent years, China has been expanding domestic demand and promoting the service industry. This is a mixed blessing for the further development of tourism. To make accurate prediction of tourist flow, this paper proposes a tourist flow prediction model for scenic areas based on the particle swarm optimization (PSO) of neural network (NN). Firstly, a system of influencing factors was constructed for the tourist flow in scenic areas, and the factors with low relevance were eliminated through grey correlation analysis (GCA). Next, the long short-term memory (LSTM) NN was optimized with adaptive PSO, and used to establish the tourist flow prediction model for scenic areas. After that, the workflow of the proposed model was introduced in details. Experimental results show that the proposed model can effectively predict the tourist flow in scenic areas, and provide a desirable prediction tool for other fields.