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Article
Publication date: 17 February 2022

Prajakta Thakare and Ravi Sankar V.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in…

Abstract

Purpose

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

Design/methodology/approach

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

Findings

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

Originality/value

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 13 December 2021

Wei Yuan, Renfeng Yang, Jianyou Yu, Qunrong Zeng and Zechen Yao

Spray curing has become the preferred curing method for most cement concrete members because of its lower cost and sound effect. However, the spray curing quality of…

Abstract

Purpose

Spray curing has become the preferred curing method for most cement concrete members because of its lower cost and sound effect. However, the spray curing quality of members is vulnerable to random variation environment factors and anthropogenic interferences. This paper aims to introduce the machine learning algorithm into the spray curing system to optimize its control method to improve the spray curing quality of members.

Design/methodology/approach

The critical parameters affecting the spray curing quality of members were collected through experiments, such as the temperature and humidity of the member's surface, the temperature, humidity and wind speed of the environment. The C4.5 algorithm was used as a weak classifier algorithm, and the AdaBoost.M1 algorithm was used to cascade multiple weak classifiers to form a robust classifier according to the collected data.

Findings

The results showed that the model constructed by the AdaBoost.M1 algorithm had achieved higher accuracy and robustness among the two algorithms. Based on the classification model built by the AdaBoost.M1 algorithm, the spray curing system can cause automatic decision-making spray switching according to the member's real-time curing state and environment.

Originality/value

With the classification model constructed by the AdaBoost.M1 algorithm, the spray curing system can overcome the disadvantages that external factors greatly influence the current control method of the spray curing system, and the intelligent control of the spray curing system was realized to a certain extent. This paper provides a reference for applying machine learning algorithms in the intellectual transformation of bridge construction equipment.

Details

Construction Innovation , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 11 February 2021

Krithiga R. and Ilavarasan E.

The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by…

Abstract

Purpose

The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters.

Design/methodology/approach

In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm.

Findings

The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency.

Originality/value

The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 5
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 7 July 2020

Jiaming Liu, Liuan Wang, Linan Zhang, Zeming Zhang and Sicheng Zhang

The primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction…

Abstract

Purpose

The primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction performance of four tree-based ensemble models, i.e. bagging with tree regressors (bagging-decision tree [Bagging-DT]), AdaBoost with tree regressors (Adaboost-DT), random forest (RF) and gradient boosting decision tree (GBDT).

Design/methodology/approach

This study proposed a majority voting feature selection method by combining lasso regression with the Akaike information criterion (AIC) (LR-AIC), lasso regression with the Bayesian information criterion (BIC) (LR-BIC) and RF to select indicators with excellent predictive performance from initial 38 indicators in 5,642 samples. The selected features were deployed to build the tree-based ensemble models. The 10-fold cross-validation (CV) method was used to evaluate the performance of each ensemble model.

Findings

The results of feature selection indicated that age, corpuscular hemoglobin concentration (CHC), red blood cell volume distribution width (RBCVDW), red blood cell volume and leucocyte count are five most important clinical/physical indicators in BG prediction. Furthermore, this study also found that the GBDT ensemble model combined with the proposed majority voting feature selection method is better than other three models with respect to prediction performance and stability.

Practical implications

This study proposed a novel BG prediction framework for better predictive analytics in health care.

Social implications

This study incorporated medical background and machine learning technology to reduce diabetes morbidity and formulate precise medical schemes.

Originality/value

The majority voting feature selection method combined with the GBDT ensemble model provides an effective decision-making tool for predicting BG and detecting diabetes risk in advance.

Article
Publication date: 17 October 2008

Thiago Turchetti Maia, Antônio Pádua Braga and André F. de Carvalho

To create new hybrid algorithms that combine boosting and support vector machines to outperform other known algorithms in selected contexts of binary classification problems.

Abstract

Purpose

To create new hybrid algorithms that combine boosting and support vector machines to outperform other known algorithms in selected contexts of binary classification problems.

Design/methodology/approach

Support vector machines (SVM) are known in the literature to be one of the most efficient learning models for tackling classification problems. Boosting algorithms rely on other classification algorithms to produce different weak hypotheses which are later combined into a single strong hypothesis. In this work the authors combine boosting with support vector machines, namely the AdaBoost.M1 and sequential minimal optimization (SMO) algorithms, to create new hybrid algorithms that outperform standard SVMs in selected contexts. This is achieved by integration with different degrees of coupling, where the four algorithms proposed range from simple black‐box integration to modifications and mergers between AdaBoost.M1 and SMO components.

Findings

The results show that the proposed algorithms exhibited better performance for most problems experimented. It is possible to identify trends of behavior bound to specific properties of the problems solved, where one may hence apply the proposed algorithms in situations where it is known to succeed.

Research limitations/implications

New strategies for combining boosting and SVMs may be further developed using the principles introduced in this paper, possibly resulting in other algorithms with yet superior performance.

Practical implications

The hybrid algorithms proposed in this paper may be used in classification problems with properties that they are known to handle well, thus possibly offering better results than other known algorithms in the literature.

Originality/value

This paper introduces the concept of merging boosting and SVM training algorithms to obtain hybrid solutions with better performance than standard SVMs.

Details

Kybernetes, vol. 37 no. 9/10
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 24 September 2019

Qinghua Liu, Lu Sun, Alain Kornhauser, Jiahui Sun and Nick Sangwa

To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm…

Abstract

Purpose

To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small.

Design/methodology/approach

The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness.

Findings

The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness.

Originality/value

This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

Book part
Publication date: 11 September 2020

D. K. Malhotra, Kunal Malhotra and Rashmi Malhotra

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of…

Abstract

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision trees, AdaBoost, and support vector machines (SVMs) to identify potential bad loans. Our results show that AdaBoost does provide an improvement over simple decision trees as well as SVM models in predicting good credit clients and bad credit clients. To cross-validate our results, we use k-fold classification methodology.

Article
Publication date: 30 October 2018

Shrawan Kumar Trivedi and Prabin Kumar Panigrahi

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification…

Abstract

Purpose

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).

Design/methodology/approach

Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.

Findings

Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.

Research limitations/implications

This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.

Practical implications

In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers.

Originality/value

A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

Details

Journal of Systems and Information Technology, vol. 20 no. 3
Type: Research Article
ISSN: 1328-7265

Keywords

Article
Publication date: 16 January 2007

Pei Jia, Huosheng H. Hu, Tao Lu and Kui Yuan

This paper presents a novel hands‐free control system for intelligent wheelchairs (IWs) based on visual recognition of head gestures.

10152

Abstract

Purpose

This paper presents a novel hands‐free control system for intelligent wheelchairs (IWs) based on visual recognition of head gestures.

Design/methodology/approach

A robust head gesture‐based interface (HGI), is designed for head gesture recognition of the RoboChair user. The recognised gestures are used to generate motion control commands to the low‐level DSP motion controller so that it can control the motion of the RoboChair according to the user's intention. Adaboost face detection algorithm and Camshift object tracking algorithm are combined in our system to achieve accurate face detection, tracking and gesture recognition in real time. It is intended to be used as a human‐friendly interface for elderly and disabled people to operate our intelligent wheelchair using their head gestures rather than their hands.

Findings

This is an extremely useful system for the users who have restricted limb movements caused by some diseases such as Parkinson's disease and quadriplegics.

Practical implications

In this paper, a novel integrated approach to real‐time face detection, tracking and gesture recognition is proposed, namely HGI.

Originality/value

It is an useful human‐robot interface for IWs.

Details

Industrial Robot: An International Journal, vol. 34 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Open Access
Article
Publication date: 19 December 2018

Min Wang, Shuguang Li, Lei Zhu and Jin Yao

Analysis of characteristic driving operations can help develop supports for drivers with different driving skills. However, the existing knowledge on analysis of driving…

Abstract

Purpose

Analysis of characteristic driving operations can help develop supports for drivers with different driving skills. However, the existing knowledge on analysis of driving skills only focuses on single driving operation and cannot reflect the differences on proficiency of coordination of driving operations. Thus, the purpose of this paper is to analyze driving skills from driving coordinating operations. There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature. A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.

Design/methodology/approach

AdaBoost was used to extract features and the combined features method was used to combine two or more different driving operations at the same location.

Findings

A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.

Originality/value

There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature.

Details

Journal of Intelligent and Connected Vehicles, vol. 1 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

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