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Food wastage and consumerism in circular economy: a review and research directions
Purpose: Considering food waste as a global problem resulting from the wastage of valuable resources that could fulfil the requirements of malnourished people, the current research focusses on understanding consumerisms impact on this phenomenon. Additionally, the circular economy (CE) approach can be critical in reducing food waste and promoting sustainability. Design/methodology/approach: A systematic literature review was conducted using bibliometrics and network analysis. The study reviewed 326 articles within 10 years, from 2013 to 2023. Findings: The findings reveal four prominent factors behavioural, environmental, socioeconomic and technological in managing food waste (FW). Reducing FW at a holistic level can benefit individuals and the environment in several ways. Research limitations/implications: Consumers are encouraged to be more responsible for their food consumption by reducing food waste, as it affects societies and businesses both economically and environmentally. This can help promote a responsible consumption culture that values quality over quantity and encourages people to make more informed choices about what they eat and how they dispose of it post-consumption. All stakeholders, including firms, the government and consumers, must examine the motives behind inculcating pro-environmental behaviour. Originality/value: Addressing consumerism and the ability to decrease FW behaviour are complex issues that require a multidimensional approach. This study seeks to fill the gap in understanding consumerism and the capacity to reduce FW using the CE approach and understand the research gaps and future research trends. 2024, Emerald Publishing Limited. -
Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs
The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently. Copyright 2023, Mary Ann Liebert, Inc., publishers 2023. -
A Framework for Dress Code Monitoring System using Transfer Learning from Pre-Trained YOLOv4 Model
Maintaining a proper dress code in organizations or any environment is very important. It not only imbibes a sense of discipline but also reflects the personality and qualities of people as individuals. To follow this practice, some organizations like educational institutions and a few corporations have made it mandatory for the personnel to maintain proper attire as per their regulations. Manual checks are performed to adhere to the organizations' regulations which becomes tedious and erroneous most of the times. Having an automated system not only saves time but also there is very little scope of mistakes and errors. Taking this into context, the main aim and idea behind the project is to propose a model for detecting the dress code in such workplaces and educational institutions where the attire needs to be regularly monitored. The model detects Business Formals (Blazer, Shirt & Pants) worn by the personnel, for which CNN has been considered, along with YOLOv4, for performing the detection, due to its nature of giving the highest accuracy in comparison to the other object-detection models. Providing the Mean Average Precision of around 81%, it becomes evident that the model performs quite well in performing the detections. 2023 IEEE. -
The effect of airline service quality on customer satisfaction and loyalty in India
Indian Aviation Industry has been one of the world's fastest-growing aviation industries with private airlines representing more than 75 percent of the domestic aviation industry. With an 18 percent compound annual growth rate (CAGR) and 454 airports and airstrips in place in the country, 16 of which are designated as international airports, it has been stated that by 2011 the aviation sector will be witnessing a revival. In 2009, with traffic movement rising and revenues rising by nearly US$ 21.4 million, India's Airports Authority appears expected to earn better margins in 2009-10, as indicated by the Civil Aviation Ministry's latest estimates. The most crucial step in identifying and providing high-quality service is to understand exactly what customers expect. Quality of service is one of the best models for measuring customer expectations and perceptions. A company's performance results in customer satisfaction with a product or service. Passenger satisfaction is important to customer sovereignty. Customers can be loyal without being highly satisfied and being highly satisfied and yet not being loyal. Companies are required to gain a better understanding of the online environment relationship between satisfaction and behavioural intention, and to assign online marketing strategies between satisfaction initiatives and behavioural intention programme. In addition, the findings of this research will assist airline managers to better serve their customers, track and improve quality of service and achieve the highest level of satisfaction for their passengers. 2020 Elsevier Ltd. All rights reserved. -
Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection
Drone detection techniques are used to detect unmanned aerial systems (UAS) also commonly known as drones. A rapid increase in these drones has limited the airspace safety and so the research for drone detection has emerged. This study compares between the two widely used deep-learning models, previously used YOLOv7 and the latest YOLOv8. The overall finding of this study suggests that the YOLOv8 deep-learning model appears to be more promising and may make valuable contributions on their own. We got the result that for 10 epochs YOLOv8 gave 50.16% accuracy while YOLOv7 gave 48.16% accuracy making YOLOv8 more promising for the task. As a practical application for future work, we intend to deploy YOLOv8 on edge devices to achieve real-time drone detection in critical security applications. 2023 IEEE. -
Performance Analysis of Various Machine Learning Classification Models Using Twitter Data: National Education Policy
With the exponential growth of social networking sites, people are using these platforms to express their sentiments on everyday issues. Collection and analysis of people's reactions to purchases of products, public services, etc. are important from a marketing and innovation perspective. Sentiment analysis also called opinion mining or emotion extraction is the classification of emotions in text. This technique has been widely used over the years to determine sentiment within given text data. Twitter is a social media platform primarily used by people to express their feelings about specific events. In this paper, collected tweets about National Education Policy which has been a hot topic for a while; and analyzed them using various machine learning algorithms such as Random Forest classifier, Logistic Regression, SVM, Decision Tree, XGBoost, Naive Bayes. This study shows that the Decision tree algorithm is performing best, compare to all the other algorithms. 2023 IEEE. -
Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices
IoT devices are widely used in medical domain for detection of high blood sugar and life threatening disease such as cancer. Breast cancer is one of the most challenging type of cancer which not only affects women but in some cases men also. Deep learning is one of the widely used technology which provides efficient classification of cancerous lumps but it is not useful for IoT devices as the devices lack resources such as storage and computation. For the suitability in IoT devices, in this work, we are compressing UNet, the popular semantic segmentation technique, for the pixel-wise classification of breast cancer. For compressing the deep learning model, we use genetic algorithm which removes the unwanted layers and hidden units in the existing UNet model. We have evaluated the proposed model and compared with the existing model(s) and found that the proposed compression technique suppresses the storage requirement to 77.1%. Additionally, it also improves the inference time by 3.82without compromising the accuracy. We conclude that the primary reason of inference time improvement is the requirement of less number of weight and bias by the proposed model. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Women empowerment in India: Are we on the right track?
Information and communication technologies (ICTs) have attracted the continued attention of national governments, international bodies, private organizations, civil societies, and NGOs. ICT has the potential to act as an influential method for promoting gender equality and social-economic and political empowerment of women. The chapter describes a number of ICT-backed initiatives in different countries targeted towards various concerns of women such as health, education, violence, governance, income, etc. It demonstrates the ability of ICT for empowering women especially those belonging to the marginalized group. This chapter examines the key challenges including technical, social, and economic to the usage of ICT for women's development as well as suggests initiatives for initiatives for national governments, policy makers, and organizations focusing on the issue of women empowerment. 2023, IGI Global. -
An Efficient andOptimized Convolution Neural Network forBrain Tumour Detection
Brain tumour is a life threatening disease and can affect children and adults. This study focuses on classifying MRI scan images of brain into one of 4 classes namely: glioma tumour, meningioma tumour, pituitary tumour and normal brain. Person affected with brain tumours will need treatments such as surgery, radiation therapy or chemotherapy. Pretrained Convolution Neural Networks such as VGG19, MobileNet, and AlexNet which have been widely used for image classification using transfer learning. However due to huge storage space requirements these are not effectively deployed on edge devices for creation of robotic devices. Hence a compressed version of these models have been created using Genetic Algorithm algorithm which occupies nearly 3040% of space and also a reduced inference time which is less by around 50% of original model. The accuracy provided by VGG19, AlexNet, MobileNet and Proposed CNN before compression was 92.18%, 89.45%, 93.75% and 96.85% respectively. Similarly the accuracy after compression for VGG19, AlexNet, MobileNet and Proposed CNN was 91.34%, 88.92%, 94.40% and 95.29%. 2023, Springer Nature Switzerland AG. -
Whale Optimization Based Approach toCompress andFasten CNN forCrop Disease andSpecies Identification
In recent years deep learning and machine learning have been widely researched for image based recognition. This research proposes a simplified CNN with 3 layers for classification from 39 classes of crops and their diseases. It also evaluates the performance of pre-trained models such as VGG16 and ResNet50 using transfer learning. Similarly traditional Machine Learning algorithms have been trained and tested on the same dataset. The best accuracy using proposed CNN was 87.67% whereas VGG16 gave best accuracy of 91.51% among Convolution Neural Network models. Similarly Random Forest machine learning method gave best accuracy of 93.02% among Machine Learning models. Since the pre-trained models are having huge size hence in order to deploy these solutions on tiny edge devices compression is done using Whale Optimization. The maximum compesssion was obtained with VGG16 of 88.19% without loss in any performance. It also helped betterment of inference time of 44.13% for proposed CNN, 56.76% for VGG16 and 63.23% for ResNet50. 2023, Springer Nature Switzerland AG. -
Indian Social Stock Exchange as a Funding Avenue for Social Enterprises
Purpose: The growth of Indian social enterprises faces a significant obstacle, which is a lack of access to finance. The social stock exchange was recently launched to cater to this problem. This study explored the financial challenges the selected social enterprises faced in India and analyzed the performance of small and medium enterprises platforms. Methodology: Three variables were subjected to descriptive analysis: migration to the principal board, funds generated, and listings by small and medium-sized enterprises. Furthermore, market volatility was evaluated using the generalized autoregressive conditional heteroskedasticity (GARCH) model. To explore financial constraints, primary research entailed conducting in-depth interviews utilizing the case study technique with both for-profit and non-profit social companies. Findings: The findings from the study concluded that IPO financing significantly supported Indian SMEs. Additionally, social enterprises face significant financing challenges related to affordability, availability, and regulation. Social stock exchange presents a promising solution to these constraints. Practical Implications: The study has recommended defining social enterprises legally will help in strengthening the operating framework of a social stock exchange. The study emphasized the important role of finance in the viability of social enterprises by highlighting financial challenges. The findings provided useful information for various stakeholders to bridge the funding gap, thereby promoting the potential advantages of the social stock exchange platforms. Originality: The study addressed the research gap with an examination of small and medium enterprise platforms and the influence of the social stock exchange on social enterprise financing in India. Primary data from both for-profit and non-profit social entrepreneurs added a disaggregated dimension. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Effect of Work Experience on Psychological Capital and Job Satisfaction among Employees
In todays fast-paced workplaces, where technology is evolving at a dizzying rate, professionals face a myriad of problems. Their inability to strike a healthy work-life balance may lead to feelings of dissatisfaction with their job. Consequently, in order to achieve flexible, long-term growth and job happiness, businesses should support their employees good psychological development. Primary data was acquired from employees in the automotive manufacturing company, totalling 95 individuals, using standardized questionnaires that had a good level of reliability and validity. The results indicated that there is no significant effect of work experience on the psychological capital of employees (F = 1.21; p < 0.30) and their job satisfaction (F = 0.35; p < 0.70). The major findings indicate that regardless of an employees level of experience, there is no substantial variation in the psychological capital and job satisfaction of the employees. This variation may also arise because of other specific factors. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization
Precision agriculture is the process that uses information and communication technology for farming and cultivation to improve overall productivity, efficient utilization of resources. Soil prediction is one of the primary phases in precision agriculture, resulting in good quality crops. In general, farmers perform the soil prediction manually. However, the efficiency of soil prediction may be enhanced by using current digital technologies. One effective way to automate soil prediction is image processing techniques in which soil images may be analyzed to determine the soil. This paper presents an efficient image analysis technique to predict the soil. For the same, a robust feature selection technique has been incorporated in the image analysis of soil images. The developed feature selection technique uses a new oscillating spider monkey optimization algorithm (OSMO) for the selection of features that are relevant and non-redundant. The new oscillating spider monkey optimization algorithm increases precision and convergence behavior by using an oscillating perturbation rate. A set of standard benchmark functions was deployed to visualize the performance of the new optimization technique (OSMO), and results were compared based on mean and standard deviation. Furthermore, the soil prediction approach is validated on a soil dataset, having seven categories. The proposed feature selection method selects the 41% relevant features, which provide the highest accuracy of 82.25% with 2.85% increase. 2013 IEEE. -
Stock Price Prediction Using RNNs: A Comparative Analysis of RNN, LSTM, GRU, and BiRNN
Stock price prediction is a crucial area of financial market research, having significant implications for investors, traders, and analysts. However, given the dynamic and intricate nature of financial markets - which are impacted by a wide range of variables such as economic statistics, geopolitical developments, and market sentiment - accurately projecting stock prices is intrinsically difficult. Conventional techniques frequently fail to fully capture these dynamics, producing predictions that are not ideal. Recurrent Neural Networks (RNNs), one of the most recent developments in machine learning, provide potential methods to overcome these obstacles. Despite their potential, the effectiveness of different RNN architectures in stock price prediction remains an area of active research. This study compares four Recurrent Neural Network (RNN) architectures - Simple RNN, Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BiRNN) - for forecasting the Nifty 50 index values on the Indian National Stock Exchange (NSE) from the year 2000 to 2021. Using a comprehensive dataset spanning two decades, we assess each model's performance using the metrics Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The data shows that the BiRNN model regularly outperforms the other models in all criteria i.e., MAE, MAPE, and MSE, indicating higher predictive accuracy. This study adds to the existing research by offering useful insights into the usefulness of RNN models, especially that of the BiRNN model for predicting stock prices, specifically in the setting of the Nifty 50 index. Our findings emphasize BiRNN's potential as a stock price prediction model and open new options for future research in this area. 2024 IEEE. -
Implementing Quality Healthcare Strategies for Improving Service Delivery at Private Hospitals in India
Healthcare is becoming the largest growing sector of India because of its huge coverage, providing services and investment by public and private players. In India growth of private hospitals have totally changed the scenario of health care delivery. This study explores the effectiveness of the strategies to provide quality health care and thereby improving the service delivery in Private Hospitals. In total 122 responses were collected after administering the questionnaires. The findings of this study reveals that quality health care strategies has positive impact on service delivery. Quality health care strategies showed a different kind of associations with three measures of quality namely structure, process and outcome measures. The implications from the study provides the need of multifaceted approach for implementing quality improvement strategies and adoption of the model for the same. This study recommends a blend of quality improvement programs with increased ICT (Information and Communication Technology) applications for enhancing the turnaround time. Further study can be conducted on other healthcare quality dimensions and strategic interventions that can enhance the quality of health care and clinical outcomes in Private Hospitals in India. 2017 Indian Institute of Health Management Research. -
Standards of human rights to palliative care: gaps and trends
Purpose: The purpose of this paper is to investigate key milestones in development of standards of human rights to health care in particular context of addressing palliative care, relevant efforts of advocacy in past decade and future area of growth. Design/methodology/approach: In this study, analysis of human rights and its standards in context of palliative care has been provided through the lens of freedom from ill treatment and torture, right to health care and older persons and childrens rights. Findings: Findings of this study highlighted significant developments in this area which include following: first treaty of human rights which explained right to palliative care; first resolution on palliative care by World Health Assembly; special rapporteurs report focussed on denial of pain; and addressing issue of controlled medicine availability in special session of UN General Assembly. Originality/value: Human rights standards and their development in context of palliative care have been most significant in relation to freedom from ill treatment and torture, right to health care and older persons rights. Further work is required in context of childrens rights and treaty bodies of human rights need to consistently address state obligations towards palliative care. 2020, Emerald Publishing Limited. -
Comprehensive study of the relationship between multiverse and big data
Studies linking two broader spectra of topics have fascinated scholars in many aspects. Here we tried associating two such far-reaching aspects which have finite connectivity between them. Multiverse has been the talk of the hour which explains the theory of multiple universes which exist in parallel. This is a topic in physics concerned with many relative matters. On the other side, Big data is the subject in computing and information science describing the volume, velocity, and variability of the data hitting computer-connected systems. Big data can only be handled with newer architectures, algorithms, and methodologies as its features are contradicting regular computer systems and networks. It is well known that multiple processors are required to handle big data existing in parallel performing a single job given by the data analyst. So as we know, multiverse consist of hypothetical concepts of several parallel universe having everything like information, energy, and time. However, we see this situation to draw an association connecting parallel universes of the multiverse with parallel processors of big data by incorporating the concepts of working of parallel universe in the processing of Big Data. We provide a comprehensive observation on both the topics and take positive lenience on bringing a newer terminology in data science. History of multiverse along with big data structures are brought in with related parameters. This aspect is novel in its nature and we complement the literature carried out by the researchers and scholars appropriate analysis. We also showcase a model of the school of thought mentioned above in drawing conclusions. 2023 The Authors -
Impact of COVID-19 on the mental health among children in China with specific reference to emotional and behavioral disorders
Purpose: This paper aims to investigate impact of coronavirus COVID-19 on childrens mental health specifically emotional and behavioral disorders. It aims at identifying the main disorders faced by children during epidemics and suggests recommendations to nurture resilience among children and involving them in various positive activities. Design/methodology/approach: This study is based on review of literature focused on COVID-19. Recent articles related to coronavirus or COVID-19 and psychological distress among children were included to draw conclusion and impact of COVID-19 on mental health of children. Due to the limited availability of studies on CONID-19 impact on mental health of children, studies focused on recent pandemic were focused. Findings: The identified literature reports a negative impact of COVID-19 on individuals mental health. Relatives health, poor appetite, fear of asking questions about epidemics, agitation, clinginess, physical discomfort, nightmares and poor sleep, inattention and separation issues were among the major psychological conditions analyzed. Personal attributes such as resilience, should be nurtured so that children will be empowered to manage difficult situations such as traumas and disappointments. Several measures were suggested by pediatricians in China to family members and parents such as playing games with children to reduce feeling of loneliness, increased communication to address their concerns and fears, promoting and encouraging physical activities and involving in musical activities to reduce fear, worry and stress among children. Originality/value: Coronavirus is new pandemic and growing rapidly. most of the research studies are focused on physical health of individuals, but mental health concept has bene overlooked. This study helps to broaden the scope of research on children's mental health by examining the impact of COVID-19. 2020, Emerald Publishing Limited. -
Crowdsourcing in Higher Education: Theory and Best Practices
The widespread use of crowdsourcing strategies in higher education institutions improves the performance of students by using collective initiatives to enhance the skills of each student, efficiently optimizes the lecturing process by exchanging and pooling research materials, and also improves the financial situation of alumni by encouraging crowdfunding of tuition. We identify four main areas in this study where the use of crowdsourcing strategies plays an important role in the success of alumni in institutions of higher education. The proposed crowd teaching approach optimizes lecturing, allowing lecture notes to be shared and exchanged according to the various curricula of Higher Education Studies. With crowd learning, students learn by execution on collaborative projects in which different students share (effectively) teaching each other under the guidance of the lecturer, learn the necessary skills to achieve the projects goals and solve the proposed issue. In relation to accessing funding, the tuition fees of students can be financed by crowdsourcing approaches through crowd tuition and even crowdfunding can be used to procure laboratory and classroom content or the learning stays of students abroad. Using this crowdsourcing tool, students can find assistance in paying university taxes and also have an interest in further learning with other students. The application of crowdsourcing to education allows for optimization of the institutions budget and a more effective use of learning time, leading eventually to better outcomes for students. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Interconnections of yogic practices with mental health
Yoga, an ancient practise of humankind, attempts to promote a lifestyle that is free of maliciousness with emphasis on inculcating qualities that would aid the individual in living a life that is truly actualizing. Practise of yoga is not limited to holding specific asanas but various components of yoga such as pranayama, pratyahara, etc.; all attempt to enhance an individual's wellbeing. The chapter has contextualized yoga therapy including pranayama, mudras, and chakras to biopsychosocial models, and attempted to identify yogic practises that bring holistic enhancement. Yoga, being cost-effective, and having no side effects, unlike pharmacological treatments, can be used as an adjunctive therapeutic agent in improving symptoms or improving mood and reducing stress. However, it is important to note the feasibility and limitations of yoga interventions, like proper trained professionals to minimize any ill effects. The chapter attempted to promote the practise of yoga as an adjunctive form of treatment which would thereby aid in improving biopsychosocial wellbeing. 2024, IGI Global. All rights reserved.