K. Balanaga Gurunathan, M. Krishnakumar and C. Suganya
Body dimensions differ from person to person as well as generation to generation. It is essential to periodically provide anthropometric surveys to revise the sizing system…
Abstract
Body dimensions differ from person to person as well as generation to generation. It is essential to periodically provide anthropometric surveys to revise the sizing system according to recent changes in the data, in order to allow apparel manufacturers to produce mass customized products. The sizing system has a vital role in apparel fit, which attracts customers to buy ready to wear garments. There will not be quality in apparel unless it satisfactorily fits the potential wearers. Current sizing standards rely on anthropometric data that are decades old. The current generation is apparently taller than its predecessors. People are growing into a race of giants and the future generations are likely to be even bigger. Increasing human sizes will have implications on future homes, clothes, cars and furniture. This study will help to prove this fact through figures obtained by taking the measurements of those above 18 years old and their parents. It will also be helpful to find out the impacts of changes in dimensional growth on clothing consumption.
Details
Keywords
Guiwen Liu, Juma Hamisi Nzige and Kaijian Li
The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose…
Abstract
Purpose
The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose the future outline.
Design/methodology/approach
The paper adopted literature reviews methodology involving 1,057 relevant documents published in 2008-2017 from 15 journals. The selected documents were empirically analyzed through a topic-modeling technique. A latent Dirichlet allocation model was applied to each document to infer 50 key topics. A machine learning for language toolkit was used to get topic posterior word distribution and word composition.
Findings
This is an exploratory study, which identifies the distribution of topics and themes; the trend of topics and themes; journal distribution trends; and comparative topic, themes and journal distribution trend. The distribution and trends show an increase in researcher’s interest and the journal’s priority on OSC research. Nevertheless, OSC existing literature is faced with; under-researched topics such as building information modeling, smart construction and marketing. The under-researched themes include organizational management, supply chain and context. The authors also found an overload of similar information in prefabrication and concrete topics. Furthermore, the innovative methods and constraints themes were found to be overloaded with similar information.
Research limitations/implications
The naming of the themes was based on our own interpretation; hence, the research results may lack generalizability. Therefore, a comparative study using different data processing is proposed. The study also provides future research outline as follows: studying OSC topics from dynamic evolution perspective and identifying the new emerging topics; searching for effective strategies to enhance OSC research; identifying the contribution of countries, affiliation and funding agency; and studying the impact of these themes to the adoption of OSC.
Practical implications
This study is of values to the scholars, as it could stimulate research to under-researched areas.
Originality/value
This paper justifies a need to have a broad understanding of the nature and structure of existing OSC literature.
Details
Keywords
The purpose of this paper is to investigate the feasibility of using artificial neural networks (ANNs) in conjunction with data envelopment analysis (DEA) for the performance…
Abstract
Purpose
The purpose of this paper is to investigate the feasibility of using artificial neural networks (ANNs) in conjunction with data envelopment analysis (DEA) for the performance measurement of major mobile phone providers, and for subsequent predictions related to best performance benchmarking and decision making.
Design/methodology/approach
DEA and ANN are combined, providing an integrated modeling approach via a two-stage process. DEA is used for front end measurement, while ANN provides learning and prediction capabilities. DEA analysis of industry characteristics is based on the measurement of each decision-making unit's (DMU) performance. Back propagation neural networks (BPNN) can then predict each DMU's efficiency score, based on the results of the DEA models. Additional BPNN models provide best performance predictions.
Findings
The DEA module successfully evaluates the competitive status of firms in the mobile phone industry in terms of efficiency. Efficiency trends over the observation period reveal the dynamic nature of competition in this industry. The predictive power of the BPNN module has been demonstrated as well. The proposed system is an effective benchmarking and decision support tool, via its capability to simulate performance scenarios, thereby facilitating insightful, prudent decision making.
Originality/value
This paper proposes the use of two different but complementary methods, DEA and ANN, in a combined performance modeling approach, and examines mobile phone providers. This methodology can improve users’ performance benchmarking and decision-making processes. Additionally, adaptive prediction capability is provided through approximating efficient frontiers, in addition to performance measurement.
Details
Keywords
Arnab Banerjee, Tanusree Dutta and Aditya Shankar Mishra
Handloom products often fail to infiltrate the global or mainland market, resulting in small localized markets, limited demand and profitability. Recent times have also witnessed…
Abstract
Purpose
Handloom products often fail to infiltrate the global or mainland market, resulting in small localized markets, limited demand and profitability. Recent times have also witnessed a decline in the weaving population of India. Assam, accounting for a third of all households engaged in the handloom industry in India, has been widely hit by unemployment, migration and demotivation among weavers due to lack of profitability in the sector. This research aims to study the case of Assam as an exemplar to identify the barriers and cognitive biases impacting the sales of such ethnic apparel and propose nudges as interventions to address such concerns.
Design/methodology/approach
A conjoint-based experimental study was used to understand and compare the cognitive biases of two study groups: an ethnic group from Assam and a non-ethnic group from various Tier I and Tier II cities of India. The groups were exposed to a variety of ethnic Assamese and ethnic non-Assamese products to understand their value perception using conjoint analysis.
Findings
Results indicate a potential lack of cognitive fluency when dealing with Assamese ethnic garments, triggering System II thinking among the non-ethnic (national buyer) group. The underlying cause may be the inability to attribute substitution of the given product for a more familiar product. The results suggest that exposure may lead to priming, which in turn can increase cognitive fluency.
Originality/value
Within the limits of the literature reviewed, designing a conjoint-based experiment and proposing the use of nudge to popularize certain ethnic garments are novel contributions of this study.
Details
Keywords
Suganya Pandi and Pradeep Reddy Ch.
Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high…
Abstract
Purpose
Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption.
Design/methodology/approach
In this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service.
Findings
The results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications.
Originality/value
The computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.
Sukumar Rajendran, Sandeep Kumar Mathivanan, Prabhu Jayagopal, Kumar Purushothaman Janaki, Benjula Anbu Malar Manickam Bernard, Suganya Pandy and Manivannan Sorakaya Somanathan
Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge…
Abstract
Purpose
Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.
Design/methodology/approach
The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).
Findings
The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.
Originality/value
The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.
Details
Keywords
Majid Abdolrazzagh-Nezhad and Shaghayegh Izadpanah
Various methods are used for cancer detection such as genetic tests, scanning, MRI, mammography, etc. These methods help collect data on patients, which can be utilized for…
Abstract
Purpose
Various methods are used for cancer detection such as genetic tests, scanning, MRI, mammography, etc. These methods help collect data on patients, which can be utilized for comparing a new patient’s information with the aggregated data to detect cancer. The main step in this process is data classification. There are several cancer detection methods with their own disadvantages in flexibility, non-linear complexity and sensitive in imbalance data. In this paper, a new fuzzy bio-inspired based classification method is designed to classify the imbalance medical data.
Design/methodology/approach
In this paper, a new fuzzy bio-inspired-based classification method is designed to classify the imbalance of medical data. The method consists of a new fuzzy draft of the Cuckoo Optimization Algorithm (COA) and separating hyper-planes based on assigning binary codes to separated regions that are called Hyper-Planes Classifier (HPC). Based on the technical review is done in the paper, the HPC has a better structural superiority than the other classification algorithms. The Fuzzy Cuckoo Optimization Algorithm (FCOA), which fills up its challenge in proper tuning parameters, is proposed to optimize the weights of the separating hyper-planes with linear complexity time.
Findings
The experimental results were presented in five steps. Step1, the details of the average and the best results of the proposed methods were reported and compared. Step2, the quality of the detection methods with different numbers of hyper-planes were compared. The obtained weights of different numbers of hyper-planes were reported in Step3. Step4, the convergence process of the FCOA and the COA were shown. Step5, the best obtained results were compared with the best reported one in previous literature. The experimental results and the presented comparisons show that the proposed hybrid detection method is comparable to other methods and operates better than them in most cases.
Originality/value
A technical review has been done based on classifying the applied classification methods to cancer detection and analyzing advantages (+) and disadvantages (−) of the methods and their optimizer algorithms. A new fuzzy draft of COA has been designed to dynamically tuning the Egg Laying Radius based on a fuzzy inference system with four fuzzy rules. A novel hybridization of the hyper-planes classification method and the designed FCOA has been proposed to optimize the hyper-planes' weights. The effectiveness of the proposed hybridization has been examined in famous UCI cancer datasets based on one, two, three and four hyper-planes and compared with more than 30 previous researches.
Details
Keywords
Debarshi Mukherjee, Ranjit Debnath, Subhayan Chakraborty, Lokesh Kumar Jena and Khandakar Kamrul Hasan
Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent…
Abstract
Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.
Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.
Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.
Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.
Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.
Details
Keywords
Madhukar Chhimwal, Saurabh Agrawal and Girish Kumar
The circular economy concepts are useful for resource conservation, eliminating waste and enhancing the efficiency of production to improve the sustainability of the system. The…
Abstract
Purpose
The circular economy concepts are useful for resource conservation, eliminating waste and enhancing the efficiency of production to improve the sustainability of the system. The application of CE in Indian manufacturing industry is in nascent stage. India’s manufacturing sector significantly contributes to the economic development of the nation; therefore, this study aims to identify and analyze the sustainability related challenges faced during the implementation of the circularity concept.
Design/methodology/approach
Comprehensive survey of literature and the use of Pareto analysis yield ten significant challenges which are further analyzed using fuzzy-Decision-Making Trial and Evaluation Laboratory approach.
Findings
Findings revealed that noncompliance of environmental laws, revenue generation, design issues owing to technological limitations and less preference to refurbished and reused product are some of the major challenges to the CE practices in the manufacturing industry.
Research limitations/implications
The results will help the researchers and practitioners in strategic decision-making for the improved application of circularity in the production process.
Originality/value
This paper contributes to the identification and prioritization of sustainability-related challenges faced during the implementation of a novel concept by a developing economy.
Details
Keywords
Sara H. Goodman, Matthew Zahn, Tim-Allen Bruckner, Bernadette Boden-Albala, Janet R. Hankin and Cynthia M. Lakon
The study examines health care inequities in viral load testing among hepatitis C (HCV) antibody-positive patients. The analysis predicts whether individual and census tract…
Abstract
Purpose
The study examines health care inequities in viral load testing among hepatitis C (HCV) antibody-positive patients. The analysis predicts whether individual and census tract sociodemographic characteristics impact the likelihood of viral load testing.
Methodology/Approach
This a study of 26,218 HCV antibody-positive patients in Orange County, California, from 2010 to 2020. The case data were matched with the 2017 American Community Survey to help understand the role of neighborhood socioeconomic characteristics in testing for viral load. Multivariable logistic regression was used to predict the probability of ever testing for HCV viral load.
Findings
Thirty-six percent of antibody-positive persons were never viral load tested. The results show inequalities in viral load testing by sociodemographic factors. The following groups were less likely to ever test for viral load than their counterparts: (1) individuals under 65 years old, (2) females, (3) residents of census tracts with lower levels of health insurance enrollment, (4) residents of census tracts with lower levels of government health insurance, and (5) residents of census tracts with a higher proportion of non-white residents.
Research Limitations/Implications
This is a secondary database from public health department reports. Using census tract data raises the issue of the ecological fallacy. Detailed medical records were not available. The results of this study emphasize the social inequality in viral load testing for HCV. These groups are less likely to be treated and cured, and may spread the disease to others.
Originality/Value
This chapter is unique as it combines routinely collected public health department data with census tract level data to examine social inequities associated with lower rates of HCV viral load testing.