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Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

Digital learning systems are crucial for education and data collected can analyse students learning performances to improve support. The purpose of this study is to design and build an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet.

A Learning Box has been build based on minicomputer and a web learning management system (LMS). This study presents different options to create such a system and discusses various approaches for data syncing. The structure of the final setup is a Moodle (Modular Object Oriented Developmental Learning Environment) LMS on a Raspberry Pi which provides a Wi-Fi hotspot. The authors worked with lecturers from X University who work in remote Northern Territory regions to test this and provide feedback. This study also considered suitable data collection and techniques that can be used to analyse the available data to support learning analysis by the staff. This research focuses on building an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet. Digital learning systems are crucial for education, and data collected can analyse students learning performances to improve support.

The resultant system has been tested in various scenarios to ensure it is robust when students’ submissions are collected. Furthermore, issues around student familiarity and ability to use online systems have been considered due to early feedback.

Monitoring asynchronous collaborative learning systems through analytics can assist students learning in their own time. Learning Hubs can be easily set up and maintained using micro-computers now easily available. A phone interface is sufficient for learning when video and audio submissions are supported in the LMS.

This study shows digital learning can be implemented in an offline environment by using a Raspberry Pi as LMS server. Offline collaborative learning in remote communities can be achieved by applying asynchronized data syncing techniques. Also asynchronized data syncing can be reliably achieved by using change logs and incremental syncing technique.

Focus on audio and video submission allows engagement in higher education by students with lower literacy but higher practice skills. Curriculum that clearly supports the level of learning required for a job needs to be developed, and the assumption that literacy is part of the skilled job in the workplace needs to be removed.

To the best of the authors’ knowledge, this is the first remote asynchronous collaborative LMS environment that has been implemented. This provides the hardware and software for opportunities to share learning remotely. Material to support low literacy students is also included.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

This study aims to investigate the effects of organizational culture factors on the selection of software process development models and develops a conceptual model for selecting and adopting process development models with an organizational culture approach, using 12 criteria and their sub-criteria defined in Fey and Denison’s model (12 criteria).

The research hypotheses were investigated using statistical analysis, and then the criteria and sub-criteria were selected based on Fey and Denison’s model and the experts’ viewpoints. Afterward, the organizational culture of the selected company was measured using the data from 2016 and 2017, based on Fey and Denison’s questionnaire. Due to the correlation between the criteria, using the decision-making trial and evaluation technique, the correlation between sub-criteria were determined, and by analytical network process method and using Super-Decision software, the process development model was preferred to the 12 common models in information systems development.

Results indicated a significant and positive effect of organizational culture factors (except the core values factor) on the selection of development models. Also, by changing the value of organizational culture, the selected process development model changed either. Sensitivity analysis performed on the sub-criteria implied that by changing and improving some sub-criteria, the organization will be ready and willing to use the agile or risk-based models such as spiral and win-win models. Concerning units where the mentioned indicators were at moderate and low limits, models such as waterfall, V-shaped and incremental worked more appropriately.

While many studies were performed in comparing development models and investigating their strengths and weaknesses, and the impact of organizational culture on the success of information technology projects, literature indicated that the impact of organizational sub-culture prevailing in the selection of development process models has not been investigated. In this study, new factors and indicators were addressed affecting the selection of development models with a focus on organizational culture. Correlation among the factors and indicators was also investigated and, finally, a conceptual model was proposed for proper adoption of the models and methodologies of system development.