In the dynamic world of higher education, technological advancements are continually reshaping teaching and learning approaches, with learning analytics (LA) playing a crucial role in this transformation. This systematic literature review (SLR) explores the significant impact of LA in higher education, specifically its transformative role in personalizing and enhancing educational feedback mechanisms. Utilizing a wide range of educational data, LA facilitates a shift from generic to individualized feedback, leading to improved learning outcomes and equity. However, incorporating LA into higher education is not without challenges, ranging from data privacy concerns to the possibility of algorithmic errors. Addressing these challenges is vital for unlocking the full potential of LA. This paper also examines emerging LA trends, such as augmented reality, emotion-sensing technology, and predictive analytics, which promise to further personalize learning experiences in higher education settings. By anchoring these advancements within core educational principles, we foresee a future of education marked by innovation and diversity. This SLR provides an overview of LA’s evolution in higher education, highlighting its transformative power, acknowledging its challenges, and anticipating its future role in shaping a dynamic, responsive educational environment.

Deep learning has quickly evolved over the past several years from a promising to a feasible solution for medical image analysis. This is a great topic for research because more and more people are using medical imaging to treat and diagnose. A significant benefit of deep learning is the ability to use enormous volumes of data to eliminate the painstaking hand-crafting of features, which needs strong domain expertise. This study discusses how convolutional neural networks (CNNs) are utilized in the field, including detection, classification, registration, segmentation, and picture enhancement. It also gives some important information about how CNNs can be used to analyze medical images of the brain, eye, breast, chest, and skin.

Deepfake-style AI tutors are emerging in online education, offering personalized and multilingual instruction while introducing risks to integrity, privacy, and trust. This study aims to understand their pedagogical potential and governance needs for responsible integration. A PRISMA-guided, systematic review of 42 peer-reviewed studies (2015–early 2025) was conducted from 362 screened records, complemented by semi-structured questionnaires with 12 assistant professors (mean experience = 7 years). Thematic analysis using deductive codes achieved strong inter-coder reliability (κ = 0.81). Four major themes were identified: personalization and engagement, detection challenges and integrity risks, governance and policy gaps, and ethical and societal implications. The results indicate that while deepfake AI tutors enhance engagement, adaptability, and scalability, they also pose risks of impersonation, assessment fraud, and algorithmic bias. Current detection approaches based on pixel-level artifacts, frequency features, and physiological signals remain imperfect. To mitigate these challenges, a four-pillar governance framework is proposed, encompassing Transparency and Disclosure, Data Governance and Privacy, Integrity and Detection, and Ethical Oversight and Accountability, supported by a policy checklist, responsibility matrix, and risk-tier model. Deepfake AI tutors hold promise for expanding access to education, but fairness-aware detection, robust safeguards, and AI literacy initiatives are essential to sustain trust and ensure equitable adoption. These findings not only strengthen the ethical and governance foundations for generative AI in higher education but also contribute to the broader agenda of sustainable digital education. By promoting transparency, fairness, and equitable access, the proposed framework advances the long-term sustainability of learning ecosystems and aligns with the United Nations Sustainable Development Goal 4 (Quality Education) through responsible innovation and institutional resilience.

In this present era of technology, segmentation of image is a fundamental and significant component of computer vision and image processing applications. These applications include medical image analysis, compression of images, surveillance, security footage, and many others. Researchers have proposed and developed various models, theories, and algorithms in this advancing field of image segmentation. Due to the success of a wide range of computer vision applications, work whose goal was to use deep learning algorithms for the development and usability of image segmentation was completed not long ago. In this study, a detailed review of the literature on image segmentation is being written, which covers a vast and extensive amount of work done by researchers at the time of writing this review. This review investigates the similarity and differences between the proposed models by researchers in the past. Along with similarities, we have also discussed the strength and challenges of the model under the umbrella of deep learning algorithms. In the end, future work has been discussed along with keeping in view the past work of researchers.

The primary function of Big Data analytics is the storage and processing of data. The term Artificial Internet of Things (AIoT) refers to the intelligent network that links together people, data, objects, and processes. The purpose of this chapter is to investigate the existing frameworks for handling Big Data concepts and their analytics in the development of AIoT networks. Big Data is based on information acquired from several sources, including sensor nodes installed in intelligent things, network devices, and users. Data need predictive analysis, planning, and management. Precise data are used to control the surrounding environment. However, as automation improves, Big Data handling is a significant issue. This chapter proposes a Big Data analytics model to handle AIoT networks by using data pre-processing, classification, and prediction methods. This chapter also provides a comprehensive look at how Big Data analytics can be applied to the task of creating a better AIoT network. In addition, this chapter briefly examines the many fascinating Big Data analytics possibilities for AIoT networks that might transform society. The chapter concludes with AIoT and Big Data analytics research prospects and challenges. <!----> <!----> <!----> <!---->

In this study, all of the methods utilized to segment cardiac images have been evaluated. Recent research reveals that new deep learning approaches using high-speed GPUs provide effective results when compared to more classical image segmentation methods. Along with the evaluation of research, all of the accessible cardiac imaging datasets are documented in a comprehensive manner.