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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.

This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.

To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.

The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.

Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.

This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.

It is becoming easier for end-users to modify their information system, sometimes against the wishes of management or the original manufacturer. In the mobile device context, “modding”, “jailbreaking” or “rooting” allows a mobile phone user to select operating software and network providers other than those mandated by the original provider. Prior studies have theorised that modders and non-modders possess different perspectives on the relationship between them and their device provider. However, these differences have not been empirically demonstrated in prior work. This paper aims to test theoretical pathways to explain the behavioural relationship effects of security within the modding context.

This study models four relationship conceptualisations from prior research. The study tests this model using a survey of 464 mobile device users to compare the user attitudes of modders and non-modders.

Modder and non-modder relationships differ. Both groups value security, but in different ways: modder relationships are governed by satisfaction and commitment, while non-modder relationships are governed more by trust.

To the best of the authors’ knowledge, this is the first study to empirically demonstrate the relationship differences between IS modifiers and non-modifiers. Most published work has focused on IS that are unmodified. Incorporating device modification may improve behavioural understanding of end-users.

Cannabis as a recreational drug is prohibited in Nigeria. Consequently, the open use of cannabis attracts both formal and informal sanctions. As such, there is much stigma on users' faces across social spaces. This has led to innovations in drug use. Recently, non-medical use of tramadol has been rising across each of the gender categories. This study aims to understand (1) tramadol use prompts, (2) the sudden surge in gendered recreational use of tramadol and (3) the gendered challenges of recreational tramadol use among Nigerian university students.

Data were collected between December 2021 and October 2022. The researcher conducted 20 semi-structured interviews, with participants chosen purposefully from acquaintances and others selected through chain referral. The selection criteria included knowledge of a particular tramadol tablet and willingness to participate. This study got ethical approval from the Anambra State Ministry of Health (Ref: MH/AWK/M/321/354) and oral consent was obtained before the interviews. The participants were assured of confidentiality. The interviews were conducted in English (the formal Nigerian language) and lasted between 30 and 65 min. The data collected were transcribed and coded manually, and themes generated.

Findings suggest that peer pressures accounted for entry-level drug use. However, cannabis-related stigma is attributed to the surge in tramadol use across gender categories. Furthermore, academic pressure and sexuality are major reasons for tramadol use. The challenges associated with tramadol use include headaches and addiction.

This study, to the best of the author’s knowledge, presents alternative data on the surge in tramadol use among Nigerian university students.

Developed countries control pandemics using smart decisions and processes based on medical standards and modern technologies. Studies on risk-reduction and humantechnology interaction are scarce. This study developed a model to examine the relationship between citizens, pandemic-related technology and official safety practices.

This study investigated the mediating role of new health regulations and moderating role of safety incentives due to COVID-19 case reduction in pandemic severity control. This study included 407 operations managers, nursing staff conducting pandemic testing and reporting, doctors and security personnel in China. An artificial neural network (ANN) was used to check nonlinear regressions and model predictability.

The results demonstrated the impact of the introduction of new technology protocols on the implementation of new health regulations and aided pandemic severity control. The safety incentive of case reductions moderated the relationship between new health regulations and pandemic severity control. New health regulations mediated the relationship between the introduction of new technology protocols and pandemic severity control.

Further research should be conducted on pandemic severity in diversely populated cities, particularly those that require safety measures and controls. Future studies should focus on cloud computing for nurses, busy campuses and communal living spaces.

Authorities should involve citizens in pandemic-related technical advances to reduce local viral transmission and infection. New health regulations improved people's interactions with new technological protocols and understanding of pandemic severity. Pandemic management authorities should work with medical and security employees.

This study is the first to demonstrate that a safety framework with technology-oriented techniques could reduce future pandemics using managerial initiatives.

In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.

To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.

The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.

This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.