Bahria University

Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.

BACKGROUND: To limit the rapid spread of COVID-19, countries have asked their citizens to stay at home. As a result, demographic and cultural factors related to home life have become especially relevant to predict population well-being during isolation. This pre-registered worldwide study analyses the relationship between the number of adults and children in a household, marital status, age, gender, education level, COVID-19 severity, individualism-collectivism, and perceived stress. METHODS: We used the COVIDiSTRESS Global Survey data of 53,524 online participants from 26 countries and areas. The data were collected between 30 March and 6 April 2020. RESULTS: Higher levels of stress were associated with younger age, being a woman, lower level of education, being single, staying with more children, and living in a country or area with a more severe COVID-19 situation. CONCLUSIONS: The COVID-19 pandemic revealed that certain people may be more susceptible to experience elevated levels of stress. Our findings highlight the need for public health to be attentive to both the physical and the psychological well-being of these groups.

Abstract The objective of this research is to examine the role of economic growth, technology innovation, and renewable energy in reducing transport sector CO 2 emission in China by using the annual data of 1990–2018. An application of the QARDL approach discloses that economic growth, technology innovation, and renewable energy significantly influence CO 2 emission in the transportation sector in China. Both renewable energy consumption and innovation show a negative impact on emissions of CO 2 related to transport. It depicts that due to the increase in renewable energy and innovation, the CO 2 emission in the transport sector is likely to decrease; however, an increase in the GDP of a country will upsurge the emission of CO 2 in the transportation sector. However, China should make new policies to introduce innovation in the transportation sector to minimize the emission of CO 2 .

Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. We deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, we deployed pretrained AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. We also compared the data sets with different color models to check the performance over different color images. For acute lymphoblastic leukemia detection, we achieved a sensitivity of 100%, specificity of 98.11%, and accuracy of 99.50%; and for acute lymphoblastic leukemia subtype classification the sensitivity was 96.74%, specificity was 99.03%, and accuracy was 96.06%. Unlike the standard methods, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.

The main aim of this paper is to discuss the Internet of things in wider sense and prominence on protocols, technologies and application along related issues. The main factor IoT concept is the integration of different technologies. The IoT is empowered by the hottest developments in RFID, smart sensors, communication technologies, and Internet protocols. Primary hypothesis is to have smart sensor dealing directly to deliver a class of applications without any external or human participation. Recently development in Internet and smart phone and machine-to-machine M2M technologies can be consider first phase of the IoT. In the coming years IoT is expected to be one of the main hub between various technologies by connecting smart physical objects together and allow different applications in support of smart decision making. In this paper we discuss IoT architecture and technical aspect that relate to IoT. Then, give over view about IoT technologies, protocols and applications and related issues with comparison of other survey papers. Our main aim to provide a framework to researcher and application developer that how different protocols works, over view of some key issues of IoT and the relation between IoT and other embryonic technologies including big data analytics and cloud computing.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

This paper aims to examine the role of blockchain technology for the circular economy to enhance organisational performance in the context of China–Pakistan-Economic-Corridor (CPEC). A close-ended questionnaire-based survey of manufacturing firms was administered to collect cross-sectional data from 290 respondents, which has been analysed through structural equation modelling. The circular economy approach in supply chain management offers environmental as well as economic benefits to the organisations. With the beginning of the Industry 4.0 era, there is an emphasis on technology in every field. A relatively recent phenomenon of blockchain technology promises lots of potential improvement in business operations. According to our results, with features such as visibility, transparency, relationship management, and smart contracting, blockchain technology was found to play a positive role in the circular economy. Furthermore, green practices were found to have a positive nexus with environmental and economic pathways to the firms' performance, whereas environmental performance was also found to have a positive nexus with the economic health of the firm. Last but not the least, both environmental and economic performances were found to be the cause of a boost in organisational performance.

The present study examines the impact of unsteady viscous flow in a squeezing channel. Silver–gold hybrid nanofluid particles with different shapes are inserted in the base fluid engine oil. Flow and heat transfer mechanism is detected in the presence of magnetohydrodynamics between the two parallel infinite plates. The thermal conductivity models, that is, Yamada–Ota and Hamilton–Crosser models are used to investigate various shapes (Blade, platelet, cylinder, and brick) of hybrid nanoparticles. The model is made up of paired high nonlinear partial differential equations that are then transformed into ordinary differential equations which are coupled and strong nonlinear using the boundary layer approximation. The MATLAB solver bvp4c package is used to solve the numerical solution of this coupled system. The influence of different parameters on the physical quantities is addressed via graphs. A comparison with already reported results is given in order to confirm the current findings. The analysis shows that surprisingly the Yamada–Ota model of the Hybrid nanofluid gains high temperature and velocity profile than the Hamilton–Crosser model of the hybrid nanofluid. Also, both the models show increasing trends toward increasing the volume fraction rate of silver‐gold hybrid nanoparticles. It is also inferred that the hybrid‐nanoparticles performance is far better than the common nanofluids.

In this study, we untangle the relationship between digital platform capability and organizational agility in the manufacturing sector small and medium-sized enterprises (SMEs) by investigating the mediating role of intellectual capital and the moderating role of environmental dynamism. Using a time-lagged two-wave survey of 227 manufacturing SMEs, we tested our proposed hypotheses using structural equational modeling (SEM). Our results reveal that digital platform capability is positively associated with the agility of SMEs and that all three intellectual capital dimensions (i.e., human, organizational, and relational capital) mediate this relationship. We also found that environmental dynamism has a negative moderating role on digital platform capability and intellectual capital. Environmental dynamism curbs SMEs’ abilities to turn their digital platform capabilities into increased intellectual capital. Our results shed light on the importance of intellectual capital in creating improved organizational agility for manufacturing SMEs through digital platform capability within the boundary conditions of environmental dynamism.

OBJECTIVE: The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry. MATERIALS AND METHODS: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted. RESULTS: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics. CONCLUSION: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods.

Leadership style is an important factor that affects the enhancement of organizational performance and employee’s job performance, and what objectives they should pursue, which also makes a profit for their employees or makes another social and economic contribution to society. The present study was developed to observe the impact of transformational leadership on job performance and to investigate the mediating mechanism of corporate social responsibility (CSR). Primary data were collected from the employees by using a cross-sectional design method. Employees who participated in the study are working in the Small and Medium Enterprises (SMEs) of Pakistan. A total of 300 questionnaires were circulated, and 130 were received. The Regression analysis was executed to examine whether CSR mediated the correlation among transformational leadership and job performance. The results of the study suggest that transformational leadership positively and completely predicts job performance. Particularly, the study finds that CSR significantly mediated the effect of transformational leadership on job performance. On the basis of these findings, it can be explicated that transformational leadership, job performances, and CSR are important elements of an organization. These elements can improve organizational performance. Theoretical implications of the recent study are discussed, and offer directions for future research in the area.

Lung cancer (LC) remains a leading cause of death worldwide. Early diagnosis is critical to protect innocent human lives. Computed tomography (CT) scans are one of the primary imaging modalities for lung cancer diagnosis. However, manual CT scan analysis is time-consuming and prone to errors/not accurate. Considering these shortcomings, computational methods especially machine learning and deep learning algorithms are leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. In the present article, we proposed a novel transfer learning-based predictor called, Lung-EffNet for lung cancer classification. Lung-EffNet is built based on the architecture of EfficientNet and further modified by adding top layers in the classification head of the model. Lung-EffNet is evaluated by utilizing five variants of EfficientNet i.e., B0–B4. The experiments are conducted on the benchmark dataset “IQ-OTH/NCCD” for lung cancer patients grouped as benign, malignant, or normal based on the presence or absence of lung cancer. The class imbalance issue was handled through multiple data augmentation methods to overcome the biases. The developed model Lung-EffNet attained 99.10% of accuracy and a score of 0.97 to 0.99 of ROC on the test set. We compared the efficacy of the proposed fine-tuned pre-trained EfficientNet with other pre-trained CNN architectures. The predicted outcomes demonstrate that EfficientNetB1 based Lung-EffNet outperforms other CNNs in terms of both accuracy and efficiency. Moreover, it is faster and requires fewer parameters to train than other CNN based models, making it a good choice for large-scale deployment in clinical settings and a promising tool for automated lung cancer diagnosis from CT scan images.

Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 % of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7% and 90.8% respectively.

Internet of things (IoT) is realized by the idea of free flow of information amongst various low-power embedded devices that use the Internet to communicate with one another. It is predicted that the IoT will be widely deployed and will find applicability in various domains of life. Demands of IoT have lately attracted huge attention, and organizations are excited about the business value of the data that will be generated by deploying such networks. On the contrary, IoT has various security and privacy concerns for the end users that limit its proliferation. In this paper, we have identified, categorized, and discussed various security challenges and state-of-the-art efforts to resolve these challenges.

Though community empowerment and sustainable tourism development (STD) have been discussed in the existing literature, little research has focused on the elaborate mechanisms between these two variables. Therefore, the present study examines the relationship between community empowerment and STD, along with the mediating role played by community support for tourism. Using social exchange theory, this research establishes theoretical relationships between vital variables for STD. A survey of empirical study was undertaken, and data were collected from 353 local residents in the northern area of Pakistan. The results for data analyses demonstrated a significant relationship between community empowerment and STD initiatives, and community support for tourism was shown to partially mediate the relationship between the two variables. The findings imply that high community empowerment enables the community to establish successful sustainable tourism development through local people’s support for tourism. This study contributes theoretically to identifying the idea that community members’ support for tourism has a crucial function bridging the link from community empowerment to sustain tourism in a local area.

Purpose The purpose of this paper is to empirically examine the mediating role of potential and realized absorptive capacity in intellectual capital (IC) and business performance. It also investigates the direct impact of the components of IC on business performance. Design/methodology/approach Partial least square-structural equation modeling (PLS-SEM) was used to assess the effect of IC dimensions on performance and to analyze the mediating role of absorptive capacity in this relationship. Data were collected from 192 managers using a survey questionnaire with Likert scale items. Findings The findings of the study show that potential absorptive capacity does not intervene in the relationship between the components of IC and those of business performance. However, realized absorptive capacity, measured as the transformation and exploitation of knowledge, played a positive mediating role in the relationship between the dimensions of IC and those of business performance. Social capital was also noted as a weak predictor of business performance, while human capital and organizational capital had a profound positive influence. Originality/value This study contributes to the literature on IC by examining the role of realized and potential absorptive capacity in the relationship between IC components and firm performance. This research also helps practitioners recognize the importance of transformation and the exploitation of knowledge for business performance.

This study attempts to examine the role of sustainable Human Resource Management (HRM) practices on job performance and encompasses training as a moderator variable to further evaluate the association among HRM practices and employee’s job performance.The study seeks to measure the effect of selection, participation, and employee empowerment on job performance in the publicly owned universities of Pakistan. The descriptive survey research design was utilized for this study. The target population was the entire teaching staff of two publicly owned universities (namely “The University of Agriculture Peshawar” and “Hazara University Mansehra” Pakistan). By using a convenient sampling technique, 130 sample participants were selected from the target population. The reliability scales were tallied by using Cronbach’s Alpha. The findings of the study are gleaned by using regression to investigate the role of HRM practices in job performance and whether training moderated the association between HRM practices and employee performance. Through Statistical Package of Social Science (SPSS), Hayes process was used regarding the moderation effect of training between HRM practices and job performance. The main results of regression analysis validate that HRM practices, such as selection, participation, and employee empowerment, have a significant and positive effect on employee job performance. Specifically, the study suggests that training significantly moderates the effect of HRM practices on the performance of employees and that sustainability of HRM practices has a great impact on job performance. Based on the outcomes the study confirms that the proposed hypotheses are statistically significant. Furthermore, directions for future research are offered.

Purpose In this study, we have collected the response from 200 private university lecturers in Kurdistan Region of Iraq. In order to test the hypotheses, we have proposed structural equations modeling (SEM). Design/methodology/approach The purpose of this paper is to elaborate the direct and indirect effects of e-service quality on perceived value, satisfaction and willingness to pay for online meeting platforms in the education sector. This study also explores the effect of e-service quality on users' perception and satisfaction. Findings The results reveal that e-service quality directly affects the perceived value and satisfaction but has no direct effect on the willingness to pay. Secondly, perceived value and satisfaction mediated the relationships between service quality and willingness to pay. However, it is observed that perceived value has a more significant impact on the willingness to pay compared to satisfaction. It is further reported that perceived value is one of the antecedents of satisfaction. The study also explores the direct relationship between perceived value and willingness to pay, and introduces satisfaction as a mediating variable between perceived value and willingness to pay. Research limitations/implications The sample is geographically limited as only online faculty and staff working at private universities participated in the study. This study has implications for administrators of higher educational institutions and companies providing IT solutions for online meetings. From a managerial standpoint, this study provides and IT companies a broad theoretical basis that designing a successful online meeting platform should specifically emphasize e-service quality, perceived value and customer satisfaction. Originality/value There is no study that evaluated the links among e-service quality, value, satisfaction, and willingness to pay for the online meeting platform services. Therefore, this study is useful for the private university administration and online meeting platform developers and investors.