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Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

The purpose of the paper is to afford a comprehensive conceptualization and operationalization of the construct of talent management through an inclusive exploration of conceptual clarifications for existing confusions while developing a complete measuring instrument.

The archival method was adopted together with a systematic review based on Khan et al.’s (2003) five steps of systematic literature review. The systematic review has encircled published research articles between 1982 and 2023 in the human resource management (HRM) arena. A total of 130 articles were initially scrutinized, and 106 were systematically reviewed to conceptualize, operationalize and explore clarifications for confusions and instrument development for talent management.

This study explored conceptual clarifications for existing confusions towards talent management while recognizing definitions that come under the main philosophical schools for the underlying concept of talent. A novel practical definition has been established for talent management while recognizing dimensions, and then certain elements. A comprehensive instrument has been developed to measure talent management.

This study is limited to instrument development in measuring talent management; nevertheless, there is an enormous scope for using the instrument to empirically measure talent management through organizational and employees perspectives linked to diverse global contexts in future studies.

The developed comprehensive instrument is a vibrant contribution to future investigations related to empirically measuring talent management associated with organizational and employee perspectives related to diverse global contexts in winning “war for talent.” This study endows a significant input to the whole frame of HRM knowledge as it resolves existing conceptual ambiguities towards talent management while defining and operationalizing it.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

This study aims to provide insight into the determinants of wellness tourism satisfaction, thereby taking a nonlinear approach regarding the relationships between attribute-level performance of wellness facility attributes, on the one hand, and wellness destination attributes, on the other hand, and global wellness tourist satisfaction. In addition, scores of impact range are calculated to reveal the potentially most determinant wellness facility and destination attributes.

This study uses data from a survey-based study conducted among 1,331 wellness tourists who have engaged in wellness tourism activities at one of 28 hotels with wellness offerings and 10 spas in Croatia. Impact-asymmetry analysis and impact-range analysis are used to quantify the potential of individual wellness attributes to generate satisfaction and dissatisfaction among wellness tourists and to perform a classification of wellness attributes according to the three-factor theory of customer satisfaction.

Operators of wellness tourism facilities, as well as managers of wellness destinations, must not make any compromises in quality levels because most wellness attributes have significantly higher potential to frustrate than please tourists. Basic factors such as cleanliness, punctuality or safety turned out to have the strongest influence on global satisfaction levels. Moreover, in line with previous research, wellness tourists have large expectations from destinations to have a preserved and beautiful nature, which is by far the most influential destination attribute. In addition to a safe environment and high-quality accommodation, wellness tourists further prefer rich cultural offerings.

To the best of the authors' knowledge, this is the first study to apply a nonlinear analysis approach to the quality–satisfaction relationship in a wellness tourism setting. Moreover, to the knowledge of the authors, this is the only study that used separate attribute models for wellness facilities, on the one hand, and wellness destinations, on the other hand, based on a nation-wide sample that covers multiple cases (i.e. multiple facilities and destinations).

本研究旨在深入了解养生旅游满意度的决定因素, 从而采用非线性方法来研究（i）养生设施属性和 （ii）养生目的地属性对国际养生游客满意度的关系。此外, 本文还计算了影响范围的分数, 以揭示潜在的最具决定性的养生设施和目的地属性。

本研究使用了基于对 1,331 名养生游客进行调查问卷的数据, 这些游客曾在克罗地亚 28 的酒店以及10个水疗中心进行了养生旅游活动。本文采用影响不对称分析（IAA）和影响范围分析（IRA）来量化个体养生属性在健康游客中产生满意度和不满意的潜力, 并根据顾客三因素满意度理论对健康属性进行分类。

养生旅游设施的运营商以及养生目的地的管理者不能在质量水平上做出任何妥协, 因为大多数养生属性很可能使游客感到沮丧, 而不是取悦游客。事实证明, 清洁、准时及安全等基本因素对全球满意度影响最大。此外, 根据之前的研究, 健康游客对目的地抱有很大的期望, 希望拥有保存完好且美丽的自然风光, 这是最具影响力的目的地属性。除了安全的环境和高品质的住宿外, 养生游客更看重丰富的文化产品。

这是第一项将非线性分析方法应用于养生旅游环境中的质量与满意度关系的研究。此外, 据作者所知, 这是唯一一项基于涵盖多个案例（即多个设施及目的地）的国家样本, 一方面对养生设施使用单独的属性模型, 另一方面对养生目的地使用单独的属性模型的研究。

Este estudio tiene como objetivo proporcionar información sobre los determinantes de la satisfacción del turismo de bienestar, adoptando así un enfoque no lineal con respecto a las relaciones entre el rendimiento a nivel de atributos de (i) atributos de instalaciones de bienestar, por un lado, y (ii) atributos de destino de bienestar, por otro lado, y la satisfacción del turista de bienestar global. Además, se calculan puntajes de rango de impacto para revelar las instalaciones de bienestar y los atributos de destino potencialmente más determinantes.

este estudio utiliza datos de un estudio basado en encuestas realizado entre 1,331 turistas de bienestar que participaron en actividades de turismo de bienestar en uno de los 28 hoteles con ofertas de bienestar y diez spas en Croacia. El análisis de asimetría de impacto (IAA) y el análisis de rango de impacto (IRA) se utilizan para cuantificar el potencial de los atributos de bienestar individuales para generar satisfacción e insatisfacción entre los turistas de bienestar y para realizar una clasificación de los atributos de bienestar de acuerdo con la teoría de los tres factores del cliente. satisfacción.

Los operadores de instalaciones de turismo de bienestar, así como los administradores de destinos de bienestar, no deben comprometer los niveles de calidad porque la mayoría de los atributos de bienestar tienen un potencial significativamente mayor para frustrar que para complacer a los turistas. Los factores básicos, como la limpieza, la puntualidad o la seguridad, resultaron ser los que más influyeron en los niveles de satisfacción global. En consecuencia, estos atributos no deben verse como fuentes potenciales de satisfacción y deleite del cliente, sino que deben otorgarse altos niveles de desempeño para evitar una fuerte insatisfacción. Además, en línea con investigaciones anteriores, los turistas de bienestar tienen grandes expectativas de que los destinos tengan una naturaleza preservada y hermosa, que es, con mucho, el atributo de destino más influyente. Además de un entorno seguro y un alojamiento de alta calidad, los turistas de bienestar prefieren una rica oferta cultural. Aplicando la teoría de los tres factores, una visión más matizada de la formación de la satisfacción del turista de bienestar mostró que estos atributos del destino tienen un potencial mucho mayor para crear una fuerte insatisfacción que satisfacción.

Este es el primer estudio que aplica un enfoque de análisis no lineal a la relación calidad-satisfacción en un entorno de turismo de bienestar. Además, según el conocimiento de los autores, este es el único estudio que utilizó modelos de atributos separados para instalaciones de bienestar, por un lado, y destinos de bienestar, por el otro, en base a una muestra nacional que cubre múltiples casos (es decir, múltiples instalaciones y destinos).

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.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

In this research, the initial list of internal capabilities in small and medium-sized enterprises (SMEs) leading to success in international markets has been extracted. Then, the most relevant capabilities to international SMEs under servitisation and hybrid offerings have been screened. Next, the selected capabilities have been classified, and ultimately the relationship amongst the capabilities has been analysed. The conceptual model for SMEs participating in international markets with hybrid offerings has been illustrated.

A literature review has been employed to extract the initial list of internal capabilities to address the research objectives. Then, a novel hesitant fuzzy Delphi (HFD) method has been developed to select the most relevant capabilities for SMEs for hybrid offerings in international markets by using the experts opinions. Subsequently, a novel hesitant fuzzy interpretive structural modelling (HFISM) has been developed to classify the capabilities, design a level-based conceptual model and present the relationship amongst the prominent capabilities.

After the literature review, sixteen internal capabilities leading to success in the international market via hybrid offerings have been extracted. Then, eight selected capabilities were chosen for further investigation by applying 15 expert opinions and via the HFD approach. According to HFISM results, a level-based conceptual model was emanated, and “ability to take advantage of international opportunities”, “financial strength”, “technology level” and “efficient innovation management” were considered as the most fundamental capabilities resulting in successful hybrid offerings in international markets.

Alongside the multi-layer decision-making approach developed in this manuscript to analyse the internal capabilities roles in hybrid offering success towards international markets, to the best knowledge of the authors, the hesitant fuzzy approaches developed in this article have not been previously presented by any other scholar. A novel HFD approach has been designed for consensus amongst the experts under uncertain circumstances. Furthermore, a novel HFISM has been suggested and employed in this research to comprehend the relationship amongst the internal capabilities.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.