Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study’s findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models’ generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

Laser powder bed fusion (LPBF) provides a rapid and cost-effective solution for fabricating metallic parts with near full density and high precision, strength, and stiffness directly from metallic powders. In LPBF, process variables are widely recognised as fundamental factors that have important effect on the quality of the built parts. However, activity of designing process variables for LPBF, i.e., process planning for LPBF, still heavily depends on knowledge from domain experts. This necessitates a knowledge base that enables the capture, representation, inference, and reuse of existing knowledge. In this paper, a description logic (DL) based ontology for knowledge representation in process planning for LPBF is presented. Firstly, a set of top-level DL entities and specific DL entities and semantic web rule language (SWRL) rules for part orientation, support generation, model slicing, and path planning are created to construct the ontology. The application of the ontology is then illustrated via process planning on an LPBF part. Finally, the benefits of the ontology are demonstrated through a few examples. The demonstration results show that the ontology has rigorous computer-interpretable semantics, which provides a semantic enrichment model for LPBF process planning knowledge and enables automatic consistency checking of the ontology, knowledge reasoning on the ontology, and semantic query from the ontology. This would lay solid foundation for development of a process planning tool with autonomous decision-making capability.

By means of the weight functions, the idea of introduced parameters, using the transfer formula and Hermite–Hadamard’s inequality, a more accurate half-discrete multidimensional Hilbert-type inequality with the homogeneous kernel as ${\frac{1}{{(x+||k-\xi |{|}_{\alpha })}^{\lambda }}}^{}(x,\lambda >0)$ involving one multiple upper limit function is given, which is a new application of Hilbert-type inequalities. The equivalent conditions of the best possible constant factor related to several parameters are considered. The equivalent forms the operator expressions and some particular inequalities are obtained.

By means of the weight functions, the idea of introduced parameters, using the transfer formula and Hermite–Hadamard’s inequality, a more accurate half-discrete multidimensional Hilbert-type inequality with the homogeneous kernel as $\frac{1}{(x + \Vert k - \xi \Vert _{\alpha} )^{\lambda + m}}\ (x,\lambda > 0)$1 ( x + ∥ k − ξ ∥ α ) λ + m ( x , λ > 0 ) involving one derivative function of m-order is given. The equivalent conditions of the best possible constant factor related to several parameters are considered. The equivalent forms. the operator expressions and some particular inequalities are obtained.

Because of the anti-tampering, decentralization, anonymous transaction, and other characteristics of the blockchain, it is considered to be a technology that can bring huge changes to the Internet field, especially the financial Internet. As a huge distributed ledger, the blockchain has obvious deficiencies in query processing module design and client query performance. In addition, the current blockchain storage engine cannot provide version control and multi-person collaboration functions. This article proposes to solve the problem of query performance and query semantics not rich by linking other databases, the version control function is realized by separately designing the version control semantics, and the function of deleting the duplicate data by the Pos-tree can be effectively used to solve the problem of waste of storage space during version control. Experiments show that under normal network delay $(\lt 80$ ms), the middleware system proposed in this paper has a slight improvement in query performance, and can also save storage space as much as possible during version control.