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A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A review on ensembles-based approach to overcome class imbalance problem
Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019. -
The rise of new age social media influencers and their impact on the consumers' reaction and purchase intention
The internet revolution and digitisation have created innovative platforms and spokespersons for brands beyond traditional media. The marketing landscape for brands and customers is evolving towards a more personal and authentic approach; adopting various social media platforms and influencers creates more brand engagement. Influencers are the new third-party endorsers, catering to and recommending products to their loyal community of followers. The influencer and their fans/followers are a brands new storytellers. However, selecting the right influencer for a brands promotional strategy requires careful consideration of several factors. This paper aims to study the impact/effect of these variables, namely, endorsers credibility and corporate credibility, on consumers attitudes towards the brand and its impact on purchase intention, with respect to the millennial era. In the present study, 14 Likert-based questions were designed, asking the respondents to rank their choice of agreement on a scale of 1 to 5. The results were obtained through statistical analysis, including measuring the relationship between variables using confirmatory factor analysis and regression techniques. And the study found that corporate credibility has a significantly higher impact (approximately 90%) than individual endorsements (including those by celebrities) in enhancing customers brand perception. Copyright 2024 Inderscience Enterprises Ltd. -
Determinants of Hand Washing Practices among Adolescents in India Findings from CNNS Data, 2016-18
The study attempts to assess the effect of socioeconomic determinants on access to Good Handwashing Practices (GHP) among the adolescent population in India. The Comprehensive National Nutrition Survey (CNNS), 2016-18 dataset is used to identify the predictor and outcome variable for the study. Binary logistic regression established the adolescents age and sex, mothers schooling, wealth index, and the region as a significant predictor for GHP. The study revealed that gender, age, caste, education, individual household wealth, and the region has a significant association with adolescent hand-washing practices, where economic conditions drive the individual practice of handwashing more than the behavioural aspect. It requires government intervention to improve sanitation and water facilities to accelerate hand-washing among adolescents in India. 2022 Tata Institute of Social Sciences. All rights reserved. -
Role of formal restructuring in post-bankruptcy performance of companies: Case study from India
The resolution of distressed assets/NPAs is among the greatest challenges faced by banks in India. Corporate restructuring is a popular method of revival of distressed companies wherein changes are proposed across financial, operational & portfolio & managerial restructuring. This chapter delves into the intricate landscape of court driven corporate restructuring actions and post-bankruptcy performance, particularly within the context of India's dynamic market. Leveraging empirical evidence, the chapter explores the effects of restructuring actions on post-bankruptcy performance. This is also in the background of emerging use of AI tools and technologies in corporate distress resolution. By examining the actual performance data of companies undergoing insolvency resolution process (CIRP) under IBC in India, the chapter offers valuable insights into the efficacy of different restructuring actions and their implications for post-bankruptcy performance. This chapter provides an important contribution in understanding the complexities and effectiveness of bankruptcy resolution processes. 2024, IGI Global. All rights reserved. -
Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels
Evaluation of flow resistance is necessary for the computation of conveyance capacity in open channels. The significance of the friction factor in channels with bedload conditions is paramount. The response of flow resistance in gravel-bed channels in bedload transport conditions is distinct from that of a fixed bed. The paper studies the different empirical approaches in the literature to determine the friction factor under bedload transport conditions and proposes an expression by genetic programming for the same. Various hydraulic and geometric parameters affect flow resistance in the bedload transport condition. The present study includes bed slope, relative submergence depth, aspect ratio, Reynolds number, and Froude number as influencing factors for such flow conditions. A wide range of experimental datasets is employed to determine the effect of these influencing parameters and develop a customised single expression for the friction factor. The experimental data set has also been moderated for sidewall corrections. The predictability of the proposed model is compared to various empirical equations from the literature. Unlike the existing models, the proposed model provides a more extensive expression for effectively predicting the friction factor for a wide range of datasets. The conveyance capacity of a river is validated from the estimated value of friction factor, as compared to other standard models. The developed Multi-Gene Genetic Programming (MGGP) model reasonably predicts discharge in the rivers, signifying that the model can competently be applied to field study within the specified range of parameters. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Factors influencing purchase intention of online shopping customers: A review of the existing literature
The rapid digital transformation, especially on the internet, has provided businesses with unprecedented opportunities for global expansion. This shift has revolutionized marketing, replacing costly and labour-intensive efforts with cost-effective digital strategies. E-commerce platforms facilitate this process by offering an interactive interface for users to post reviews, comments, and questions, thus enhancing the decision-making experience. Understanding customer purchase intention is vital in this digital age, as it's shaped by various antecedent factors, with digital word-ofmouth (eWOM) being a significant influencer. However, the relationship between eWOM and purchase intention remains underexplored. This chapter reviews 60 previous studies, shedding light on the factors affecting online customers' purchase intentions. The study identifies research gaps, setting a clear direction for future investigations. 2024, IGI Global. All rights reserved. -
Digital image correlation (DIC) for measuring strain in brick masonry specimen using Ncorr open source 2D MATLAB program
This micro-article is focused on the application of DIC in measuring strain for a 2D structural element like brick masonry specimen. Conventional techniques of strain measurements involve devices like strain gauges, LVDTs, dial gauges and extensometers. Even though Commercial DIC packages are available and have been widely used to perform DIC, these setups are quite expensive. In this study a simple DSLR camera along with Ncorr, an open source 2D MATLAB program is used to perform the DIC. Results have also been obtained experimentally by using conventional measuring devices and compared with that of DIC. The results obtained through DIC is well in agreement with that of experiments, and the difference in strain at failure was observed to be 4.7%. Furthermore, the results of this study should encourage researchers to adopt this technique in studies of behaviour and structural health monitoring of brick masonry specimens. 2019 The Authors -
Three-component p-TSA catalyzed synthesis of hydrazinyl thiazole derivatives
A direct single-pot three-component procedure for synthesizing bio-active hydrazinyl thiazole derivatives has been demonstrated. The reaction involves substituted 2-Bromoacetophenones, carboxaldehydes, and thiosemicarbazide to form the hydrazinyl thiazole scaffolds via a simple condensation reaction followed by intramolecular cyclization with p-TSA as a catalyst at room temperature. The ease of product separation, lack of column chromatographic purification, and use of readily available starting materials result in an efficient approach for organic synthesis. 2023 Elsevier Ltd. All rights reserved. -
Environmentally conscious synthesis of novel pyrano[2,3-d]pyrimidines via ternary deep eutectic solvents
Pyrano[2,3-d]pyrimidine and its analogues have gained considerable courtesy because of their diverse biological functions and wide-ranging applications, from pharmaceutical agents to essential natural pigments. However, synthesising pyrano[2,3-d]pyrimidine with multiple reactants is challenging and requires advanced green chemistry solutions. This study investigates the generation of thirteen new pyrano[2,3-d]pyrimidine analogues through a single-step, open-flask, multicomponent reaction (MCR) strategy involving aldehydes, phenylhydrazine, ethyl acetoacetate, and barbituric acid via deep eutectic solvents (DES). These DESs serve as environmentally friendly alternatives to traditional solvents. A ternary deep eutectic solvent (TDES) was evaluated for its catalytic solvent activity among ten different formulations. TDES-7 (5 mL) demonstrated the best performance, achieving 95 % product formation within 30 min at room temperature. Its remarkable catalytic activity and ability to produce high yields across multiple reaction cycles make it a standout choice for this application. The collaboration between MCR and TDES underscores an important blend of two significant green aspects, demonstrating their potential to achieve a green and productive sustainable synthesis method with an noble E-factor of 0.1236. 2024 Elsevier B.V. -
Novel heterocyclic thiosemicarbazones derivatives as colorimetric and "turn on" fluorescent sensors for fluoride anion sensing employing hydrogen bonding
(Chemical Equation Presented) Two novel heterocyclic thiosemicarbazone derivatives have been synthesized, and characterized, by means of spectroscopic and single crystal X-ray diffraction methods. Their chromophoric-fluorogenic response towards anions in competing solvent dimethyl sulfoxide (DMSO) was studied. The receptor shows selective recognition towards fluoride anion. The binding affinity of the receptors with fluoride anion was calculated using UV-visible and fluorescence spectroscopic techniques. 2013 Elsevier B.V. All rights reserved. -
An Efficient Detection and Prediction of Intrusion in Smart Grids Using Artificial Neural Networks
In recent years, fraud identification on Internet of Things (IoT) devices has been essential to obtaining better results in all fields, such as smart cities, smart grids, etc. As a result, there are more IoT devices in the smart grid's power management sectors, and apart from these identifications, intrusion into the smart grid is very difficult. Hence, to overcome this, a proposed intrusion detection system in a smart grid using an artificial neural network (ANN) has been used to detect the intrusion and improve the prediction rate, and it has been very effective on various faults injected into the smart grids in ranges and seasons. As per the simulation result, the proposed method shows better results as compared to a conventional neural network (CNN) with respect to the root mean square error in terms of weekly, monthly, and seasonal terms of 0.25%, 0.15%, and 0.26%, respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Evaluation of tourism infrastructure around the ancient marvels of Mahabalipuram and Pattadakal
India is rich in culture and well-known for its mythological knowledge. It has plenty of architectural marvels recognized by UNESCO and maintained by the Archaeological Survey of India (ASI). For this chapter, two of the cultural UNESCO sites, Mahabalipuram and Pattadakal, have been taken as the scope of the chapter. The introduction section talks about the history of the monuments and provides a brief overview of the tourism infrastructure. The following section, evaluation of tourism infrastructure, aims to analyze Mahabalipuram and Pattadakal's tourism infrastructure using four parameters: transportation and connectivity, accommodation, gastronomical facilities, and tourist amenities. An observational study of the sites was conducted between December 2023 and January 2024. This is followed by the recommendations provided by the authors to improve the tourism infrastructure in these sites and, finally, the chapter's conclusion and future scope for researchers. 2024, IGI Global. All rights reserved. -
Effects of dark matter on the upper bound mass of neutron stars
Observations have indicated that we do not see neutron stars (NS) of mass near the theoretical upper limit as predicted. Here we invoke the role of dark matter (DM) particles in star formation, and their role in lowering the mass of remnants eventually formed from these stars. Massive stars can capture DM particles more effectively than the lower mass stars, thus further softening the equation of state of the remnant neutron stars. We also look at the capture of DM particles by the NS, which could further soften the upper mass limit of NS. The admixture of DM particles would be higher at earlier epochs (high z). 2020 Elsevier B.V. -
Revisiting Cournot Duopoly Model An Experimental Study
Journal of the Institute for Research in Social Sciences and Humanities, Vol-6 (1&2), pp. 151-170. ISSN-0973-3353 -
Speculative investment decisions in cryptocurrency: a structural equation modelling approach
Cryptocurrency markets are inclined towards speculative usage due to the inherent high risk of financial loss and the potential for substantial gains during transaction completion. In response to this phenomenon, this study represents the inaugural effort to explore the influence of variables such as subjective norms, domain knowledge, impulsive investment tendencies, and self-control on decisions related to speculative investments. Utilising structural equation modelling with a dataset of 367 responses in India, the study is the first of its kind. The research reveals that subjective norms and domain knowledge play a significant role in influencing impulsive investment and self-control. Additionally, impulsive investment exhibits significant associations with decisions involving speculative investments. This insight underscores the complexity wherein individuals, despite exercising self-control, may still engage in speculative decisions that lead to adverse consequences. The findings have practical implications for investors and regulators, offering valuable insights into investment behaviours within the cryptocurrency realm. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
AI-Powered Smart Waste Management Systems Using Cloud Analytics
The increasing volume of trash and urbanisation has made it essential for waste management to be innovative, sustainable and effective. This study offers a smart waste management system that optimises garbage collection, sorting & recycling using AI, Internet of Things and cloud computing. Real-time monitoring, automated garbage categorisation, predictive fill-level forecasting & intelligent route planning are possible with a hybrid edge-cloud architecture. Technology improves operational efficiency & environmental sustainability over conventional trash systems by incorporating machine learning algorithms and real-time sensor data. Multi-modal data fusion, scalability, cloud analytics & adaptive feedback mechanisms enable system improvement. Sensor upgrades and staged implementation tactics assist in handling IoT adoption issues, including challenging trash categorisation accuracy and high startup expenses. This comprehensive strategy supports smart city development and global sustainability. Future improvements include blockchain for waste tracking and 5 G-powered edge AI for automatic garbage processing. 2025 IEEE. -
The Role of Major-Sport Event Cricket with Respect to Consumer Perception and Attitude Towards Ambush Marketing
International Journal of Business and Management Invention, Vol-2 (10), pp. 76-81. ISSN-2319-801X