Adaptive Exponential Bat algorithm and deep learning for big data classification

Mujeeb, Sana; Sam, R. Praveen; Madhavi, K.

doi:10.1007/s12046-020-01521-z

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…The reported results of both methods LC-KNN, and RC-KNN showed better performance when tested on Big datasets. It's also worth highlighting other Big Data classification-related studies, like as [77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,105,108,93,109,110,111,112,113,114,115,114,116,117,118,119,120,121,122,123,124,…”

Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

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There are a plethora of invented classifiers in Machine learning literature, however, there is no optimal classifier in terms of accuracy and time taken to build the trained model, especially with the tremendous development and growth of Big data. Hence, there is still room for improvement. In this paper, we propose a new classification method that is based on the well-known magnetic force. Based on the number of points belonging to a specific class/magnet, the proposed magnetic force (MF) classifier calculates the magnetic force at each discrete point in the feature space. Unknown examples are classified using the magnetic forces recorded in the trained model by various magnets/classes. When compared to existing classifiers, the proposed MF classifier achieves comparable classification accuracy, according to the experimental results utilizing 28 different datasets. More importantly, we found that the proposed MF classifier is significantly faster than all other classifiers tested, particularly when applied to Big datasets and hence could be a viable option for structured Big data classification with some optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Magnetic Force Classifier: A Novel Method for Big Data Classification

et al. 2022

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“…TP, TN, FP, and FN in Eq. ( 6) are true positives, true negatives, false positives, and false negatives, respectively [33]. A confusion matrix has been formed for calculating the performances of the model used in the study.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Deep Network Model and Regression Analysis Using OLS Method for Predicting Lung Vital Capacity

Sümbül¹

2023

Business, Management and Economics

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With the advancement of technology, many new devices and methods with machine learning and artificial intelligence (ML-AI) have been developed and these methods have begun to play an important role in human life. ML-AI technology is now widely used in many applications such as security, military, communications, bioengineering, medical treatment, food industry, and robotics. In this chapter, deep learning methods and medical usage techniques that have become popular in recent years will be discussed. Experimental and simulation results and a comprehensive example of the biomedical use of the deep network model will be presented. In addition, the regression analysis using the ordinary least squares (OLS) method for estimating lung vital capacity (VC) will be discussed. The simulation results showed that the VC parameter was predicted with higher than 90% accuracy using the proposed deep network model with real data.

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“…In the classification, clustering, and forecasting domain, the BA is successfully applicable in several problems such as prediction and classification [ 18 , 20 , 74 , 123 , 125 , 183 , 185 , 195 , 205 , 212 , 245 , 285 , 294 , 307 ], data clustering [ 17 , 27 , 46 , 98 , 223 , 234 , 238 , 295 , 301 , 345 , 346 ], data forecasting [ 39 , 168 , 243 , 335 ], and train the feed forward neural network [ 75 , 171 , 213 ]. These applications are shown in Fig 15 .…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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Adaptive Exponential Bat algorithm and deep learning for big data classification

Cited by 15 publications

References 21 publications

Magnetic Force Classifier: A Novel Method for Big Data Classification

Magnetic Force Classifier: A Novel Method for Big Data Classification

Deep Network Model and Regression Analysis Using OLS Method for Predicting Lung Vital Capacity

Recent advances of bat-inspired algorithm, its versions and applications

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