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This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

The purpose of this paper is to understand US federal government policy during the early stages of the COVID-19 pandemic and the connections to graduate education. Using the multiple streams framework, the paper outlines these actions through various streams (problems, policy and political) and perspectives (defining problems, articulating options and mobilizing responses).

The primary sources of data collected for this study were US federal government policies from March 2020 through May 2021. Policies were examined through introduction, implementation and alteration (when possible) within the specific time period of the study. The policies outlined in this paper were connected to the US Department of Education, and to a lesser extent, the National Institutes of Health, the National Science Foundation and other federal agencies. Data analysis was a two-fold process. First, the individual policy was considered as a single case and second, a cross-case comparison occurred across the multiple cases.

Analysing the study’s data in the problem stream provides a strong indicator of how the pandemic was perceived as a challenge for US graduate education. The pandemic served as a focusing event and illuminated the connections of graduate education to key institutional functions, including research and teaching. Broadly, US federal policy actions in this area focused on giving institutions resources and flexibility to support graduate students and allow them to continue their academic work while also seeding funding and incentives to continue the movement of knowledge, activities and people in the research pipeline. Actions in the policy stream aligned with the decentralized nature of the US higher education system and allowed for choice by academic institutions within the parameters of options.

This paper extends extant literature related to policy-making and graduate education to consider policy-making during a time of crisis. The paper offers methodological and conceptual ideas for consideration in future research.

The purpose of this study is threefold: firstly, to compare Pakistani and French consumers’ perceptions of well-being; secondly, to investigate how consumers in both countries relate to food; and thirdly, to assess whether they associate food with well-being.

Thirty participants (15 French and 15 Pakistani) between the ages of 24 and 35 were interviewed, using convenience and snow bowling sampling. Data triangulation was performed by combining three qualitative techniques, word association, photo-elicitation-based interviewing and open-ended questions to explore consumer perceptions of well-being, food and food well-being.

The study’s findings suggest that well-being is a broad concept in which food is an ingredient. Food and well-being share common elements, and food well-being can be defined as an individual’s psychological, physical, social and societal relationship with food ascribed by affordability and food literacy.

Pleasure, sharing and respect emerged as dimensions of food well-being that can be applied to transfigure consumer behaviour and reduce over-consumption, food waste and hunger. The dimensions of well-being and food were explored for both countries to understand their cultural nuances and determine the influence of food on well-being. This comparative analysis will help researchers understand consumers’ preferences for food in various aspects from two regions. This study can potentially contribute to scale development in food and well-being, which can help researchers measure the effects of food and well-being in different sectors of the economy, particularly in health care. The most aspiring aspect of the current research is the insights unveiled during interactions with research participants, which will help develop consumer baseline feelings.

Research indicates a long historical connection between racism and nationalist ideologies. This connection has been highlighted in the resurgence of exclusionary nationalism in recent years, across many multicultural societies. This chapter discusses the notions of race, ethnicity and nation, and critically examines how racism shapes contemporary manifestations of nationalist discourse across the world. It explores the historical role of settler-colonialism, imperial expansions and the capitalist development in shaping the racial/ethnic aspect of nationalist development. Moreover, it provides an analysis of the interconnections between the racialisation of minorities, exclusionary ideologies and the consolidation of ethno-nationalist tropes. This chapter further considers the impact of demographic changes in reinforcing anti-migrant exclusionary sentiments. This is examined in connection with emerging nativist discourse, exploring how xenophobic racism has shaped and is shaped by nostalgic nationalism based on the sanitisation of the legacies of Empire and colonialism.

Microfinance programs across the countries are designed on the self-help and peer pressure model, aim at microentrepreneurship development. Despite of significant studies on microfinance-supported microentrepreneurship (MSM), not a single literature examines it from the systems thinking. In addition to that, the extant literature did not look MSM from the behavioral perspectives. To address the above gaps, the present study aims to examine self-help group (SHG)-based microfinance programs from the systems approach using the Stimulus-Organism-Behavior-Consequence (SOBC) model.

Information gathered from 786 women SHG members from four states of India through a structured interview schedule. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were conducted to process data. Additional statistical tests were performed to test the reliability and validity.

It was found that the “positive stimulus” (social intermediation, financial intermediation and business development services) positively impacted; and “negative stimulus” (intermediation accountability, and intermediation assumption) negatively impact, to “motive” (attitude, subjective norms, and perceived control) for micro-entrepreneurship in the SHG-based microfinance. Further, “motive” positively predicted “behavioral intention”; the “behavioral intention” positively determined “consequences” of micro-entrepreneurship. Intermediation as stimuli acted as “input”; the motive and behavioral intention acted as the “process”, and the consequence acted as the “output” in the SHG-based microentrepreneurship system.

To the best of the author's knowledge, this paper is the first one to examine the behavioral systems of microentrepreneurship programs through the Stimulus-Organism-Behavior-Consequence (SOBC) model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0801

This paper explores the context within which experimental, pedagogically progressive schools were established in Australia during the first decades of the 20th century.

The paper presents a case study of the establishment of Rosbercon Girls’ Grammar School. It draws on educator accounts, archival documents and contemporary literature to provide a brief narrative of the events leading to the opening of the school; to sketch the family of educators who were pivotal in making it a reality; and to identify key aspects of the social and legislative context that made such an initiative possible.

Rosbercon was established at a time when a modest school could be established relatively easily by a small group of educators with a shared vision. The early 20th century was a moment of national optimism in Australia, where an appetite for new educational ideas created a climate in which innovative educators found fertile soil for their pedagogical experiments and adaptation of emerging ideas from around the world. Their efforts were facilitated by an emerging global network of personal interactions, professional learning, professional associations and educational literature.

This paper addresses the relative lack of scholarly examination of the origins of Rosbercon Girls’ Grammar School, an institution that previous authors have identified as Australia’s oldest experimental school. The case study also contributes to a broader appreciation of the trajectory of progressive education during the early 20th century.

The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley’s (1984) theory about the organization of sciences and Fuchs’ (1993) theory about scientific change.

The study combines historical source analysis with conceptual and theoretical analysis for characterizing LIS. An attempt is made to empirically validate the distinction between LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies.

The origin of fragmentation in LIS due the contributions of other disciplines can be traced in the 1960s and 1970s for solving the problems produced by the growth of scientific literature. Computer science and business established academic programs and started research relevant to LIS community focusing on information retrieval and bibliometrics. This has led to differing research interests between LIS and other disciplines concerning research topics and methods. LIS has been characterized as fragmented adhocracy as a whole, but we make a distinction between research topics LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies.

The paper provides an elaborated historical perspective on the fragmentation of LIS in the pressure of other disciplines. It also characterizes LIS as discipline in a fresh way by applying Whitley’s (1984) theory.