The advancement of mobile technologies has motivated countries around the world to aim for smarter health management to support senior citizens. However, the use of mobile health applications (mHealth apps) among senior citizens appears to be low. Thus, drawing upon user expectations, the present study examined user requirements for a senior-friendly mHealth application. A total of 74 senior citizens were interviewed to explore the difficulties they encounter when using existing mobile apps. This study followed Nielsen’s usability model to identify user requirements from five aspects, namely learnability, efficiency, memorability, error, and satisfaction. Based on the results, a guideline was proposed pertaining to usability and health management features. This guideline offers suggestions for mHealth app issues related to phrasing, menus, simplicity, error messages, icons and buttons, navigation, and layout, among others. The study also found that speech recognition technology can help seniors access information quickly. The proposed guideline and findings offer valuable input for software and app developers in building more engaging and senior-friendly mHealth apps.

The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Whilst collaboration from the branding and marketing perspective for the fashion industry is well documented, little is known about the knowledge collaboration (KC) of fashion designers, and how information and communication technology (ICT) is involved within their Communities of Practice (CoPs). This paper proposes an a priori conceptual model to enable the examination of KC among fashion designers within their CoPs. It aims to interlink the concepts of KC, CoPs, and ICT to understand fashion designers in their social learning reality. In that regard, the a priori conceptual model addresses three broad research questions: 1) How do fashion designers collaborate? 2) How do they participate within their professional communities? And 3) Does ICT play a role? The concepts used in the a priori conceptual model will be interlinked with specific indicators extracted from past studies. Since past studies had not focused on this area of research before, future studies can benefit from the a priori conceptual model by applying it to examine KC within CoPs of fashion designers or other groups.

Over the past decade, there has been an increase in hateful content on social media platforms, specifically in tweets. Hence, it brings a challenge to identify and classify tweets containing racism, discrimination, offense, toxicity, or abuse. This article proposes a novel hybrid approach that combines the power of Transformer-based language modeling with ensemble learning to classify offensive, toxic, and hateful tweets. Specifically, the robustly optimized bidirectional encoder representations from Transformers pretraining approach (RoBERTa)-large model is employed for feature extraction, followed by a hyperparameter-tuned extreme gradient boosting (XGBoost) classifier for final classification. The approach was evaluated on three widely used datasets ToxicTweets, Davidson, and HateSpeechDetection and compared against state-of-the-art methods, including deep architectures such as convolutional neural network (CNN), bidirectional encoder representations from Transformers (BERT), and AngryBERT; transformer models including DistilBERT, RoBERTa, A Lite BERT (ALBERT), and efficiently learning an encoder that classifies token replacements accurately (ELECTRA); and logistic regression. The experimental results demonstrate that the proposed hybrid model significantly outperforms existing approaches in terms of accuracy, precision, recall, and F1-score, achieving the highest accuracy of 97% on the HateSpeechDetection dataset and 92.42% on the Davidson dataset. Furthermore, the approach was compared with other ensemble methods, including Adaptive Boosting (AdaBoost), random forest, support vector classifier (SVC), light gradient boosting machine (LightGBM), and bagging, to highlight its superior performance. This study suggests that integrating RoBERTa-large with XGBoost is an effective approach for hate speech detection and provides a robust solution to the increasing problem of online hate and toxicity.

Aim/Purpose: The primary goal of this research is to analyze the factors that influence the implementation of Mobile Cloud Computing (MCC) in Higher Education Institutions (HEIs). Background: Despite MCC’s benefits in lowering technology and administration costs, HEIs, particularly in developing countries such as Palestine, are skeptical and hesitant to use this technology. This research provides a model of MCC implementation based on the Technology-Organization-Environment framework (TOE) combined with the Diffusion of Innovation (DOI) model. Methodology: Data were collected through qualitative semi-structured interviews with a targeted sample of ten IT experts and professionals from Palestinian Higher Education Institutions (PHEIs) and local cloud service providers. The obtained data was examined using thematic analysis in the NVIVO software. Contribution: The findings can be used as a guide for introducing MCC in higher education settings. It will lead to a better understanding of MCC implementation by HEIs, which can help institutions harness the benefits and progress offered by MCC, enabling them to overcome challenges and enhance their operations. The study will provide valuable suggestions for the initial steps HEIs can take towards MCC implementation. Moreover, it will benefit cloud service providers, as it will enhance their understanding of the factors that influence MCC implementation in HEIs. This will help them tailor their services and solutions better to meet the unique needs and requirements of the higher education sector. Findings: The results show that data security risks, relative advantages, compatibility, complexity, top management support, competitive pressure, and cloud service provider support all have a substantial impact on MCC implementation. The interviewed IT specialists also proposed two new contributing factors (technology readiness and government support). Recommendations for Practitioners: The results are expected to aid decision-makers at HEIs and cloud service providers by focusing on the crucial role of the factors that appeared in the study. Recommendation for Researchers: It is recommended that researchers in future studies test the proposed model in settings and cultures different from the context in which this study was conducted. In addition, surveying the opinions of other stakeholders and adding other factors not included in the research are important to provide different perspectives and generalize the results. Impact on Society: Understanding the factors that influence the implementation of MCC in HEIs can enhance access to educational resources for both learners and teachers while promoting innovation in teaching and learning. Additionally, this understanding aids in the development of educational institutions, enabling them to contribute to societal progress by improving the quality of education and fostering global cooperation, ultimately leading to the advancement of society. Future Research: Examining the long-term effects of MCC implementation on educational outcomes, student performance, and institutional efficiency will offer valuable insights into the enduring advantages and possible limitations of these technologies. Future research should also investigate the impact of emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the functionality and effectiveness of MCC within educational environments. Furthermore, conducting comparative research across various regions and educational systems might help to identify the socioeconomic factors that influence MCC acceptance and implementation.

This paper presents knowledge collaboration among tax professionals in a tax-knowledge context within Malaysian accounting associations through the conceptual lens of a community of practice. Semi-structured in-depth interviews were conducted with a total of 29 tax professionals. Additionally, data were also gathered from field notes and archival data. The findings revealed that the Malaysian accounting-professional associations reflected a community of practice. Knowledge collaboration occurs among members in this community in order to attain the highest standard of technical and professional competency in tax knowledge and practice. The findings from this study complement and expand previous research on CoP, knowledge management, and collaboration. The findings suggest exploring a better strategy to implement a central repository of knowledge acquired or generated by the members within the community to support the learning lifecycle.