The deep vision sensing has been a practical tool in early disease detection, and this work aims at an important branch of ocular disease recognition. Although a number of researchers had paid attention to it during past years, fine-grained ocular feature extraction always remains a challenge. To handle with this issue, this work benefits from comprehensive ability of the convolution-Transformer structure (Conformer), and proposes vision sensing-driven intelligent ocular disease detection using conformer-based dual fusion. On the one hand, the proposal combines technical advantages of convolution and visual Transformer to more accurately fuse local subtle features and global representation information in images. On the other hand, the proposal significantly improves accuracy and robustness of the model by optimizing depth and width. Simulation experiments on real-world ocular disease image datasets show that the proposed model exhibits higher performance in ocular disease detection compared to other methods. Numerical results show that it improves the detection accuracy by 1% to 3.7% compared to several mainstream baseline methods. This research result not only promotes the development of ocular disease detection, but also provides more reliable technical support for accurate diagnosis of ophthalmic diseases.

In recent years, data-driven remote medical management has received much attention, especially in application of survival time forecasting. By monitoring the physical characteristics indexes of patients, intelligent algorithms can be deployed to implement efficient healthcare management. However, such pure medical data-driven scenes generally lack multimedia information, which brings challenge to analysis tasks. To deal with this issue, this paper introduces the idea of ensemble deep learning to enhance feature representation ability, thus enhancing knowledge discovery in remote healthcare management. Therefore, a multiview deep learning-based efficient medical data management framework for survival time forecasting is proposed in this paper, which is named as "MDL-MDM" for short. Firstly, basic monitoring data for body indexes of patients is encoded, which serves as the data foundation for forecasting tasks. Then, three different neural network models, convolution neural network, graph attention network, and graph convolution network, are selected to build a hybrid computing framework. Their combination can bring a multiview feature learning framework to realize an efficient medical data management framework. In addition, experiments are conducted on a realistic medical dataset about cancer patients in the US. Results show that the proposal can predict survival time with 1% to 2% reduction in prediction error.

In context of 6G mobile computing, the combination of Industrial Internet of Things (IoT) and machine learning extends intelligent sensing ability to improve industrial operation efficiency. In conventional operation of sewage treatment process (STP), manipulators often made excessive aeration amount in treatment process, in order to reach environmental standard. However, such rough operation mode will bring redundant energy consumption. To deal with this issue, this work employs industrial 6G-IoT environment provide basic data conditions for intelligent sensing scheme. On this basis, an industrial 6G-IoT sensing and machine-learning-supported intelligent sensing framework is established for indicator control strategy in STP. In particular, the amount of dissolved oxygen (DO) is selected as the main control object. Then, given inlet conditions and expected outlet conditions, the support vector regression model is formulated to predict the appropriate DO amount values. The proposed approach is evaluated on data collected from a real-world industrial 6G-IoT-based STP. And it is compared with several typical machine-learning-based prediction methods. Numerical results show that the method proposed is 5% better than the typical methods with a deviation of less than 0.6 and can achieved prediction precision about 80%.

Effective data management schemes have always been the major demand in universal industrial Internet of Things (IoT) systems, especially in resource-constrained scenarios. In realistic wastewater treatment process (WTP), only limited monitoring data resource can be available due to some digital constraint. Aiming at this practical issue, this work explores utilization of deep neural network to deal with such practical issue in the objective situation. Therefore, a deep learning-based data management scheme for intelligent control of WTP under resource-constrained IoT systems, is proposed in this paper. Firstly, a specific data encoding and preprocessing approach is developed for the objective business scenario. Then, the detailed workflow of a deep neural network structure is applied to predict key intermediate parameters which can further guide control decision. Finally, a comprehensive series of experiments are conducted on a real-world dataset which covers a range of one year. Both efficiency and robustness of the proposal are tested by introducing several performance metrics. The results show that it can have proper prediction effect in such resource-constrained environment, which can facilitate following intelligent control operations.

This letter proposes an efficient infrared small target detection method based on the 1-D difference of guided filtering (DoGF). First, the 1-D DoGF is constructed by measuring the difference of image structure fidelity between primitive guided filtering (GF) and local variance weighted GF from the perspective of 1-D signal analysis, which can effectively filter out 1-D noise components and protrude pulse signals. Second, the 1-D row and column DoGF are applied to process the infrared image along horizontal and vertical directions, respectively, and then the row–column crossed DoGF (rcDoGF) and column–row crossed DoGF (crDoGF) are calculated and integrated, which can greatly highlight the pulse-like small target signal and eliminate the background clutter. Finally, the small targets can be extracted with a simple adaptive threshold. Experimental results show that the proposed algorithm has high detection accuracy for small infrared targets under heavy noise interference, as well as for targets with different sizes and shapes.

Intelligent algorithms-driven industrial decision systems have been a general demand for modeling complex sewage treatment processes (STP). Existing researches modeled complex STP with the use of various neural network models, yet neglecting the fact that latent and occasional relations exist inside complex STP. To deal with the challenge, this paper proposes graph embedding-based intelligent industrial decision for complex STP (GE-STP). The graph embedding (GE) scheme is employed to enhance feature extraction and neural computing structure is utilized to simulate uncertain biochemical transformation inside STP. The introduction of GE can not only improves the fineness of feature spaces, but also improves the representative ability of models towards complex industrial processes. On this basis, the GE-STP is evaluated on a real-world data set collected from a realistic sewage treatment plant equipped with a set of Internet of Things devices. And some typical neural network models that have been utilized for modeling complex STP, are selected as baseline methods. Three groups of experiments show that efficiency of the GE-STP exceeds baselines about 6%–12%, and that the GE-STP is not susceptible to parameter changing.

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications, such as group recommender systems. As the distances between people have been greatly shortened, there has been more general demand for the provision of personalized services aimed at groups instead of individuals. The existing methods for capturing group-level preference features from individuals have mostly been established via aggregation and face two challenges: 1) secure data management workflows are absent and 2) implicit preference feedback is ignored. To tackle these current difficulties, this article proposes secure AIoT for implicit group recommendations (SAIoT-GRs). For the hardware module, a secure Internet of Things structure is developed as the bottom support platform. For the software module, a collaborative Bayesian network model and noncooperative game are introduced as algorithms. This secure AIoT architecture is able to maximize the advantages of the two modules. In addition, a large number of experiments are carried out to evaluate the performance of SAIoT-GR in terms of efficiency and robustness.

In ear of smart cities, intelligent medical image recognition technique has become a promising way to solve remote patient diagnosis in IoMT. Although deep learning-based recognition approaches have received great development during the past decade, explainability always acts as a main obstacle to promote recognition approaches to higher levels. Because it is always hard to clearly grasp internal principles of deep learning models. In contrast, the conventional machine learning (CML)-based methods are well explainable, as they give relatively certain meanings to parameters. Motivated by the above view, this paper combines deep learning with the CML, and proposes a hybrid intelligence-driven medical image recognition framework in IoMT. On the one hand, the convolution neural network is utilized to extract deep and abstract features for initial images. On the other hand, the CML-based techniques are employed to reduce dimensions for extracted features and construct a strong classifier that output recognition results. A real dataset about pathologic myopia is selected to establish simulative scenario, in order to assess the proposed recognition framework. Results reveal that the proposal that improves recognition accuracy about two to three percent.

Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines.