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In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision‐making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher‐level disease detection models. [ABSTRACT FROM AUTHOR]

Summary: The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications. [ABSTRACT FROM AUTHOR]

Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit. [ABSTRACT FROM AUTHOR]

Internet‐based information exchange has resulted in the propagation of false and misleading information, which is highly detrimental to individuals and humankind. Due to the speed and volume of social media news production, supervised artificial intelligence algorithms require many annotated data, which is difficult, costly, and time‐consuming. To address this issue, we offer a novel federated semi‐supervised framework based on self‐ensembling that utilizes the linguistic and stylometric information of annotated news articles and searches for hidden patterns in unlabeled data to denoise labels. Self‐ensembling predicts the labels of unlabeled data by using the outcomes of network‐in‐training from earlier epochs. These cumulative predictions should be a stronger predictor for unknown labels than the output of the most recent training epoch; hence, they may be utilized as a substitute for the labels of unlabeled data. The approach is distinctive in collecting all of the outputs from the neural network's past training periods. It utilizes them as an unsupervised target against which to assess the current output prediction of unlabeled articles. We intend to create a dataset centred on denoising to forward the study. The dataset is mapped using (1) the shifting focus time from published news articles and (2) the semi‐supervised method based on coincidence contexts for a neural contrast embedding model for learning low‐dimensional continuous vectors that generate a focus time‐based query in sequential news articles for temporal comprehension. The model achieved 0.83% F‐measure with lexicon expansion semi‐supervised learning. [ABSTRACT FROM AUTHOR]

Every individual in our technologically evolved world needs proper data security. The procedure of exchanging medical information is increasingly concerned with data privacy. Many techniques have been offered for preserving data security. These techniques use approaches such as k‐anonymity, l‐diversity, and others. However, such solutions are vulnerable to attribute disclosure, homogeneity, and background knowledge risks due to their syntactic nature. In this work, we describe a safe and secure architecture and semantic approach for data sharing that is based on blockchain, local differential privacy (LDP), and federated learning (FL). The proposed framework generates an atmosphere devoid of trust in which data owners are no longer required to have trust in the controllers. The FL models enable the whole network to decentralize its data‐driven learning. Interplanetary file system (IPFS) is used to provide data security in a distributed environment because each file in IPFS has a digital fingerprint that is computed using a cryptographic hash function on the file's whole contents. Due to the rigorous privacy guarantee, data owners no longer need to be worried about the security of their data. The proposed model's assessment parameters include latency, throughput, privacy, and accuracy. The data privacy of the proposed model is protected via LDP and FL, and its latency and throughput communication transactions on permissioned blockchain are calculated and compared with those of the benchmark model. The findings indicate that the proposed model delivers 85% more accurate privacy than the benchmark model. [ABSTRACT FROM AUTHOR]

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi‐agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor‐based data. Multi‐agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state‐of‐the‐art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions. [ABSTRACT FROM AUTHOR]