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Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue.

The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider.

The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents.

The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour.

This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit.

This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.

In this chapter, we investigate the role of the Internet of Things (IoT) for a more sustainable future. The IoT is an umbrella term that refers to an interrelated network of devices connected to the internet. It also encompasses the technology that enables communication between these devices as well as between the devices and the cloud. The emergence of low-cost microprocessors, sensors and actuators, as well as access to high bandwidth internet connectivity, has led to the massive adoption of IoT systems in everyday life. IoT systems include connected vehicles, connected homes, smart cities, smart buildings, precision agriculture, among others. During the last decade, they have been impacting human activities in an unprecedented way. In essence, IoT technology contributes to the improvement of citizens' quality of life and companies' competitiveness. In doing so, IoT is also contributing to achieve the Sustainable Development Goals (SDGs) that were adopted by the United Nations in 2015 as an urgent call to action by all countries to eradicate poverty, tackle climate change and ensure that no one is left behind by 2030. The World Economic Forum (WEF) recognises that IoT is undeniably one of the major facilitators for responsible digital transformation, and one of its reports revealed that 84% of IoT deployments are presently addressing, or can potentially address the SDGs. IoT is closely interlinked with other emerging technologies such as Artificial Intelligence (AI) and Cloud Computing, for the delivery of enhanced and value-added services. In recent years, there has been a push from the IoT research and industry community together with international stakeholders, for supporting the deployment and adoption of IoT and AI technologies to overcome some of the major challenges facing mankind in terms of protecting the environment, fostering sustainable development, improving safety and enhancing the agriculture supply chain, among others.

In the present study, we examined how effectively online travel agencies (OTAs) handle negative e-word-of-mouth on social media platforms like Facebook, Twitter, and Instagram. We collected data from 497 participants using survey method. To test the hypotheses formulated from the existing literature, structural equation modeling was adopted in this study. The results from structural equation modeling indicate effective handling of the negative e-word of mouth (e-WOM) on social media websites significantly affects customer satisfaction and repurchase intention. The current research work provides insight into social media recovery efforts and service fairness when handling negative e-WOM. The study recommends that customers can distinguish the differences between general efforts and adaptive complaint-handling efforts, and dissimilarities may influence satisfaction, repurchase intentions, etc. Although empathy, apology, responsiveness, and paraphrasing are considered pioneer strategies in complaint handling, customers' negative e-WOM, and firms' recovery management, but the current study is among a few to categorize OTAs' handling of negative e-WOM and complaint handling efforts in the social media environment.

This study aims to determine the most similar set of recommendation books to the user selections in LibraryThing.

For this purpose, 30,000 tags related to History on the LibraryThing have been selected. Their tags and the tags of the related recommended books were extracted from three different recommendations sections on LibraryThing. Then, four similarity criteria of Jaccard coefficient, Cosine similarity, Dice coefficient and Pearson correlation coefficient were used to calculate the similarity between the tags. To determine the most similar recommended section, the best similarity criterion had to be determined first. So, a researcher-made questionnaire was provided to History experts.

The results showed that the Jaccard coefficient, with a frequency of 32.81, is the best similarity criterion from the point of view of History experts. Besides, the degree of similarity in LibraryThing recommendations section according to this criterion is equal to 0.256, in the section of books with similar library subjects and classifications is 0.163 and in the Member recommendations section is 0.152. Based on the findings of this study, the LibraryThing recommendations section has succeeded in introducing the most similar books to the selected book compared to the other two sections.

To the best of the authors’ knowledge, itis for the first time, three sections of LibraryThing recommendations are compared by four different similarity criteria to show which sections would be more beneficial for the user browsing. The results showed that machine recommendations work better than humans.

The purpose of this study is to draw from the conservation of resources (COR) theory and investigate two separate models termed Model A and Model B. Model A examines the mediating role of life satisfaction (LS) on the relationship between servant leadership (SL) and lecturers’ attitudinal loyalty (AL) and behavioral loyalty (BL). Model B examines the sequential mediating role of LS and AL on the link between SL and BL.

Data were collected using a cross-sectional survey from 247 public university lecturers which were analyzed through SPSS, structural equation model (AMOS 23), and PROCESS Macro v4.0.

Study findings revealed that LS fully and partially mediates the relationships between SL and lecturers’ AL and BL, respectively. Furthermore, LS and AL sequentially mediate the relationship between SL and BL.

The study provides insight to university management into how their selfless and caring behavior can contribute to lecturers' retention. As such, university management should provide an environment that fosters a culture of selfless and caring leadership behavior.

The study contributes to the theoretical development of SL by explicating the mechanism that links SL and positive outcomes in the workplace. The major contribution lies in exploring the mediating role of LS on the link between SL and lecturers’ AL and BL on one hand and the sequential mediating role of LS and AL on the link between SL and BL on the other hand in a context characterized by high-power distance.

Vertical handover has been grown rapidly due to the mobility model improvements. These improvements are limited to certain circumstances and do not provide the support in the generic mobility, but offering vertical handover management in HetNets is very crucial and challenging. Therefore, this paper presents a vertical handoff management method using the effective network identification method.

This paper presents a vertical handoff management method using the effective network identification method. The handover triggering schemes are initially modeled to find the suitable position for starting handover using computed coverage area of the WLAN access point or cellular base station. Consequently, inappropriate networks are removed to determine the optimal network for performing the handover process. Accordingly, the network identification approach is introduced based on an adaptive particle-based Sailfish optimizer (APBSO). The APBSO is newly designed by incorporating self-adaptive particle swarm optimization (APSO) in Sailfish optimizer (SFO) and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is utilized for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like end to end (E2E) delay, jitter, signal-to-interference-plus-noise ratio (SINR), packet loss, handover probability (HOP) are considered to find the best network.

The developed APBSO-based deep stacked autoencoder outperformed than other methods with a minimal delay of 11.37 ms, minimal HOP of 0.312, maximal stay time of 7.793 s and maximal throughput of 12.726 Mbps, respectively.

The network identification approach is introduced based on an APBSO. The APBSO is newly designed by incorporating self-APSO in SFO and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is used for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like E2E delay, jitter, SINR, packet loss and HOP are considered to find the best network. The developed APBSO-based deep stacked autoencoder outperformed than other methods with minimal delay minimal HOP, maximal stay time and maximal throughput.

The purpose of this study is to identify the level of quality of work life (QWL) of employees working in the Garment industries using a validated scale.

Survey methods were used for this study. A questionnaire was designed to collect the data and information, and it is validated through exploratory factor analysis, confirmatory factor analysis.

The majority of employees are not satisfied with the present status of QWL in garment units. Followings are the predominant components, which influence the QWL of employees compensation and rewards; job security; grievance handling; work environment; training and development; job nature; satisfaction in job; facilities and relation and cooperation.

The study was conducted in 133 garment industries where sample responses were obtained from 851 workers working in Indian Garment industries. In the competitive business environment, retaining a talented workforce is one of the big challenges to the organization. An unsatisfied employee is the first enemy of the organization, it is the prime task of the employers to keep the workforce at a satisfying level, otherwise, it will lead to employee turnover, performance and productivity. This paper helps to identify and quantify the components of the quality of work-life of employees if employers address these components job satisfaction level of employees will increase; therefore, our results will help the HR managers and policymakers to take appropriate decisions to enhance QWL.

The purpose of this study is to determine the enablers of the quality of work life (QWL) of employees working in the Garment industries.

The study was carried out in a fivefold step. In the first step, the enablers of QWL were identified through an exhaustive literature survey, in the second step identified vital few components through Pareto analysis. Then the third step was followed by exploratory factor analysis (EFA) to further, to identify the precise components and validate the same using confirmatory factor analysis in fourth step. The final step included interpretive structural modeling and Cross-Impact Matrix Multiplication Applied to Classification analysis to model the validated components and determine the interrelationships and linkages.

Predominant QWL enablers of employees working in the garment industries are training and development, satisfaction in job, compensation and rewards, relation and co-operation, grievance handling, work environment, job nature, job security and facilities.

In this study, the interpretive structural model is designed based on the opinion of the experts who are working in the garment industry considering the responses from employees in garment sectors. The framework can be extended further to the other sectors.

In future, the researchers in QWL may develop a model to quantify the level of employees’ QWL who are working in different sectors. Enablers of QWL are essential, and based on this further statistical analysis can be carried out. This study will provide limelight to the researchers in choosing the valid and reliable set of enablers for the empirical studies. Organizations can get benefit by implementing the outcome of this research for the enhancement of the QWL of employees.

The study was carried out in 133 garment industries where 851 workers constituted the final valid responses that were considered for analysis. The outcomes from the study help administrators, policy and decision-takers in taking decisions to enhance QWL.

Integrating complementary information with high-quality visual perception is essential in infrared and visible image fusion. Contrast-enhanced fusion required for target detection in military, navigation and surveillance applications, where visible images are captured at low-light conditions, is a challenging task. This paper aims to focus on the enhancement of poorly illuminated low-light images through decomposition prior to fusion, to provide high visual quality.

In this paper, a two-step process is implemented to improve the visual quality. First, the low-light visible image is decomposed to dark and bright image components. The decomposition is accomplished based on the selection of a threshold using Renyi’s entropy maximization. The decomposed dark and bright images are intensified with the stochastic resonance (SR) model. Second, texture information-based weighted average scheme for low-frequency coefficients and select maximum precept for high-frequency coefficients are used in the discrete wavelet transform (DWT) domain.

Simulations in MATLAB were carried out on various test images. The qualitative and quantitative evaluations of the proposed method show improvement in edge-based and information-based metrics compared to several existing fusion techniques.

In this work, a high-contrast, edge-preserved and brightness-improved image is obtained by the processing steps considered in this work to get good visual quality.