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Article
Publication date: 17 March 2021

Eslam Mohammed Abdelkader

Cracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the…

Abstract

Purpose

Cracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.

Design/methodology/approach

This paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.

Findings

It was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.

Originality/value

Literature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.

Details

Smart and Sustainable Built Environment, vol. 11 no. 3
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 15 December 2017

Farshid Abdi, Kaveh Khalili-Damghani and Shaghayegh Abolmakarem

Customer insurance coverage sales plan problem, in which the loyal customers are recognized and offered some special plans, is an essential problem facing insurance companies. On…

Abstract

Purpose

Customer insurance coverage sales plan problem, in which the loyal customers are recognized and offered some special plans, is an essential problem facing insurance companies. On the other hand, the loyal customers who have enough potential to renew their insurance contracts at the end of the contract term should be persuaded to repurchase or renew their contracts. The aim of this paper is to propose a three-stage data-mining approach to recognize high-potential loyal insurance customers and to predict/plan special insurance coverage sales.

Design/methodology/approach

The first stage addresses data cleansing. In the second stage, several filter and wrapper methods are implemented to select proper features. In the third stage, K-nearest neighbor algorithm is used to cluster the customers. The approach aims to select a compact feature subset with the maximal prediction capability. The proposed approach can detect the customers who are more likely to buy a specific insurance coverage at the end of a contract term.

Findings

The proposed approach has been applied in a real case study of insurance company in Iran. On the basis of the findings, the proposed approach is capable of recognizing the customer clusters and planning a suitable insurance coverage sales plans for loyal customers with proper accuracy level. Therefore, the proposed approach can be useful for the insurance company which helps them to identify their potential clients. Consequently, insurance managers can consider appropriate marketing tactics and appropriate resource allocation of the insurance company to their high-potential loyal customers and prevent switching them to competitors.

Originality/value

Despite the importance of recognizing high-potential loyal insurance customers, little study has been done in this area. In this paper, data-mining techniques were developed for the prediction of special insurance coverage sales on the basis of customers’ characteristics. The method allows the insurance company to prioritize their customers and focus their attention on high-potential loyal customers. Using the outputs of the proposed approach, the insurance companies can offer the most productive/economic insurance coverage contracts to their customers. The approach proposed by this study be customized and may be used in other service companies.

Article
Publication date: 13 January 2021

Manish Sinha and Divyank Srivastava

With the current pandemic situation, the world is shifting to online buying and therefore the purpose of this study is to understand how the industry can improve sales based on…

Abstract

Purpose

With the current pandemic situation, the world is shifting to online buying and therefore the purpose of this study is to understand how the industry can improve sales based on the product recommendations shown on their online platforms.

Design/methodology/approach

This paper has studied content-based filtering using decision trees algorithm and collaborative filtering using K-nearest neighbour algorithm and measured their impact on sales of product of different genres on e-commerce websites and if their recommendation causes a difference in sales.This paper has conducted a field experiment to analyse the customer frequency, change in sales caused by different algorithms and also tried analysing the change in buying preferences of customers in post-pandemic situation and how this paper can improve on the search results by incorporating them in the already used algorithms.

Findings

This study indicates that different algorithms cause differences in sales and score over each other depending upon the category of the product sold. It also suggests that post-Covid, the buying frequency and the preferences of consumers have changed significantly.

Research limitations/implications

The study is limited to existing users of these sites, it also requires the sites to have a huge database of active users and products. Also, the preferences and likings of Indian subcontinent might not generally apply everywhere else.

Originality/value

This study enables better insight into consumer behaviour, thus enabling the data scientists to design better algorithms and help the companies improve their product sales.

Details

International Journal of Innovation Science, vol. 13 no. 2
Type: Research Article
ISSN: 1757-2223

Keywords

Article
Publication date: 1 April 1978

JOSEF KITTLER

All the modified Nearest Neighbour methods of pattern classification2–6 developed to reduce the amount of computer storage and time needed for the implementation of a NN…

Abstract

All the modified Nearest Neighbour methods of pattern classification2–6 developed to reduce the amount of computer storage and time needed for the implementation of a NN classifier require prohibitively costly data preprocessing which involves detailed examination of the neighbouring points to the elements of the reference set. In this paper a method for determining k‐nearest neighbours to a given point is described. The method uses the computationally efficient city block distance to select candidate points for the set of k‐nearest neighbours. In this way the preprocessing time is considerably reduced.

Details

Kybernetes, vol. 7 no. 4
Type: Research Article
ISSN: 0368-492X

Open Access
Article
Publication date: 28 July 2020

Harleen Kaur and Vinita Kumari

Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other…

11147

Abstract

Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affected by this disease. Early detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions. In the current research we have utilized machine learning technique in Pima Indian diabetes dataset to develop trends and detect patterns with risk factors using R data manipulation tool. To classify the patients into diabetic and non-diabetic we have developed and analyzed five different predictive models using R data manipulation tool. For this purpose we used supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-nearest neighbour (k-NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR).

Article
Publication date: 1 August 2005

Songbo Tan

With the ever‐increasing volume of text data via the internet, it is important that documents are classified as manageable and easy to understand categories. This paper proposes…

Abstract

Purpose

With the ever‐increasing volume of text data via the internet, it is important that documents are classified as manageable and easy to understand categories. This paper proposes the use of binary k‐nearest neighbour (BKNN) for text categorization.

Design/methodology/approach

The paper describes the traditional k‐nearest neighbor (KNN) classifier, introduces BKNN and outlines experiemental results.

Findings

The experimental results indicate that BKNN requires much less CPU time than KNN, without loss of classification performance.

Originality/value

The paper demonstrates how BKNN can be an efficient and effective algorithm for text categorization. Proposes the use of binary k‐nearest neighbor (BKNN ) for text categorization.

Details

Online Information Review, vol. 29 no. 4
Type: Research Article
ISSN: 1468-4527

Keywords

Book part
Publication date: 31 December 2010

Dominique Guégan and Patrick Rakotomarolahy

Purpose – The purpose of this chapter is twofold: to forecast gross domestic product (GDP) using nonparametric method, known as multivariate k-nearest neighbors method, and to…

Abstract

Purpose – The purpose of this chapter is twofold: to forecast gross domestic product (GDP) using nonparametric method, known as multivariate k-nearest neighbors method, and to provide asymptotic properties for this method.

Methodology/approach – We consider monthly and quarterly macroeconomic variables, and to match the quarterly GDP, we estimate the missing monthly economic variables using multivariate k-nearest neighbors method and parametric vector autoregressive (VAR) modeling. Then linking these monthly macroeconomic variables through the use of bridge equations, we can produce nowcasting and forecasting of GDP.

Findings – Using multivariate k-nearest neighbors method, we provide a forecast of the euro area monthly economic indicator and quarterly GDP, which is better than that obtained with a competitive linear VAR modeling. We also provide the asymptotic normality of this k-nearest neighbors regression estimator for dependent time series, as a confidence interval for point forecast in time series.

Originality/value of chapter – We provide a new theoretical result for nonparametric method and propose a novel methodology for forecasting using macroeconomic data.

Details

Nonlinear Modeling of Economic and Financial Time-Series
Type: Book
ISBN: 978-0-85724-489-5

Keywords

Article
Publication date: 11 July 2019

Yazhong Deng

The purpose of this study was to establish a massive online open course (MOOC)-based map of higher education knowledge and apply it to university libraries. It hoped to provide…

Abstract

Purpose

The purpose of this study was to establish a massive online open course (MOOC)-based map of higher education knowledge and apply it to university libraries. It hoped to provide more targeted and personalized learning services for every learner.

Design/methodology/approach

In this study, MOOC and university library information services were outlined, the development status of MOOC at home and abroad and the development of university library information services were introduced, and the necessity and significance of MOOC in developing information services in university libraries were analyzed. What is more, the knowledge map of university libraries was explored. The four modules include the construction of data sets, the identification of related entities from plain text, the extraction of entity relationships and the practical application of knowledge maps. For the logical relationship of the course, a combination of knowledge base and machine learning was adopted. In the knowledge map application module, the knowledge map was visualized. Aiming at the generation of personalized learning scheme, a prior data set was constructed by means of the knowledge base. The original problem was considered as a multi-classification problem. K-nearest neighbor classifier divided all courses into four academic years to obtain all courses. According to the course stage, the personalized learning scheme of some majors in higher education was obtained.

Findings

The experiment showed that it was feasible to apply the higher education knowledge map based on MOOC to university libraries. In addition, it was effective to divide the course into four stages by classifier. In this way, the specific professional training program can be obtained, the information service of the university library can be improved, and the accuracy and richness of the entire learning program can be increased.

Research limitations/implications

Due to the limitations of conditions, time and other aspects, there were not many opportunities to visit the field library, which led to limited level and imperfect research. There were many proper nouns and professional terms in foreign references, but my English translation ability was limited. The relevant investigation on foreign studies may not be detailed and comprehensive enough, and the analysis and induction of influencing factors of university library information service may not be rigorous and concise enough.

Practical implications

As the base of university information dissemination, the university library is the source of knowledge. At the same time, it is also the temple of students’ independent learning and the media of mainstream culture and improving its own information service level is also in line with the trend of The Times. Under this background, this research studied the influence of MOOC on university library information service and focused on the challenges and opportunities faced by university library information service in the MOOC environment, so as to continuously improve its cultural serviceability and better serve teachers and students.

Originality/value

Since the birth of MOOC, they have exerted great influence and enlightenment on universities and relevant educational institutions within a few years. European and American universities take an active part in the construction of the MOOC platform and explore how to make better use of the library to build MOOC resources in practice. It is also a hot topic for university libraries to participate in the construction of MOOC information resources. Therefore, the study of this topic has both theoretical and practical significance.

Details

The Electronic Library, vol. 37 no. 5
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 23 May 2018

Wei Zhang, Xianghong Hua, Kegen Yu, Weining Qiu, Shoujian Zhang and Xiaoxing He

This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the…

Abstract

Purpose

This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry.

Design/methodology/approach

The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point.

Findings

Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints.

Research limitations/implications

Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable.

Practical implications

The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system.

Social implications

The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services.

Originality/value

A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

Article
Publication date: 13 February 2024

Aleena Swetapadma, Tishya Manna and Maryam Samami

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the…

Abstract

Purpose

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Design/methodology/approach

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

Findings

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

Originality/value

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

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