Artificial Intelligence (AI) has enormous potential to make a transformative impact in multiple fields. It has made significant strides in Higher Education by reshaping traditional administrative processes, learning, leadership, and teaching. This review explores the substantial impact of integrating AI in Higher Education Institutions (HEIs), from improving education delivery to enhancing student outcomes and streamlining administrative processes and strategic leadership. By catering to the diverse learning needs of students with the help of tools that directly affect academics, monitor student engagement and performance, and provide data-driven interventions, AI offers what the HEIs have long been waiting for to revolutionize the overall Higher Education landscape. This review also highlights that with AI's ability to streamline administrative tasks by enhancing admissions and enrolment processes, academic records management system, and financial aid and scholarships processes, AI not only facilitates improving the overall processes but also makes staff and faculty members focus less on mundane and monotonous tasks, hence concentrating more on the responsibilities and strategic initiatives that require focused attention. We identified that the key to unlocking the significant potential of AI is responsible strategic leadership. Strategic leadership requires aligning AI integration goals with the strategic mission of HEIs, fostering an environment ready to embrace innovation and ensuring that the required accountability and governance frameworks are in place for AI integration and usage. It is also the role of leadership to consider ethical considerations, collaborations with the relevant stakeholders, concerns about job displacement, and potential biases, ensuring that AI is used to its full potential for the benefit of faculty, staff, students, and society. We conclude the paper with AI-driven future implications, i.e., emerging technologies, continuous enhancement and AI-based enhanced research accomplishments.

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

Background: The rapid development of modern technologies renders a convenient and efficient solution to implement Electronic Health Records (EHRs) systems. The rapid growth of healthcare data has a distinctive attribute of digital transformations. The big datasets of healthcare, their complexity and their dynamic nature have posed severe challenges associated with the analysis, pre-processing, privacy, security, storage, usability and data exchange. Material and Methods: We have performed the Systematic Literature Review (SLR) and followed the Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. SLR refers to the methodology that discovers, analyses and accesses recent research literature related to the subject field. The research papers were searched from academic repositories like IEEE, WOS, Scopus and PubMed for the previous five years on March 2023. Results: The designed search string provides 199 research articles in total. We filter the research articles based on inclusion-exclusion strategies and quality assessment metrics. Six main criteria for research inclusion-exclusion for SLR are formulated. These works of literature insight into 1) the issues associated with interoperability and security of EHRs by using the Blockchain (BC) technology, 2) different frameworks and tools to improve privacy and security in the healthcare domain, 3) the open issues of using BC technology in the electronic healthcare domain, 4) the standardized ways to store EHRs, 5) various ways to handle the big data using the BC systems and 6) the usage of Federated Learning (FL) to preserve the privacy of EHRs in the healthcare domain. We acquired 46 research articles based on the criteria (inclusion-exclusion) that investigate the above-mentioned issues. Conclusion: The SLR will serve as the state-of-the-art (SOTA) for future researchers in the field of BC in healthcare. Additionally, the paper provides insights to the new researchers to revolutionize the healthcare domain by adopting the latest digitalized technologies. The proposed study identified various reflections. It analyzed the architectural mechanism that supports the security and interoperability of EHRs. Secondly, the study described different tools and frameworks to improve the privacy and security of EHRs using the BC. Thirdly, the open issues of storing and preserving the EHRs using BC in the healthcare system were determined. Fourth, it analyzed and provided a detailed view of using standardized ways for storing and handling big data by using the BC system. Lastly, the usage of FL to preserve the privacy of EHRs was analyzed.

Misinformation can seriously impact society, affecting anything from public opinion to institutional confidence and the political horizon of a state. Fake News (FN) proliferation on online websites and Online Social Networks (OSNs) has increased profusely. Various fact-checking websites include news in English and barely provide information about FN in regional languages. Thus the Urdu FN purveyors cannot be discerned using fact-checking portals. State-of-the-art (SOTA) approaches for Fake News Detection (FND) count upon appropriately labelled and large datasets. FND in regional and resource-constrained languages lags due to the lack of limited-sized datasets and legitimate lexical resources. The previous datasets for Urdu FND are limited-sized, domain-restricted, publicly unavailable and not manually verified where the news is translated from English into Urdu. In this paper, we curate and contribute the first largest publicly available dataset for Urdu FND, "Ax-to-Grind Urdu", to bridge the identified gaps and limitations of existing Urdu datasets in the literature. It constitutes 10,083 fake and real news on fifteen domains collected from leading and authentic Urdu newspapers and news channel websites in Pakistan and India. FN for the Ax-to-Grind dataset is collected from websites and crowdsourcing. The dataset contains news items in Urdu from the year 2017 to the year 2023. Expert journalists annotated the dataset. We benchmark the dataset with an ensemble model of mBERT, XLNet, and XLM-RoBERTa. The selected models are originally trained on multilingual large corpora. The results of the proposed model are based on performance metrics, F1-score, accuracy, precision, recall and MCC value. F1-score of 0.924, accuracy of 0.956, precision of 0.942, recall of 0.940 and an MCC value of 0.902 demonstrate the effectiveness of the proposed approach for Urdu FND. Comparison analysis with SOTA ML and DL models and existing Urdu benchmark datasets exhibit that the ensemble model outperforms them for Urdu FND. The dataset used for our experiments is publicly available at https://github.com/HjH-Whu-CRC/Ax-to-Grind-Urdu for further analysis and validation.

Fake News (FN) prevalence on Online Social Networks (OSNs) and online websites is a worldwide issue. The previous studies on Fake News Detection (FND) have focused on rich-resource languages with limited relevance to users other than native speakers. Despite the progressing multilingual approaches, FND in low-resource languages remains obscure due to a lack of large-sized annotated corpora of real-world news. Large Language Models (LLMs) have emerged as a promising solution for multilingual FND. This study leverages the power of LLMs for an automated mechanism compared to traditional feature extraction methods. We curate the first large-sized multi-domain corpus, the Hook and Bait Urdu, with 78,409 fake and true news, and fine-tune the LLaMA 2 model for our proposed approach. We implement the curated dataset for two experiments. First, this study evaluates the dataset for unimodal text-based Urdu FND. The proposed LLaMA-based approach shows an accuracy of 0.978 and an F1-score of 0.971. For our second experiment, we fine-tuned the LLaMA 2-based framework for multilingual FND using the curated dataset (in Urdu) and the ISOT Fake News dataset (in English). Analytical and prediction performance comparisons with the previous studies validate the efficacy of the proposed framework with an accuracy of 0.984 and an F1-score of 0.980. The lightweight LoRA fine-tuning method, with 0.032% trainable parameters, ensured robust data handling, computational efficiency while leveraging early stopping and optimized hyperparameters for reliable and high-performing monolingual and multilingual FND. The real-world news dataset is publicly available for developing an automated FND mechanism to curb the threat of FN and related cybercrimes.

Fake News (FN) dissemination on websites and online platforms influences human behaviours, socio-political domains, and the sovereignty of a country. The outpour of biased news and propaganda on online portals can be addressed by restricting online propaganda using an automated mechanism. Proving the authenticity of news and information on online platforms in regional languages, such as Urdu, with limited resources and datasets, is challenging. Furthermore, limited research in resource-constrained languages has created language bias in Artificial Intelligence (AI) research, which is concentrated in this study. Natural Language Processing (NLP) techniques have been used for Fake News Detection (FND) for English news and various language-related tasks. Previous studies used Machine Learning (ML), Deep Learning (DL), and individual Pre-trained Language Models (PLMs) for Urdu FND. ML-based ensemble model showed better performance than pre-trained models for Urdu FND. We propose a methodology for Urdu FND by applying stacked ensemble learning of PLMs, ELECTRA, mBERT and XLM-RoBERTa after apposite fine-tuning and hyperparameter optimization. To overcome the limitations of each pre-trained transformer model, these are fine-tuned individually using a publicly available Urdu dataset. The prediction performance results of the proposed stacking approach surpass the performance of each pre-trained model. An Accuracy of 0.914, a Matthews Correlation Co-efficient (MCC) value of 0.898, and an F1-score of 0.904 validate the efficacy of the proposed ensemble model.