Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.

The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today’s most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it is imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it is crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works and assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research.

Securing Electronic Medical Records (EMRs) is one of the most critical applications of cryptography over the Internet due to the value and importance of data contained in such EMRs. Although blockchain-based healthcare systems can provide security, privacy, and immutability to EMRs, several outstanding security and latency issues are associated with existing schemes. For example, some researchers have used the blockchain as a storage tool which increases delay and adversely affects the blockchain performance since it stores a copy of each transaction. A distributed ledger also requires appropriate space and computational power with increased data size. In addition, existing healthcare-based approaches usually rely on centralized servers connected to clouds, which are vulnerable to denial of service (DoS), distributed DoS (DDoS), and collusion attacks. This paper proposes a novel hybrid-deep learning-based homomorphic encryption (HE) model for the Industrial Internet of Medical Things (IIoMT) to cope with such challenges using a consortium blockchain. Integrating HE with the proposed IIoMT system is a vital contribution of this work. The use of HE while outsourcing to the cloud the storage provides a unique facility to perform any statistical and machine learning operation on the encrypted EMR data, hence providing resistance to collusion and phishing attacks. Our proposed model uses a pre-trained hybrid deep learning model in the cloud and deploys the trained model into blockchain-based edge devices in order to classify and train local models using EMRs. This is further conditioned on the private data of each edge and IoT device connected with the consortium blockchain. All local models obtained are aggregated to the cloud to update a global model, which is finally disseminated to the edge nodes. Our proposed approach provides more privacy and security than conventional models and can deliver high efficiency and low end-to-end latency for users. Comparative simulation analysis with state-of-the-art approaches is carried out using benchmark performance metrics, which show that our proposed model provides enhanced security, efficiency, and transparency.

The expeditious propagation of Internet of Things (IoT) technologies implanted in different smart devices such as smartphones and smartwatches have a ubiquitous consequence on the modern population. These devices are employed to collect data and to aid in tracking and analyzing the users’ daily activities using various human activity monitoring and recognition (HAR) techniques. However, most current HAMR approaches rely on exploratory case-based shallow feature learning architectures, which endeavor to recognize activities correctly in real-world situations. To address this issue, we offer a unique strategy for HAMR that leverages the attention mechanism with multi-head convolutional neural networks (CNNs) and Long-Short-Term-Memory (LSTM). The accuracy of activity detection is improved in the presented method by integrating attention into multi-head CNNs followed by LSTM for better feature extraction and selection. Verification investigations are carried out using data from the University of California (UCI) repository, which is publicly available. The results show that our proposed framework is more accurate than current frameworks using both the 10-fold and leave-one-subject-out cross-validation. Finally, the proposed method can recognize human activity in real-time, regardless of the type of smart device.