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Article
Publication date: 4 June 2024

Akhil Kumar and R. Dhanalakshmi

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7…

132

Abstract

Purpose

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

Design/methodology/approach

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

Findings

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

Originality/value

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 3
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 2 November 2023

Khaled Hamed Alyoubi, Fahd Saleh Alotaibi, Akhil Kumar, Vishal Gupta and Akashdeep Sharma

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from…

224

Abstract

Purpose

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.

Design/methodology/approach

The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.

Findings

On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.

Originality/value

The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

Details

Robotic Intelligence and Automation, vol. 43 no. 6
Type: Research Article
ISSN: 2754-6969

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Article
Publication date: 28 June 2022

Akhil Kumar

This work aims to present a deep learning model for face mask detection in surveillance environments such as automatic teller machines (ATMs), banks, etc. to identify persons…

299

Abstract

Purpose

This work aims to present a deep learning model for face mask detection in surveillance environments such as automatic teller machines (ATMs), banks, etc. to identify persons wearing face masks. In surveillance environments, complete visibility of the face area is a guideline, and criminals and law offenders commit crimes by hiding their faces behind a face mask. The face mask detector model proposed in this work can be used as a tool and integrated with surveillance cameras in autonomous surveillance environments to identify and catch law offenders and criminals.

Design/methodology/approach

The proposed face mask detector is developed by integrating the residual network (ResNet)34 feature extractor on top of three You Only Look Once (YOLO) detection layers along with the usage of the spatial pyramid pooling (SPP) layer to extract a rich and dense feature map. Furthermore, at the training time, data augmentation operations such as Mosaic and MixUp have been applied to the feature extraction network so that it can get trained with images of varying complexities. The proposed detector is trained and tested over a custom face mask detection dataset consisting of 52,635 images. For validation, comparisons have been provided with the performance of YOLO v1, v2, tiny YOLO v1, v2, v3 and v4 and other benchmark work present in the literature by evaluating performance metrics such as precision, recall, F1 score, mean average precision (mAP) for the overall dataset and average precision (AP) for each class of the dataset.

Findings

The proposed face mask detector achieved 4.75–9.75 per cent higher detection accuracy in terms of mAP, 5–31 per cent higher AP for detection of faces with masks and, specifically, 2–30 per cent higher AP for detection of face masks on the face region as compared to the tested baseline variants of YOLO. Furthermore, the usage of the ResNet34 feature extractor and SPP layer in the proposed detection model reduced the training time and the detection time. The proposed face mask detection model can perform detection over an image in 0.45 s, which is 0.2–0.15 s lesser than that for other tested YOLO variants, thus making the proposed detection model perform detections at a higher speed.

Research limitations/implications

The proposed face mask detector model can be utilized as a tool to detect persons with face masks who are a potential threat to the automatic surveillance environments such as ATMs, banks, airport security checks, etc. The other research implication of the proposed work is that it can be trained and tested for other object detection problems such as cancer detection in images, fish species detection, vehicle detection, etc.

Practical implications

The proposed face mask detector can be integrated with automatic surveillance systems and used as a tool to detect persons with face masks who are potential threats to ATMs, banks, etc. and in the present times of COVID-19 to detect if the people are following a COVID-appropriate behavior of wearing a face mask or not in the public areas.

Originality/value

The novelty of this work lies in the usage of the ResNet34 feature extractor with YOLO detection layers, which makes the proposed model a compact and powerful convolutional neural-network-based face mask detector model. Furthermore, the SPP layer has been applied to the ResNet34 feature extractor to make it able to extract a rich and dense feature map. The other novelty of the present work is the implementation of Mosaic and MixUp data augmentation in the training network that provided the feature extractor with 3× images of varying complexities and orientations and further aided in achieving higher detection accuracy. The proposed model is novel in terms of extracting rich features, performing augmentation at the training time and achieving high detection accuracy while maintaining the detection speed.

Details

Data Technologies and Applications, vol. 57 no. 1
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 12 February 2018

Jenarthanan MP, Ramesh Kumar S. and Akhilendra Kumar Singh

This paper aims to perform an experimental investigation on the impact strength, compressive strength, tensile strength and flexural strength of fly ash-based green composites and…

124

Abstract

Purpose

This paper aims to perform an experimental investigation on the impact strength, compressive strength, tensile strength and flexural strength of fly ash-based green composites and to compare with these polyvinyl chloride (PVC), high density polyethylene (HDPE) and low density polyethylene (LDPE).

Design/methodology/approach

Fly ash-based polymer matrix composites (FA-PMCs) were fabricated using hand layup method. Composites containing 100 g by weight fly ash particles, 100 g by weight brick dust particles and 50 g by weight chopped glass fiber particles were processed. Impact strength, compressive strength, tensile strength and flexural strength of composites have been measured and compared with PVC, HDPE and LDPE. Impact strength of the FA-PMC is higher than that of PVC, HDPE and LDPE. Structural analysis of pipes, gears and axial flow blade was verified using ANSYS. Barlou’s condition for pipes, Lewis–Buckingham approach for gears and case-based analysis for axial flow blades were carried out and verified.

Findings

Pipes, gears and axial flow blades made form fly ash-based composites were found to exhibit improved thermal resistance (i.e. better temperature independence for mechanical operations), higher impact strength and longer life compared to those made from PVC, HDPE and LDPE. Moreover, the eco-friendly nature of the raw materials used for fabricating the composite brings into its quiver a new dimension of appeal.

Originality/value

Experimental investigation on the impact strength, compressive strength, tensile strength and flexural strength of fly ash-based green composites has not been attempted yet.

Details

World Journal of Engineering, vol. 15 no. 1
Type: Research Article
ISSN: 1708-5284

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Article
Publication date: 23 July 2024

Rohit Raj, Vimal Kumar, Nagendra Kumar Sharma and Pratima Verma

The purpose of this study is to examine how Industry 4.0 (I4.0) implementation might improve marketing performance (MP). Early adopters now have the chance to capitalize on the…

190

Abstract

Purpose

The purpose of this study is to examine how Industry 4.0 (I4.0) implementation might improve marketing performance (MP). Early adopters now have the chance to capitalize on the advantages of this successful implementation owing to the transition to I4.0. To improve MP, businesses must be able to identify and manage their effective implementation of I4.0 technologies, which are essential to improve industrial performance.

Design/methodology/approach

A survey was created and sent to 311 samples of manufacturing companies. To investigate the hypothesis created in this context, the study includes a survey-based analysis. To present the study’s findings, partial least squares-structural equation modeling has been used.

Findings

According to the findings, it can be concluded that an efficient implementation of Industry 4.0 (EII) can improve MP by positively impacting consumer loyalty and increasing customer loyalty (CL) positively enhancing by product customization (PC). The study’s key results, however, are how both PC and CL affect MP.

Research limitations/implications

The intensive production technologies that are at the center of I4.0 will be better understood by professionals thanks to this study. The Internet of Things, artificial intelligence, additive manufacturing, sophisticated robots and many more are examples of these technologies. I4.0’s application strengthens efficiency and high-quality production. The I4.0 concept is gaining popularity in both developed and emerging countries due to its higher performance. Additionally, business people are actively working to implement I4.0 and make it a big success.

Originality/value

The study identifies the successful adoption of I4.0 that has a substantial impact on businesses’ MP. However, there is a lack of noteworthy studies that can concentrate on the marketing reach with I4.0 deployment. As a result, the goal of the current research is to comprehend how I4.0 will affect MP.

Details

Journal of Business & Industrial Marketing, vol. 39 no. 10
Type: Research Article
ISSN: 0885-8624

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Article
Publication date: 30 December 2024

Rohit Raj, Vimal Kumar, Arpit Singh and Pratima Verma

This study aims to investigate the relationship between patient satisfaction (PS) and the parameters in healthcare and supply chain management (HLSCM).

37

Abstract

Purpose

This study aims to investigate the relationship between patient satisfaction (PS) and the parameters in healthcare and supply chain management (HLSCM).

Design/methodology/approach

The structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) method have been employed to identify correlation and possible configuration of causal factors that influence PS, including lack of resilience (LS), lack of visibility (LV), cost management (CM) and integration and interoperability (II).

Findings

The results from SEM confirmed that PS is highly correlated with lack of visibility, CM and II as critical parameters. Moreover, fsQCA findings state that the configuration of high levels of both resilience and lack of visibility, as well as high levels of II, are crucial for PS.

Research limitations/implications

The researchers also identified the configuration of factors that lead to low PS. The study’s results could assist healthcare providers in improving their supply chain operations, resulting in more effective and efficient healthcare service delivery and ultimately improving PS.

Originality/value

The fsQCA method used in the study provides a more nuanced understanding of the complex interplay between these factors. The inclusion of supply chain management characteristics as parameters in the evaluation of PS is a novel aspect of this research. Previous studies largely focused on more traditional factors such as physical care, waiting times and hospital amenities. By considering supply chain management factors, this study provides insights into an under-explored area of PS research, which has important implications for healthcare providers looking to improve their operations and PS.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 24 October 2024

Sumanjeet Singh, Rohit Raj, Bishnu Mohan Dash, Vimal Kumar, Minakshi Paliwal and Sonam Chauhan

The present study aims to investigate the factors of loan access that affect entrepreneurial self-efficacy (ESE) and operating efficiency of Indian Micro, Small and Medium…

93

Abstract

Purpose

The present study aims to investigate the factors of loan access that affect entrepreneurial self-efficacy (ESE) and operating efficiency of Indian Micro, Small and Medium Enterprises (MSMEs). Furthermore, the study intended to investigate the influence of ESE on the operating efficiency of Indian MSMEs and its mediating role.

Design/methodology/approach

In this study, exploratory research design is used. The study heavily relies on the primary data which has been collected by using the survey research method from a cross-section of 617 women-owned MSMEs, located in urban, rural, suburban and exurban areas of Haryana, Uttarakhand, Himachal Pradesh and NCR-Delhi. The partial least square structural equation modeling method version 3.3.3 has been used to evaluate.

Findings

In terms of the selected factors affecting access to finance, it has been established that the Loan Formalities, Banking Process, Loan Process, Staff Responsiveness and Incentive Scheme have a positive and significant influence in enhancing accessibility to finance and improving the self-efficacy and operating performance of firms. The findings also show that ESE mediates the relationship between various factors of loan access and the operating efficiency of MSMEs.

Research limitations/implications

The study’s findings show that entrepreneurial capacity is significantly and favorably impacted by attitudes toward entrepreneurship, ESE, perceived access to findings and business operations. It has also been demonstrated that entrepreneurial intentions are strongly and favorably influenced by entrepreneurial ability to access commercial bank financing for small businesses and the impact of the same on the women-owned MSMEs in India. It also revealed unfavorable loan terms, limited collateral, fear of repaying of loan and intricate loan application were among the many reasons for loan denial.

Originality/value

The study offers a comprehensive approach that simultaneously considers financial accessibility and ESE. This all-encompassing method offers a thorough grasp of the variables affecting MSMEs' operational efficiency (OE). In contrast to earlier research that might have concentrated only on direct relationships, this study explores the mediating mechanisms involved. This study examines how ESE modulates the influence of financing availability on OE, providing a comprehensive understanding of the underlying mechanisms. By taking into account particular MSME sector characteristics like size, industry or regional variations, the study may provide a unique contextual lens. Understanding how these contextual factors interact with entrepreneurial attributes and access to finance adds depth to the analysis.

Details

Journal of Small Business and Enterprise Development, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1462-6004

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Article
Publication date: 12 August 2024

Sumanjeet Singh, Dhani Shanker Chaubey, Rohit Raj, Vimal Kumar, Minakshi Paliwal and Seema Mahlawat

This study explores the intricate relationship between social media communication, consumer attitude and purchase intention within the context of lifestyle category products. With…

636

Abstract

Purpose

This study explores the intricate relationship between social media communication, consumer attitude and purchase intention within the context of lifestyle category products. With the rapid proliferation of social media platforms, businesses have turned to these platforms to connect with consumers and influence their purchasing decisions. This study aims to provide an in-depth analysis of how social media communication strategies impact consumer attitudes and, in turn, influence purchase intentions.

Design/methodology/approach

The study employs partial least squares structural equation modeling (PLS-SEM) to analyze the data collected from a sample of consumers.

Findings

The results of this study present that lack of visibility (LV), low-efficiency levels (LEL) and unpredictable elements (UE) are ranked as the top three major risk hurdles whereas real-time information on a package’s location (LV1), putting a GPS tracking system to track last-mile journey (OT3) and users wants on time location of their package (LV2) are ranked as top three most significant criteria affecting the practices of modern last-mile logistics in e-commerce businesses.

Research limitations/implications

The results of this study contribute to our understanding of how social media influences consumer behavior in the lifestyle product sector, shedding light on the underlying mechanisms that drive consumer purchasing decisions.

Originality/value

By constructing and testing experimentally a research model that reveals a thorough analysis of pertinent literature and identifies multiple important elements influencing consumer behavior in the lifestyle category, this paper adds to the body of knowledge on marketing. Practical ramifications for lifestyle firms are examined, along with suggestions for improving their social media tactics, in light of the findings.

Details

Marketing Intelligence & Planning, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-4503

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Article
Publication date: 12 November 2024

Shih-Hao Lu, Rohit Raj, Anupama Mahajan, Ajay Jha, Priyanka Verma, Hsia-Ping Lan and Sumanjeet Singh

The study aims to add to the existing literature on food supply chains by specifically taking into the redesigning of the alignment of storage, packaging and distribution…

79

Abstract

Purpose

The study aims to add to the existing literature on food supply chains by specifically taking into the redesigning of the alignment of storage, packaging and distribution practices in the modern complex supply chain. The redesign of the food supply chain’s storage, distribution and packaging is a transformative endeavor ultimately aimed at enhancing efficiency, sustainability and reliability.

Design/methodology/approach

In order to identify, classify and prioritize the main challenges, this study conducted an extensive analysis of the literature and experts’ opinions in the areas of academia, information technology and the food supply chain (FSC) using combined compromise solution method (CoCoSo) and complex proportional assessment (COPRAS).

Findings

The top three classes of key indicators revealed in this study are dynamic route optimization and on-demand delivery pods (RD4), implementation of active packaging with nanotechnology (RP3) and collaborative last-mile (RD2). The findings reveal that dynamic route optimization and on-demand delivery pods (RD4) and collaborative last-mile (RD2) are maintaining a balance between collaborative delivery networks through route optimization which is a very discussable theme in recent literature.

Originality/value

The research provides fresh insights into how perishable food shelf life parameters and the use of distribution networks within the short supply chain can be taken into consideration when redesigning the storage, packaging and distribution system for food supply chains.

Details

British Food Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0007-070X

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Case study
Publication date: 23 September 2024

Mitali Tiwari

After completion of the case study, students will be able to understand the format of for-profit social enterprise working for menstrual hygiene sustainability and its…

Abstract

Learning outcomes

After completion of the case study, students will be able to understand the format of for-profit social enterprise working for menstrual hygiene sustainability and its contribution toward U.N. Sustainable Development Goals, to appreciate the company’s alignment with the triple bottom line framework, to analyze the blue ocean mechanism that the company has developed to create an impact and to critique the strategies the Asan Cups company could adopt to increase its market share and growth.

Case overview/synopsis

Asan Cups was a for-profit social enterprise founded by Ira Guha in 2021. The company crafted reusable menstrual cups from liquid silicone, sporting a patented design in India, the UK, Europe and the USA. Successfully retailing its products in India, the UK and Europe, Asan Cups operated on a bootstrap model with a compact team of four, led by its visionary founder. From the get-go, the company embraced a compelling 1-for-1 donation initiative. For every cup sold, Asan Cups generously donated another to women who could not afford it. Collaborating with nongovernmental organizations, schools, educational institutions and social workers, the company spearheaded campaigns to heighten menstrual hygiene awareness. This proactive approach aimed to boost the acceptance of menstrual cups among rural women and championed the cause of environmental sustainability. The company did not just stop at providing an eco-friendly alternative. Asan Cups fervently educated the masses on the detrimental environmental impact of traditional disposable period products like sanitary pads and tampons. Fast-forwarding to 2023, Asan Cups had garnered approximately 30,000 users, with the adoption rate steadily climbing. The company strategically used an education-intensive model to foster awareness about period products in collaboration with partners nationwide. However, being a for-profit entity, the founder, Guha, was at a crossroads. Balancing the need for profitability, there was mounting pressure to explore additional revenue streams and expand operations and market reach. The dilemma loomed large: opt for a quicker marketing strategy or stay true to the company’s foundational education-centered approach. This case study delves into the dynamic strategies, impactful operations and growth scenarios Asan Cups navigated since its inception. It examines the pivotal choices faced by the founder and explores potential strategies for sustained growth.

Complexity academic level

This case study can be used at both undergraduate and master’s levels. The case study will be handy for strategic management and business strategy courses and can also be used for social entrepreneurship, marketing and entrepreneurship courses.

Supplementary materials

Teaching notes are available for educators only.

Subject code

CSS 11: Strategy.

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