Critical analysis: bat algorithm-based investigation and application on several domains

Umar, Shahla U.; Rashid, Tarik A.

doi:10.1108/wje-10-2020-0495

Cited by 18 publications

(11 citation statements)

References 80 publications

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“…Further optimization of the models, using techniques such as swarm optimization or bat optimization, may be necessary to achieve even higher accuracy in the future [25]. This study collects and aggregates a financial dataset from multiple social media sources for sentiment analysis to inspect how the utilized preprocessing and the models are performed with the heterogeneous data.…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Ahmad¹,

Umar²

2023

IJCAI

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Recently, extensive research in the field of financial sentiment analysis has been conducted. Sentiment analysis (SA) of any text data denotes the feelings and attitudes of the individual on particular topics or products. It applies statistical approaches with artificial intelligence (AI) algorithms to extract substantial knowledge from a huge amount of data. This study extracts the Sentiment polarity (negative, positive, and neutral) from financial textual data using machine learning and deep learning algorithms. The constructed machine learning model used Multinomial Naïve Bayes (MNB) and Logistic regression (LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results of the MNB and LR obtained a good and very good rate of accuracy respectively. Likewise, the results of RNN, LSTM, and GRU obtained an excellent rate of accuracy. It can be concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy rate. Povzetek: Raziskava analizira finančna besedila z uporabo klasičnega strojnega učenja in globokega učenja z namenom iskanja sentimenta (negativno, pozitivno, nevtralno).

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Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Ahmad¹,

Umar²

2023

IJCAI

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“…Seventh, according to the fitness values of the bat population, the bats are arranged to achieve the best positions for the optimized bat individuals. Eighth, if the maximum number of search iterations is reached or the search accuracy threshold is satisfied, we proceed to the next step [45]. Otherwise, we return to the second step for a new search.…”

Section: Lm-bpnn Improved By Bamentioning

confidence: 99%

Ergonomic Reliability Assessment of VDT System for Operation Design Based on Improved BPNN and HCR under Special Circumstances

Liu¹,

Liu²,

Huang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Ergonomic reliability plays a significant role in the safe operation of devices. With the spread of infectious diseases around the world, in work environments with high loads and high infection rates, medical staff work in a state of high self-protection. The use of visual display terminal (VDT) for medical equipment has undergone fundamental changes, and the traditional medical equipment human-machine interface design needs to be improved. After the completion of design and development, a VDT design enters the experimental testing stage, which has significant limitations for simulating the work of medical staff in the high-load and high-infection environments. The testing cost is high, and subjects face harsh conditions; thus, an ergonomic reliability model that can predict the use of VDT in such special high-infection and high-load circumstances must be established. An ergonomic reliability model based on an improved backpropagation neural network (BPNN) and human cognition reliability (HCR) is proposed for predicting and evaluating operation flows according to medical equipment VDTs. Firstly, a small data sample can be used to train BPNN to generate a network that can ensure suitable accuracy. To prevent the model from falling into local optimal solutions, the bat algorithm is introduced to improve the BPNN. Compared to a traditional BPNN, the superiority of the improved BPNN is clearly demonstrated. Secondly, the HCR method is used to analyze and highlight changes in the human factor reliability of VDTs for medical equipment in different time processes and operating processes according to BPNN prediction results, to provide a reference for selecting the optimal method. Finally, the validity and availability of the proposed method are verified through an eye tracker experiment and statistical analysis results.

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“…In the first iteration, each bat is allocated an arbitrary frequency number, and the bats are moved to their new location using their new speed. As the bats approach their prey ( 𝐴 𝑖 ) decreases while the value of (𝑟 𝑖 ) increases as shown in the following two equations [49].…”

Section: Bat Algoritm (Ba)mentioning

confidence: 99%

A Short Review on the Optimization Methods Using for Distributed Generation Planning

2023

ijSmartGrid

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Industrial and social developments increase the need for energy. Due to the fact that fossil fuels will run out to meet energy needs, alternative technologies have been sought. Interest in renewable energy sources has increased. However, since renewable energy sources vary according to geographical conditions, their continuity must be ensured. Therefore, the focus has been on distributed generation systems. The components of distributed generation systems are divided into renewable, non-renewable and storage systems. Technical difficulties may arise in the connection of distributed generation systems with renewable resources. Therefore, it is very important to optimize distributed generation so that the distributed grid provides the expected power. Optimization systems are emphasized so as to decrease efficiency, reduce costs and cut down power fluctuations in distributed generation systems. In this study, distributed generation system, renewable energy source and energy storage system are mentioned. Metaheuristic methods for the efficiency of an energy system are studied. Evolution based algorithms can only keep the search space information in the iteration found, while swarm based algorithms can store the search space information throughout the iteration. Most metaheuristic algorithms use the biological cycle process, swarm behaviour, and physical laws. Evolution based and swarm-based optimization methods are examined in metaheuristic methods. As evolution based metaheuristic methods, genetic algorithm and swarm based optimization methods particle swarm optimization, ant colony optimization, grey wolf optimization, bat algorithm, whale optimization algorithm, cuckoo search algorithm are discussed. A general comparison of the investigated optimization methods is presented. It was concluded that the swarm optimization methods examined were able to make fast convergence and avoid local minima and computational efficiency evaluations.

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Critical analysis: bat algorithm-based investigation and application on several domains

Cited by 18 publications

References 80 publications

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Ergonomic Reliability Assessment of VDT System for Operation Design Based on Improved BPNN and HCR under Special Circumstances

A Short Review on the Optimization Methods Using for Distributed Generation Planning

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