Journal of Information & Knowledge Management

Quantum computing (QC) holds promise for next-generation applications that requires exponential time to solve NP problems. However, even after 25+ years, researchers still struggle with stable error correction solutions for delivering a stable commercialised quantum universal quantum machine. Companies like IBM, Rigetti, and D-Wave have already started offering quantum annealers, intermediate quantum devices based on the qubit. Therefore, researchers believe that QC would be introduced as an extension of classical computers (CC), where CC would offload a specific computational intensive task. Cloud companies, including Amazon, Azure, and IBM, have already begun to provide cloud services where an organisation can use a QC to work on potential applications. This paper systematically analyses the need to provide QC as a service (QCaaS) for potential applications. Furthermore, we discuss the framework on which this service can be offered. Subsequently, we provide a detailed discussion of various toolkits, libraries, and simulators available for QC.

This study aims to investigate the influence of virtual work friendships on knowledge sharing and team performance in the context of the COVID-19 pandemic. Additionally, it explores whether perceived task interdependence moderates this relationship. A survey-based research design was employed to collect data from 213 individuals working in IT-based firms. Data were collected in two time lags to capture the dynamics of virtual work friendships, knowledge sharing and team performance. The study reveals a positive and significant relationship between virtual work friendships and team performance. Furthermore, knowledge sharing acts as a mediator between virtual friendships and team performance. Managers should actively promote friendship among employees to enhance virtual work environments. Additionally, when task interdependence is perceived highly, leveraging social resources both virtually and physically can further enhance team performance. This research contributes to the social capital theory by examining the effects of virtual work friendships on team performance in the modern workplace. It is the first study to investigate the moderating role of task interdependence in this context.

Corporate social responsibility (CSR) is gaining importance for corporations and is attracting a lot of attention. A company can be held socially responsible to its consumers, stockholders, and the general population by implementing a corporate policy of self-policing called social responsibility in business (CSR). By fulfilling their social obligations, businesses can become aware of how their actions affect society’s financial, social, and environmental conditions. Efficient corporate social responsibility practices aid businesses in improving their reputations among consumers, who are then motivated to support and show loyalty to the business. The company’s social responsibility will increase trust, which will boost customer loyalty. Huge corporations participate in corporate social responsibility mostly because it is viewed as a strategic edge and offers advantages to businesses, including enhancing their image, lowering marketing and operating expenses, and attracting investors and talents. Therefore, corporate social responsibility offers businesses several benefits, including boosting brand loyalty, trust, and positive sentiments about a company as well as fighting off the unfavourable press. By the support of managers and supervisors of the textile industry in Maharashtra, the recent study’s aspiration is to check and assess numerous aspects of corporate sustainability’s in customer loyalty. The major elements of corporate social responsibility enclosed beneath the study take in environmental responsibility, ethical responsibility, philanthropic responsibility, economic responsibility, and legal responsibility. Since customer loyalty is a significant determinant of corporate success, elements that foster loyalty are of considerable interest to businesses. All of this demonstrates the worth and necessitates for thorough study in the area of corporate social responsibilities impacting customer loyalty. This study intends to analyse the control of business social responsibilities on consumer loyalty in the textile industry of Maharashtra.

Nowadays, extracting the most suitable information becomes a very challenging problem as accumulation of data over Internet is exponentially increasing. This raises the need of such systems which can process data meaningfully, have capability of understanding the user’s questions, and can provide the most relevant information. The existing systems are not capable of giving precise results. The performance of such systems can be improved using Natural Language Processing (NLP) and Cognitive Computing System. Therefore, an artificial cognitive agent is proposed in this paper using NLP, which can take decisions in real-life using social cognition. These agents are expert in their respective domains. Moreover, social behavioural aspects and the constraints of proposed cognitive agent are also modelled in this work. The performance of the proposed agent is evaluated on the concepts of Electric Motor domain in terms of various parameters. The results depict that the proposed artificial cognitive agent obtains satisfactory results.

Stock market prediction is a vital task with high attention for gaining attractive profits with proper decisions to invest. Predicting the stock market is becoming a major challenge nowadays due to chaotic data, non-stationary data, and blaring data. Hence, it’s challenging for investors to invest money to make profits. Many techniques are developed to predict stock market trends, but each differs based on time and year. In this paper, hybridised optimisation algorithm, namely the proposed Gannet Ladybug Beetle Optimisation (GLBO), is used for training Transfer Learning (TL), which considers Convolutional Neural Network-enabled Long Short-Term Memory (CNN-enabled LSTM). This TL is responsible for predicting the stock market from augmented data. Here, the stock market dataset of two companies is used as input time series data. Moreover, many features are extracted from those input data and then from those features, necessary features are selected based on Motyka similarity. The bootstrap method is used in this paper for data augmentation. Also, GLBO is hybridised with Gannet Optimisation Algorithm (GOA) along Ladybug Beetle Optimisation (LBO) algorithm. Furthermore, the proposed model is verified for its performance capability based on three metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), as well as Mean Absolute Percentage Error (MAPE) with values of 5.8%, 24.1%, and 78.8%.

China’s construction industry is developing rapidly, and market investment is out of control. Therefore, accurate and rapid project cost estimation is the focus of the industry. The grey theory system is a method to study the uncertainty problem of little data and poor information. It can excavate different information hidden in the system observation data to achieve correct description and understanding. The research will improve the BP neural network based on grey theory, and verify the exactitude and advantage of the model from performance comparison and empirical analysis. This study can make the project cost forecast more reasonable. Results show that compared with the traditional model, the maximum error value decreased by 0.52, the research model’s maximum mean square error decreased by 0.09, and the variance decreased by 0.13. In the regression analysis, the $R$-value of the model fitting was above 0.95, the $R$-value of the verification fitting reached 0.9205. The $R$-value of the overall fitting reached 0.9475, and the fitting effect was excellent. The convergence speed of the research model is faster than other models, and the fitness value is stable at 1.36. In the empirical analysis, this model can review project cost investment budgets in some areas and reduce project costs. This can improve work efficiency. The model also has certain practical significance for the sustainable development of construction project cost estimates.

The Mobile Adhoc Network (MANET) is a vital network that is vulnerable to energy and security restrictions. Because of node mobility, building a routing protocol to offers an effective and acceptable means of routing data with minimum packet loss, less energy consumption, and extending network lifetime has become a difficult challenge in MANETs. Most of the current MANET routing protocols lack the strategy of precise load-balancing. A recent survey on load distribution in MANET shows shortest-path MANET routing protocols using centrally located nodes in many routes. Since few nodes have to carry excessive loads, congestion increases, leading to undesirable effects such as long delays, lower packet delivery, and higher routing overhead, which shows the need for proper balancing models. In this work, multipath load balancing with the proposed NUSOA is used in MANETs to reduce energy usage and packet loss. Here, the data packets are distributed through gateway nodes, which are selected by the various features such as path cost, link cost, residual energy, and distance. Proposed hybrid optimisation termed as Namib Updated Shepherd Optimisation Algorithm (NUSOA) which is the mixture of both Namib Beetle Optimisation (NBO) and Shuffled Shepherd Optimisation Algorithm (SSOA) to choose optimal gateway nodes. Moreover, the optimal path is chosen based on load balancing with the constraints of congestion detection and node availability of the path using the proposed hybrid optimisation.

Word embeddings/representations are an important component of Natural Language Processing (NLP) tasks. Most Neural Machine Translation (NMT) systems that use such representations disregard word morphology by assigning a unique vector to each unique word in the used vocabulary and thus cannot handle Out-Of-Vocabulary (OOV) words. In some languages, such as Arabic, the meaning of words is associated with the meaning of the individual characters that constitute them, as these characters embody internal information. In this study, a combination of character- and word- level models is used to determine the most effective approaches to semantically and morphologically representing affective Arabic words. Furthermore, this work examines the strategy of combining static, character and contextual word embeddings to obtain richer representations for the Arabic Machine Translation (MT) task. To the best of our knowledge, we are the first to investigate the combination of static word embeddings, contextual ones and character-level representation in Arabic MT. Furthermore, a Deep Learning (DL) architecture is employed on data preprocessed by various prominent preprocessing techniques. Various experiments were conducted and the findings indicate that the integration of various models for word embedding and character-level representation is feasible and more effective than the state-of-the-art Arabic MT systems.

The main advantage of Agile Project Management (APM) lies in its ability to investigate and resolve issues that arise during the project period, making timely adjustments to save resources and deliver successful projects on time and at a lower cost. Customers play a crucial role in determining many of these changes, highlighting their special involvement in managing projects using an agile approach. This paper prioritizes the factors influencing challenges related to customer knowledge in APM, initially identifying three categories: individual, organizational, and technological factors. Expert opinions from the software development industry verified these factors, while the DANP method explored their causal relationships and importance. The analysis revealed organizational factors’ impact on the other two categories, with individual factors ranking highest, followed by technological factors. Notable challenges related to customer knowledge include lack of time for knowledge sharing, reluctance to adopt information technology systems, ineffective communication between knowledge-holders and seekers, inadequate training on new technology, and a lack of awareness regarding knowledge benefits for project partners. These findings are presented as suggestions to project teams for effectively managing agile projects and addressing customer knowledge challenges. By implementing these recommendations, projects can achieve greater efficiency and success.

Objective: This paper used three feature selection methods on a Jordanian automobile drivers’ dataset to identify the most significant features for stress prediction algorithm performance. The dataset contains “stress” and “no-stress” classes with 30 features, categorised into physiological and contextual subsets.

Methods: Eighteen classifiers from six prediction algorithm categories were evaluated: Rule-based, Tree-based, Ensemble-based, Function-based, Naïve Bayes-based and Lazy-based. Three Feature Subset Selection (FSS) methods were used: Gain Ratio, Chi-square and feature separation. Eight evaluation measures included $F$1, Accuracy, Specificity, Sensitivity, Kappa Statistics, Mean Absolute Error (MAE), Area Under Curve (AUC) and Precision Recall Curve Area (PRCA).

Results: Among the classifiers, Lazy-based LocalKNN performed significantly well in $F$1, Accuracy, Kappa and MAE. Naïve Bayes-based Bayesian Network excelled in other measures. The original dataset with all features yielded the best overall performance, followed by the physiological-only subset. Gain Ratio and Chi-square FSS methods also showed promising results, though not significant.

Conclusion: Four physiological (EMG, EMG Amplitude, Heart rate, Respiration Amplitude) and seven contextual (time range of driving, gender, age, driving skills, general accidents, last year’s accidents, stress frequency) features contributed to the best prediction outcomes. The study highlights the importance of proper feature selection and identifies optimal algorithms for specific measures.

The quick development of intelligent information technology in modern society has accelerated education informatisation. Accordingly, the traditional modes of instruction and methods of instruction are no longer able to satisfy the educational needs of modern society, so the flipped classroom teaching method emerges at the historic moment. While injecting fresh vitality into education, this model can also reflect good teaching effects, which is of enormous practical relevance to the development of education. Additionally, flipped classroom teaching is made possible by the online teaching resources offered by colleges and institutions. In view of the unreasonable distribution of curriculum resources in the current university network teaching platform, this study proposes a university network platform combining flipped classroom teaching mode and improved Apriori algorithm. First of all, to achieve the acceptable allocation and suggestion of learning materials in accordance with students’ learning demands, the Apriori algorithm is introduced to address the university network teaching platform’s lack of adequate planning and organisation. After that, the flipped classroom teaching mode is introduced into the online teaching platform for empirical research. According to the trial findings, more than 90.22 % of the students had improved their study habits. The network teaching platform proposed by the research institute can stimulate college students’ learning interest and improve their learning ability to a certain extent.

Cybersecurity has become a great concern in many real-world applications involving adversaries with Machine Learning (ML) algorithms being more widely used. This concern is more challenging in Internet of Things (IoT) platforms. As IoT-enabled applications are growing at a rapid pace in every sector there are growing security related incidents as well. ML algorithms are widely deployed to perform data analysis, reasoning and decision-making over the data emanating from IoT devices. Security of this data while collection, communication and computing is a major challenge. Various attackers are trying to find weaknesses in the ML algorithms and trying to deceive these ML algorithms to learn the wrong information from the data. Countermeasures need to be developed to evaluate the security of the ML models. To develop such countermeasures, one needs to understand all possible attacks on the ML models. Data poisoning attacks are a class of adversarial attacks on ML where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier to satisfy certain objectives. In order to develop an attack-resistant ML model, one needs to know all possible attacks on these models. The recent data poisoning technique such as the Fast Gradient Sign Method (FGSM) is static and provides very micro control to the attacker on creating adversarial data. In this research, we develop a more robust data poisoning technique for deep neural networks using Generative Adversarial Networks (GANs) to create a data poisoning attack. We then evaluate the performance of the proposed algorithm and also compare the results obtained by FGSM.

Switching from traditional to online learning during the COVID-19 pandemic bore the associated opportunities and challenges. The study presents the clout of Perception, Emotion, Attitude, Teacher-Student, and Peer interaction on Learner Satisfaction (LS) partially mediated by motivation among business management students of Russia, India, and Bangladesh. The psychological framework used in the study, followed by Structural Equation Modelling, displayed a significant association between all exogenous and endogenous variables. The responses from the 1008 students during the pandemic corroborate two critical learning theories, i.e. Social Constructivism and Media Richness Theory, by confirming a significant and positive relationship between attitude and motivation. The learners’ perception of the content and administration is pivotal for LS. Whereas peer learning was an important aspect in Russia and India, assisted learning was preferred by the students of Bangladesh.

Knowledge Management (KM) is crucial for efficient information retrieval and forms the backbone of implementing an Information System (IS). Efficiently extracting meaningful trends from headlines aids in visualising demographic situations and addressing emergency handling needs. Understanding Information Resource Management (IRM) and Knowledge Resource Management (KRM) concepts benefits end users. This proposed KRM concept organises knowledge, ensuring global resource control through a neural decision framework. Traditional methods like Structural Equation Modelling (SEM) have higher computational steps, whereas neural network strategies in KM reduce steps and improve data prediction accuracy. The paucity of input data is a common challenge, which can be overcome by using Generative Adversarial Network (GAN) architecture for probabilistic generative processes. This research evaluates acquisition, spreading, vulnerability, and application using GAN-based knowledge systems that mitigate data scarcity through generative methods. The proposed Hierarchical Vanilla Generative Adversarial Network (HVGAN) utilises gradient functions and hierarchical computation algorithms to achieve desired knowledge extraction with lower time complexity and higher accuracy. Implementation improves efficiency in accessing stored knowledge, yielding an AUC score of 96% compared to other GAN architectures.

The purpose of this study is to examine the nexus between electronic services (e-services) and tax administration performance directly, and indirectly via taxpayers’ support. Furthermore, it investigates the relationship between taxpayers’ support and e-services effectiveness. Quantitative data were collected from 217 tax officers through questionnaires, and structural equation modelling (SEM) was performed with WarpPLS 7.0 software. This study found that tax administrator and taxpayer e-services can improve tax administration performance directly, and indirectly through the mediating role of taxpayers’ support. Furthermore, taxpayers’ support can enhance e-services’ effectiveness in tax administration. The study recommends the adoption of the proposed research model which integrated e-services and taxpayers’ support to improve tax compliance, revenue generation, and performance to stimulate economic growth and national development.

From the theoretical perspective of the elaboration likelihood model and service quality, this study attempts to provide an empirical study of patients’ medical choice behaviour and the moderating effect of disease risk under different paths with a real data set from an online healthcare community. The results demonstrate that factors from the central and peripheral routes affect patients’ behavioural intention regarding online medical choices. Disease risk plays an important moderating role. Low-risk patients are mainly influenced by factors from the peripheral route including doctors’ service reputation and disclosure of service qualifications, while high-risk patients are mainly influenced by factors from the central route including service quality of treatment and reviews quality.

At present, the sorting work of library books heavily relies on manual labour, and the sorting process is time-consuming and laborious. To address the above issues, in the context of digital transformation in the information age, a strategy research of intelligent library service transformation based on convolutional network is proposed to achieve intelligent daily maintenance regarding libraries. In particular, the arrangement of books based on category and order is resolved. First, the Canny operator detection algorithm combined with straight line is used to extract the book spine from the book image. Second, the Faster-RCNN-based target detection algorithm is selected to locate the claim number. The positioning accuracy also reaches 96% in the positioning test, which is also better than other algorithms. The specific detection time is 100s. Then the method is used to segment the characters, and the accuracy is 97%. Finally, in the character recognition of library book request numbers, the feature extraction effect of character images based on convolutional networks is the best. Compared with 95% and 96% of other algorithms, the average recognition rate is 97%, which also improves network performance. The comparative experimental findings suggest that the convolutional network algorithm can effectively achieve the information detection and book order recognition of library common claim numbers, providing a feasible solution for library intelligence.