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Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

The young population of the globe is defined by individuals aged 15 to 24 years. Based on statistics from the Instituto Brasileiro de Geografia e Estatística (IBGE), the second largest women population among 15 years as well as 19 years was in 2017 only behind 35 and 39 years. At this time, the Brazilian male population was higher. The difficulties of the young generation affected the preceding generation and promoted social dynamism. The worldwide data shows that the generation of young and the digital world have been constantly sought, but in reality, approximately one-third of the population in 2017 had no access to the internet.

The worldwide movement around topics such as strategy on its threefold basis and Industry 4.0 enable a link to company duty towards society to be established. This present study was produced from 1 March 2020 to 2 September 2020 via resources of human and literature evaluation relating to the idea of strategic, Industry 4.0, the responsibility of society and the creation of youth. Its motive is the global creation of youth. Two recommendations should be made after studying the literature and information gathering that enabled “analyzing social responsibility of the company and industry 4.0 with a pivot on young creation: a strategic framework for resources of human management”.

The adoption of defensible practices and technology bring forth by the revolution in industrial is emphasized worldwide.

The focus on the usage of these ideas is essential, so that young people can absorb the workforce in the labour market. To achieve this, the CSR idea combines this theoretical triple-created recent study.

This study aims to predict the effect of cultural distance, perceived risk and electronic word of mouth (eWOM) on higher education institutes' students' destination image. In addition, it examines the mediating role of destination image in relation to students' travel intentions.

An online survey was employed to collect data from 200 graduate and postgraduate students. The partial least squares was employed to analyse the hypothesised relationships.

The results of this study found support for the positive effect of cultural distance and eWOM on destination image. Additionally, the mediating effect of destination image was also supported.

This research confirms the vital role of destination image as an antecedent of students' future intention to visit the destination. Moreover, this study contributes to marketing theory by predicting the critical drivers of higher education students' destination image and discussing their applications in the education sector.

The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions.

The paper deals with the design of an MRI compatible electrical actuator. Three-dimensional electromagnetic and thermal analytical models have been developed to design the actuator. These models have been validated through 3D finite element (FE) computations. The analytical models have been inserted in an optimization procedure that uses genetic algorithms to find the optimal parameters of the actuator.

The analytical models are very fast and precise compared to the FE models. The computation time is 0.1 s for the electromagnetic analytical model and 3 min for the FE one. The optimized actuator does not perturb imaging sequence even if supplied with a current 10 times higher than its rated one. Indeed, the actuator’s magnetic field generated in the imaging area does not exceed 1 ppm of the B₀ field generated by the MRI scanner. The actuator can perform up to 25 biopsy cycles without any risk to the actuator or the patient since he maximum temperature rise of the actuator is about 20°C. The actuator is compact and lightweight compared to its pneumatic counterpart.

The MRI compatible actuator uses the B₀ field generated by scanner as inductor. The design procedure uses magneto-thermal coupled models that can be adapted to the design of a variety actuation systems working in MRI environment.

Administrative processes play a crucial role in various sectors, often contributing to cost overruns and delays. While lean concept has been extensively applied in many industries, there is a recognized need for further research on the integration of lean in administrative processes. To address this research gap, this study aims to systematically analyse the implementation of lean in administrative processes, providing a sector-wise comparison to explore its prospects for the construction industry.

A systematic literature review was conducted by adapting the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. Eighty-six research papers are subjected to descriptive and content analysis on different tools applied in lean integrated administrative processes in health care, education, public administration and other sectors.

The research study reveals common lean tools irrespective of sector. Moreover, specific tools used for a particular sector are analysed. Out of the various lean tools and techniques, value stream mapping, 5S and Kaizen are highly acknowledged tools in every sector of lean intervention. Furthermore, ECRS is applied explicitly in the education sector. Furthermore, it is noted that Suppliers, Inputs, Process, Outputs and Customers (SIPOC), 5whys, load levelling and control charts are identical to administrative processes in lean health care. Furthermore, Spaghetti charts and Kaikaku are only adapted to administrative processes in the public administration sector, and causal loop diagrams are used explicitly in administrative processes in the construction sector.

This study contributes to research by developing a guideline to design lean intervention for a lean-integrated administrative process in any sector which still needs to be transformed into lean. Hence, this study will be a cornerstone for incorporating lean principles for administrative processes in construction sector.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.