The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback–Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.

Metaverse is the buzz technology of the moment raising attention both from academia and industry. Many stakeholders are considering an extension of their existing applications into the metaverse environment for more usability. The healthcare industry is gradually making use of the metaverse to improve quality of service and enhance living conditions. In this paper, we focus on the potential of digital anti-aging healthcare in the metaverse environment. We show how we can use metaverse environment to enhance healthcare service quality and increase the life expectancy of patients through more confident processes, such as chronic disease management, fitness, and mental health control, in the metaverse. The convergence of artificial intelligence (AI), blockchain (BC), Internet of Things (IoT), immersive technologies, and digital twin in the metaverse environment presents new scopes for the healthcare industry. By leveraging these technologies, healthcare providers can improve patient outcomes, reduce healthcare costs, and create new healthcare experiences for a better life, thus facilitating the anti-aging process. AI can be used to analyze large-scale medical data and make personalized treatment plans, while blockchain can create a secure and transparent healthcare data ecosystem. As for IoT devices, they collect real-time data from patients, which is necessary for treatment. Together, these technologies can transform the healthcare industry and improve the lives of patients worldwide. The suggestions highlighted in this paper are worthy to undergo implementation and create more benefits that will promote a digital anti-aging process for its users for a longer life experience.

The Metaverse and Natural Language Processing (NLP) technologies have combined to fundamentally change the nature of digital sociability. Our understanding of social interaction needs to be reevaluated as the Metaverse’s influence spreads into more areas of daily life, such as AI-driven gaming, interactive training companions, museum exhibits, personalized fitness coaching, virtual mental health assistance, language translation services, virtual tour guiding, and virtual conferencing. This study analyzes how NLP is changing social relationships in these Metaverse applications. We examine how NLP algorithms influence societal norms, individual behaviors, interpersonal connections, and improve the user experience using a multi-method approach incorporating user surveys and sentiment analysis. Our study’s findings show how NLP can enhance interactive experiences while also pointing out related issues like potential bias and moral problems. Our study provides a foundational analysis, shedding light on the challenges of negotiating a social environment in the Metaverse that is molded by cutting-edge NLP. It offers stakeholders in academia and public policy essential assistance that helps them understand and manage the complex ramifications of this changing socio-technological paradigm.

With a focus on producing a new AI-based filmmaking platform, this study explores the revolutionary impact of AI on the film industry. Because of how movies are envisioned, made, and seen, it is drastically shifting due to AI. An in-depth analysis of the emergence and applications of AI in film is provided in this article, with particular attention to how these technologies are incorporated into the screenplay writing, pre-production, cinematography, editing, and visual effects phases of the filmmaking process. It focuses on how blockchain technology is revolutionizing the management of intellectual property and film distribution, how Natural Language Processing (NLP) is automating the drafting of scripts, and how generative AI is producing novel visual styles and narrative structures. AI transforms filmmaking in technical and artistic aspects by increasing production, creating new avenues for creativity, and providing distinctive visual forms. Aside from these important ethical issues, the article also discusses employment displacement, copyright infringement, authorship, creative purpose, and the possibility that AI will reinforce prejudices or preconceptions. A focus on new rules for the moral and responsible use of AI that balance the advantages of automation with the need for human creativity and oversight is placed on the legal advancements governing AI in the film industry. The research also examines how audiences respond to and understand these novel storytelling techniques by contrasting the aesthetics of traditionally made movies with those produced by AI. AI films may need help to retain emotional depth and narrative coherence, even though they frequently use cutting-edge visual effects and interactive storytelling. The study includes case studies and practical scenarios, such as the AI-generated film Sunspring , to interpret the real-world application of AI in filmmaking. Besides, the article extends AI’s role in audio and sound design, highlighting improvements in automated dubbing, sound effects synthesis, and music composition. Our research sheds light on how AI will affect filmmaking in the future, which adds to the larger conversation on the impact of emerging technologies on the creative industries. As the film business develops, AI presents important issues that need to be carefully handled, in addition to the tremendous prospects it gives for innovation and democratization.

The perception of animal emotions is key to enhancing veterinary practice, human–animal interactions, and protecting domesticated species’ welfare. This study presents a unique emotion classification deep learning-based approach for pet animals. The actual and emotional status of dogs and cats have been classified using a modified EfficientNetB5 model. Utilizing a dataset of images classified into four different emotion categories—angry, sad, happy, and neutral—the model incorporates sophisticated feature extraction methods, such as Dense Residual Blocks and Squeeze-and-Excitation (SE) blocks, to improve the focus on important emotional indicators. The basis of the second strategy is EfficientNetB5, which is known for providing an optimal balance in terms of accuracy and processing capabilities. The model exhibited robust generalization abilities for the subtle identification of emotional states, achieving 98.2% accuracy in training and 91.24% during validation on a separate dataset. These encouraging outcomes support the model’s promise for real-time emotion detection applications and demonstrate its adaptability for wider application in ongoing pet monitoring systems. The dataset will be enlarged, model performance will be enhanced for more species, and real-time capabilities will be developed for real-world implementation.

Background: Nuclei segmentation is the first stage of automated microscopic image analysis. The cell nucleus is a crucial aspect in segmenting to gain more insight into cell characteristics and functions that enable computer-aided pathology for early disease detection, such as prostate cancer, breast cancer, brain tumors, and other diagnoses. Nucleus segmentation remains a challenging task despite significant advancements in automated methods. Traditional techniques, such as Otsu thresholding and watershed approaches, are ineffective in challenging scenarios. However, deep learning-based methods exhibit remarkable results across various biological imaging modalities, including computational pathology. Methods: This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. We employed several methods, including K-means clustering, Random Forest (RF), Support Vector Machine (SVM) with handcrafted features, and Logistic Regression (LR) using features derived from Convolutional Neural Networks (CNNs). Handcrafted features extract attributes like the shape, texture, and intensity of nuclei and are meticulously developed based on specialized knowledge. Conversely, CNN-based features are automatically acquired representations that identify complex patterns in nuclei images. To assess how effectively these techniques segment cell nuclei, their performance is evaluated. Results: Experimental results show that Logistic Regression based on CNN-derived features outperforms the other techniques, achieving an accuracy of 96.90%, a Dice coefficient of 74.24, and a Jaccard coefficient of 55.61. In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. Conclusions: The conclusions suggest that leveraging CNN-based features in conjunction with Logistic Regression significantly enhances the accuracy of cell nuclei segmentation in pathological images. This approach holds promise for refining computer-aided pathology workflows, potentially leading to more reliable and earlier disease diagnoses.