The integration of AI and the IoT in education has the potential to revolutionize the way we learn. Personalized learning, real-time feedback and support, and immersive learning experiences are some of the benefits that AI and the IoT can bring to the education system. In this regard, this research paper aims to investigate how AI and the IoT can be integrated into sustainable education in order to provide students with personalized and immersive learning experiences during pandemics, such as COVID-19, for smart cities. The study’s key findings report that AI can be employed in sustainable education through personalized learning. AI-powered algorithms can be used to analyze student data and create personalized learning experiences for each student. This includes providing students with tailored content, assessments, and feedback that align with their unique learning style and pace. Additionally, AI can be used to communicate with students in a more natural and human-like way, making the learning experience more engaging and interactive. Another key aspect of the integration of AI and the IoT in education obtained from this research is the ability to provide real-time feedback and support. IoT-enabled devices, such as smart cameras and microphones, can be used to monitor student engagement and provide real-time feedback. AI algorithms can then use these data to adapt the learning experience in real time. IoT-enabled devices, such as tablets and laptops, can be used to collect and process student work, allowing for the automatic grading of assignments and assessments. Additionally, IoT technology can facilitate remote monitoring and grading of student work, which would be particularly useful for students who cannot attend traditional classroom settings. Furthermore, AI and the IoT can also be used to create intelligent personal learning environments (PLEs) that provide students with personalized, adaptive, and engaging learning experiences. IoT-enabled devices, such as smart cameras and microphones, combined with AI-powered algorithms, can provide real-time feedback and support, allowing the PLE to adapt to the student’s needs and preferences. It is concluded that integrating AI and the IoT in sustainable education can revolutionize the way people learn, providing students with personalized, real-time feedback and support and opening up new opportunities for remote and disadvantaged students. However, it will be important to ensure that the use of AI and the IoT in education is ethical and responsible to ensure that all students have equal access to the benefits of these technologies.

The Internet of Things (IoT) technology has recently emerged as a potential global communication medium that efficiently facilitates human-to-human, human-to-machine, and machine-to-machine communications. Most importantly, unlike the traditional Internet, it supports machine-to-machine communication without human intervention. However, billions of devices connected to the IoT environment are mostly wireless, small, hand-held, and resourced-constrained devices with limited storage capacities. Such devices are highly prone to external attacks. These days, cybercriminals often attempt to launch attacks on these devices, which imposes the major challenge of efficiently implementing communications across the IoT environment. In this paper, the issue of cyber-attacks in the IoT environment is addressed. An end-to-end encryption scheme was proposed to protect IoT devices from cyber-attacks.

Background: Distinguishing vitiligo from postinflammatory hypopigmentation (PIH) is clinically challenging because both conditions may present with similar depigmented lesions. Although deep learning has shown strong potential for dermatologic image classification, limited interpretability remains a barrier to clinical adoption. Objective: This study aimed to develop an interpretable deep learning framework for accurate differentiation between vitiligo and PIH using a lightweight convolutional neural network and an ensemble of explainability methods. Methods: A total of 332 clinical images (176 vitiligo and 156 PIH) were collected from King Abdullah University Hospital and publicly available online sources. Images were preprocessed and evaluated using patient-wise 5-fold cross-validation to eliminate patient-level data leakage. A pretrained MobileNetV2 model was fine-tuned by unfreezing the final 30 layers. To enhance interpretability, gradient-weighted class activation mapping (Grad-CAM), integrated gradients, and smooth gradients (SmoothGrad) were combined into an equal-weight ensemble explanation framework. Performance was assessed using accuracy, precision, recall, F ₁ -score, and the area under the receiver operating characteristic curve (AUC). Results: The proposed model achieved an overall accuracy of 94.88%, macroaveraged precision of 94.88%, recall of 94.84%, F ₁ -score of 94.86%, and an AUC of 0.9885 across the 5 validation folds. The ensemble framework produced clinically meaningful explanations in 98.48% of a representative 66-image validation subset used for interpretability analysis. Conclusions: The proposed framework combines high diagnostic performance with robust interpretability for distinguishing vitiligo from PIH. By integrating multiple complementary explanation methods, the approach enhances clinical transparency and may support dermatologists in the differential diagnosis of pigmentary disorders.

This study aims to gather up-to-date information on 6G technology, its benefits for green communication, and its possible applications. Integrating traditional terrestrial networks with emerging space, aerial, and underwater networks implies a move toward a comprehensive and ubiquitous network infrastructure. This integration provides seamless connectivity across diverse environments while addressing the limitations of current networks to achieve universal coverage, ensuring that connectivity is available consistently across different geographical locations and technology platforms. Incorporating pervasive AI is highlighted as a crucial aspect that involves embedding AI algorithms and capabilities throughout the network infrastructure to enhance efficiency, responsiveness, and adaptability. AI can be a key in network management, resource optimization, and advanced features for diverse applications. The mention of an enhanced network protocol stack indicates that 6G will likely introduce new or improved communication protocols to handle the complexities of integrated networks and meet the requirements of future applications. This statement emphasizes that the capabilities and requirements of future applications are driving the development of 6G, which could include applications in augmented reality, virtual reality, the Internet of Things, and other emerging technologies. The commendable focus on sustainable and socially seamless networks reflects an awareness of the importance of minimizing environmental impact and ensuring that technology benefits society. Exploring technologies such as terahertz and visible light communication can achieve these goals through new communication paradigms. Integrating blockchain technology can enhance security, privacy, and trust in the network, which are essential for the success of future wireless systems. Symbiotic radio involves intelligent cooperation among different wireless systems, which optimizes resource allocation and improves overall network efficiency. This article draws upon a comprehensive and up-to-date account of architectural adjustments and potential technologies in the field of green 6G, along with a novel method for assessing efficacy that will foster innovation and propel wireless networking toward a more sustainable and efficient future.