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.

Dog owners are extremely driven to comprehend the activity and health of their dogs and to keep tabs on their well-being. Dogs’ health and well-being, whether as household pets or service animals, are critical issues that are addressed seriously for moral, psychological, and economical reasons. Evaluations of a dog’s welfare depend on quantitative assessments of the frequency and variability of certain behavioral features, which are sometimes challenging to make in a dog’s normal environment. While it is challenging to obtain dogs’ behavioral patterns, it is nearly impossible to directly identify one distinct behavior when they are roaming around at will. Applications for automatic pet monitoring include real-time surveillance and monitoring systems that accurately identify pets using the most recent methods for the classification of pet activities. The suggested method makes use of a long short-term memory (LSTM)-based method to detect and classify the activities of dogs based on sensor data (i.e., accelerometer and gyroscope). The goal of this study is to use wearable sensor data and examine the activities of dogs using recurrent neural network (RNN) technology. We considered 10 pet behaviors, which include walking, sitting, down, staying, feeding, sideways, leaping, running, shaking, and nose work. As dog activity has a wider diversity, experimental work is performed on the multi-layer LSTM framework to have a positive influence on performance. In this study, data were collected from 10 dogs of various ages, sexes, breeds, and sizes in a safe setting. Data preprocessing and data synchronization were performed after the collection of data. The LSTM model was trained using the preprocessed data and the model’s performance was evaluated by the test dataset. The model showed good accuracy and high performance for the detection of 10 activities of dogs. This model will be helpful for the real-time monitoring of dogs’ activity, thus improving the well-being of dogs.

The incorporation of artificial intelligence (AI) into the healthcare system has been revolutionized, promising key advancements in diagnosis, treatment, patient care, administrative tasks, and operational efficiency. Using an in-depth analysis of the extensive amount of research on artificial intelligence and how it could help the medical industry, this study identified eleven barriers and challenges. Interpretive structural modeling (ISM) was used as a methodological approach to determine the relationship between the difficulties extracted and their dependency and driving powers. It resulted in a five-tiered model, with the introduction of innovative and new-generation tools topping the chart as the most dependent challenge. Similarly, Insufficient Data, Data Acquisition, Data Misuse, and Missing Compassion were the key drivers. Therefore, during the implementation of artificial intelligence in medicine, these challenges should be considered. Although artificial intelligence (AI) possesses the groundbreaking power to enhance patient care and operational efficiency in the healthcare sector, there are several key problems that must be addressed for implementation to be fruitful. The order of these challenges was ascertained through interpretive structural modeling, underlining the significance of innovation and data-related issues. Health systems can optimize AI’s benefits and enhance diagnosis, patient care, and overall hospital management by aggressively eliminating its deterrents.

The incorporation of artificial intelligence (AI) into the healthcare system has been revolutionized, promising keyadvancements in diagnosis, treatment, patient care, administrative tasks, and operational efficiency.The implementation of artificial intelligence into the healthcare system has been very transformational, productive, and hopeful, with the potential to revolutionize diagnosis, treatment, patient care, administrative work, and operational efficiency. Using an in-depth analysis of the extensive amount of literature on artificial intelligence and how it could help the medical industry, this study identified the eleven barriers and challenges. An interpretative structural modelling (ISM) has been used as a methodological approach to find out the relationship between the extracted challenges and their dependency and driving powers. It resulted in a five-tiered model with introduction of innovative and new generation tools topping the chart as the most dependent challenge. Similarly Insufficient Data, Data Acquisition, Data Misuse and Missing Compassion being the key drivers. Thereby, during the implementation of artificial intelligence in medicine, there challenges should be considered.