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Artificial intelligence (AI) is reshaping humanity's future, and this manuscript provides a comprehensive exploration of its implications, applications, challenges, and opportunities. The revolutionary potential of AI is investigated across numerous sectors, with a focus on addressing global concerns. The influence of AI on areas such as healthcare, transportation, banking, and education is revealed through historical insights and conversations on different AI systems. Ethical considerations and the significance of responsible AI development are addressed. Furthermore, this study investigates AI's involvement in addressing global issues such as climate change, public health, and social justice. This paper serves as a resource for policymakers, researchers, and practitioners understanding the complex link between AI and humans.

Cloud computing has been considered a core model of elastic on-demand resource allocation using a pay-as-you go model. One of the big challenges of this environment is to provide high quality service (QoS) through efficient and stringent management of cloud data center resources. With the increasing demand for cloud based services, the traffic volume inside cloud data centers (DC) has been increased exponentially. Accordingly, and to provide high QoS, a proper scheduling mechanism has to be followed by the cloud service provider. Furthermore, accurate scheduling is necessary for advancing the problem of energy consumption and resource utilization. In this paper, we propose an optimal resource allocation and consolidation virtual machine (VM) placement model for multi-tier applications in modern large cloud DCs. The proposed model targets to optimize the DCs’ energy and communication cost that influence the overall cloud performance through Software Defined Networking (SDN) control features. To solve the formulated multi-objective optimization problem, a novel adaptive genetic algorithm is proposed. The experimental results validate the efficacy of the proposed model through extensive simulations using synthetic and real workload traces. These results show that the proposed model jointly optimizes cloud QoS as well as energy consumption.

The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management.

Artificial intelligence (AI) technologies have the potential to completely transform how we teach and learn in higher education. ChatGPT, a language model developed by OpenAI, is one such tool that can deliver individualized recommendations to students, increase collaboration and communication, and improve student learning results. However, there are some obstacles to overcome, such as ethical concerns and implementation issues. This study reviews related work on the use of artificial intelligence in education, with a focus on ChatGPT and its possible applications in higher education. It also examines the benefits and drawbacks of adopting ChatGPT in higher education, as well as implementation advice. Finally, the report discusses future directions for ChatGPT research in higher education. According to the findings of this paper, ChatGPT represents a significant opportunity for higher education institutions to improve the quality and accessibility of education; however, its implementation must be approached with caution and a clear understanding of the opportunities and challenges involved.

Alzheimer’s disease (AD) is an irreversible and progressive brain disease causing brain degenerative disorder and dementia. An early diagnosis of AD provides the individual an opportunity to participate in clinical trials. Computer Aided Diagnosis (CAD) system in the health care sector has been widely used and plays an important role in detecting such diseases. However, the main challenge of such systems is through identifying the region of interest obtained through precise segmentation. This paper attempts to solve the segmentation issue by developing a precise image segmentation model. The proposed model used a derivation of a hybrid cross entropy thresholding technique for the precise extraction of infected regions. In other words, a novel segmentation methodology has been proposed using the output derivation of both Gamma and Gaussian distributions. Moreover, to tackle the performance and time-consuming problems in digital image segmentation, a parallel boosting methodology has been developed and implemented. Through using the ADNI, OASIS, and MIRIAD benchmark datasets, the experimentation results validate the effectiveness of the proposed model through achieving more than 90% accuracy with 2x times speed improvement compared to other benchmark segmentation methods.

PurposeImage segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.Design/methodology/approachThe proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.FindingsOn the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.Originality/valueA novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

In the Internet of Medical Things (IoMT), intrusion detection systems (IDS) must be lightweight yet capable of modeling highly skewed, burst-like traffic produced by medical devices and real-time monitoring nodes. Traditional Gaussian Naïve Bayes often underperforms in this setting due to its assumption of symmetric feature distributions. This paper introduces NBGamma, a modified Naïve Bayes classifier that replaces the Gaussian likelihood with a Gamma-based likelihood formulation, enabling more accurate modeling of positively skewed traffic features commonly observed in IoMT networks. Unlike prior Naïve Bayes extensions, the proposed approach mathematically integrates the Gamma distribution into the likelihood computation and formally models the IDS task as a security-driven optimization problem. Experiments on the CSE-CIC-IDS2018 dataset demonstrate significant improvements in precision, recall, F1-score, and ROC-AUC compared to Gaussian NB under both unbalanced and SMOTE-balanced settings. These results highlight the ability of NBGamma to more effectively capture malicious traffic patterns, advancing lightweight IDS modeling for resource-constrained IoMT environments. Article Highlights Strengthens cyberattack detection in Internet of Medical Things healthcare environments. Detects abnormal behavior more accurately under both normal and highly imbalanced conditions. Delivers more stable and reliable protection than commonly used intrusion detection approaches.

The rapid advancement of Generative Artificial Intelligence (GenAI) models, particularly ChatGPT, has sparked widespread discussion among educators and researchers regarding their potential implications for education. This study presents a comprehensive taxonomy of GenAI in academia and education, encompassing a wide range of applications, challenges, ethical considerations, and future prospects. Drawing on a scoping review of 453 articles, including the 50 most cited works throughout 2023, the taxonomy provides a state-of-the-art analysis of the current landscape of GenAI in education. The taxonomy offers a theoretical framework that aligns with the current discourse in GenAI and education, providing a critical evaluation of the existing literature and proposing innovative perspectives and solutions. The practical implications of the taxonomy for educators, researchers, and policymakers are highlighted, emphasizing the need for ethical considerations and informed policies to maximize the benefits of GenAI while minimizing its risks and negative impacts.

Emerging technologies (ETs), including artificial intelligence (AI), blockchain, non-fungible tokens (NFTs), the Internet of Things (IoT), augmented reality (AR), virtual reality (VR), mixed reality (MR), extended reality (XR), robotics, and 3D printing, have greatly impacted education. While these technologies are commonly used in informal learning, their application in formal education remains under explored. This systematic review addresses this gap by analyzing the effective use of these technologies in formal education. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and using bibliometric analysis, this research reviewed experimental studies from the Scopus database (2008–2022). Data visualization tools, including RStudio, VOSviewer, Python, and Microsoft Excel, facilitated robust analysis. The study identifies gaps in technology adoption arising from economic status, infrastructure, and digital literacy challenges. It highlights the benefits of mobile apps and Learning Management Systems (LMS) in enhancing information retrieval, communication, and learning support. Challenges include the need for pedagogical skills, ICT competencies, and information literacy. Additionally, the study explores the potential of adaptive learning technologies and personalized learning environments to transform education by tailoring experiences to individual needs. Effective technology integration in education provides valuable insights for educators, policymakers, and researchers, highlighting strategies to overcome existing challenges and improve educational outcomes.