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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.

As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.

PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.

In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This study aims to explore the evidence-based usage patterns of higher education commission (HEC) subscribed e-journal databases in the university digital library used by the scholarly community and the academics’ online searching behaviour at a higher education institution in Pakistan.

The study used an explanatory sequential mixed methods approach. Raw transaction log data were collected for quantitative analysis, and the interview technique was used for qualitative data collection and thematic analysis.

Log analysis revealed that HEC subscribed databases were used significantly, and among those, scholarly databases covering various subjects were more frequently used than subject-specific society-based databases. Furthermore, the users frequently accessed the needed e-journal articles through search engines like Google and Google Scholar, considering them sources of free material instead of the HEC subscribed databases.

It provides practical implications for examining the evidence-based use patterns of e-journal databases. It suggests the need for improving the access management of HEC databases, keeping in view the usage statistics and the demands of the scholars. The study may also help create market venues for the publishers of scholarly databases by offering attractive and economical packages for researchers of various disciplines in developing and underdeveloped countries. The study results also guide the information professionals to arrange orientation and information literacy programs to improve the searching behaviour of their less frequent users and enhance the utilization of these subscribed databases.

The study is part of a PhD project and, to the best of the authors’ knowledge, is the first such work in the context of a developing country like Pakistan.

Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life.

This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong.

The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services.

Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.

The purpose of this paper is to present the perception of the textile industry stakeholders (manufacturers, wholesalers, retailers, consumers and tax professionals) on India’s new goods and services tax (GST) system and find whether the introduction of GST has made doing business easier or not.

The researchers used interviews and surveys to capture the perceptions of the textile industry stakeholders at Surat, a major textile hub in India. To econometrically verify the perceptions, the researchers used a logit regression model.

The researchers found that the provision of monthly tax filing has increased textile businesses’ dependency on tax professionals, which increased business costs. Also, the GST system has made tax compliance easier and is user-friendly. However, tax refund-related issues are a significant factor that negatively impacts the ease of doing business post-GST.

The findings of the research shall be helpful for the GST Council of India and policymakers to understand the problems faced by the textile businesses and cater to their problems.

To the best of the authors’ knowledge, this study is original as none of the available studies captures the perception of all the textile industry stakeholders, namely, manufacturers, wholesalers, retailers, consumers and tax professionals, on the GST system applying econometric techniques to validate the perceptions.

This paper aims to critically review the intersection of searching and learning among children in the context of voice-based conversational agents (VCAs). This study presents the opportunities and challenges around reconfiguring current VCAs for children to facilitate human learning, generate diverse data to empower VCAs, and assess children’s learning from voice search interactions.

The scope of this paper includes children’s use of VCAs for learning purposes with an emphasis on conceptualizing their VCA use from search as learning perspectives. This study selects representative works from three areas of literature: children’s perceptions of digital devices, children’s learning and searching, and children’s search as learning. This study also includes conceptual papers and empirical studies focusing on children from 3 to 11 because this age spectrum covers a vital transitional phase in children’s ability to understand and use VCAs.

This study proposes the concept of child-centered voice search systems and provides design recommendations for imbuing contextual information, providing communication breakdown repair strategies, scaffolding information interactions, integrating emotional intelligence, and providing explicit feedback. This study presents future research directions for longitudinal and observational studies with more culturally diverse child participants.

This paper makes important contributions to the field of information and learning sciences and children’s searching as learning by proposing a new perspective where current VCAs are reconfigured as conversational voice search systems to enhance children’s learning.

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

This paper aims to explore the use and impact of social media, specifically Twitter (now X), in political mobilization in Pakistan. It focuses on the events followed by the no-confidence motion against Imran Khan as Pakistan’s prime minister in April 2022 and the protest campaign that ensued, facilitated through the strategic use of the Urdu hashtag #امپورٹڈ_حکومت_نامنظور (translated as “imported-government unacceptable”) on Twitter, both within and outside Pakistan.

Using Web scraping, data from Twitter was extracted and analyzed between 2022 and 2023. By probing into user account profiles and interactions with this hashtag, this paper investigates the claims surrounding the hashtag’s popularity, by identifying suspicious accounts and their contributions in the trending of the hashtag.

Findings suggest that the claim of the hashtag's unprecedented success was overhyped, further suggesting that the popularity and impact of the social media campaign were exaggerated. Despite high engagement rates, the study indicates a discrepancy between perceived influence and actual impact on public sentiment and political mobilization.

This paper contributes to the literature on social media’s role in political mobilization and agenda-setting in the Pakistani context. More generally, understanding hashtag dynamics and their impact on shaping public opinion, may be beneficial to academics and practitioners in better understanding the role of digital platforms in the politics.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.