The Industrial Internet of Things has grown significantly in recent years. While implementing industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the complex and varied industrial Internet of Things environment provided a new attack surface for network attackers. As a result, conventional intrusion detection technology cannot satisfy the network threat discovery requirements in today’s Industrial Internet of Things environment. In this research, the authors have used reinforcement learning rather than supervised and unsupervised learning, because it could very well improve the decision‐making ability of the learning process by integrating abstract thinking of complete understanding, using deep knowledge to perform simple and nonlinear transformations of large‐scale original input data into higher‐level abstract expressions, and using learning algorithm or learning based on feedback signals, in the lack of guiding knowledge, which is based on the trial‐and‐error learning model, from the interaction with the environment to find the best good solution. In this respect, this article presents a near‐end strategy optimization method for the Industrial Internet of Things intrusion detection system based on a deep reinforcement learning algorithm. This method combines deep learning’s observation capability with reinforcement learning’s decision‐making capability to enable efficient detection of different kinds of cyberassaults on the Industrial Internet of Things. In this manuscript, the DRL‐IDS intrusion detection system is built on a feature selection method based on LightGBM, which efficiently selects the most attractive feature set from industrial Internet of Things data; when paired with deep learning algorithms, it effectively detects intrusions. To begin, the application is based on GBM’s feature selection algorithm, which extracts the most compelling feature set from Industrial Internet of Things data; then, in conjunction with the deep learning algorithm, the hidden layer of the multilayer perception network is used as the shared network structure for the value network and strategic network in the PPO2 algorithm; and finally, the intrusion detection model is constructed using the PPO2 algorithm and ReLU (R). Numerous tests conducted on a publicly available data set of the Industrial Internet of Things demonstrate that the suggested intrusion detection system detects 99 percent of different kinds of network assaults on the Industrial Internet of Things. Additionally, the accuracy rate is 0.9%. The accuracy, precision, recall rate, F1 score, and other performance indicators are superior to those of the existing intrusion detection system, which is based on deep learning models such as LSTM, CNN, and RNN, as well as deep reinforcement learning models such as DDQN and DQN.

The rising global population and shrinking arable land present critical challenges for sustaining food security, especially under the pressures of climate change. Soilless farming has emerged as a transformative agricultural method, leveraging controlled and protected environments to address these challenges. Soilless cultivation methods, such as hydroponics, aeroponics, and aquaponics, leverage smart technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) to optimize farming practices. These innovations enable higher productivity, greater resource efficiency, and consistent year-round yields compared to traditional soil-based agriculture. This article provides a comprehensive bibliometric analysis of 7,764 Scopus-indexed research articles on soil and soilless cultivation published between 2013 and 2023. Key findings reveal that Acta Horticulturae is the most prolific source, while Ecotoxicology and Environmental Safety leads in citations (5,575), and Public Library of Science One (PLoS ONE) holds the highest h-index (404). This paper examines current trends, identifies research gaps, and maps collaboration networks, providing valuable insights for researchers, policymakers, and industry leaders. By outlining the advantages of vertical farming and Controlled Environment Agriculture (CEA)-enabled soilless techniques, this work supports informed decision-making to address food security challenges and drive sustainable agricultural innovations.

Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic’s beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient’s activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient’s breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient’s respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.