International Journal of Engineering Intelligent Systems

Construction of Humanoid Robot and Generative Artificial Intelligence Information System Based on Deep Learning

2026-10-10T15:42:44-07:00

With the rapid development of DL technology, artificial intelligence (AI) and robotics are gradually merging, especially the combination of humanoid robots and generative AI, which is becoming a key technology to promote the intelligent transformation of future industries. However, in traditional research, humanoid robots and generative AI technologies are developed in isolation, lacking effective collaboration and natural interaction, and lacking innovation in cross-domain application. To address these problems, in this study, an information system is constructed based on DL humanoid robots and generative AI. The system consists of three modules: a perception, decision and action module, and generation and interaction modules.
The perception module collects and preprocesses data of multiple modalities and then fuses the multimodal data. The decision and action module achieves the decision-making function of the information system through the DQN model. The generation and interaction module introduces the Generative Pre-Trained Transformer (GPT) model to realize the generation and interaction functions of the information system. Experiments have shown that the image perception ability of the system constructed in this study is good. The average perception error of 100 areas in the experiment is 0.0361. The decision-making ability is strong, and the decision-making score for different scenarios exceeds 0.83. It can also maintain good consistency in the generated content while taking emotions into account during conversations. The robot equipped with the system proposed in this paper and the traditional robot were put into the service industry for comparative experiments. The results demonstrated that the robot equipped with the proposed system is superior to the traditional robot according to all scoring indicators. The information system of humanoid robots and generative AI based on DL constructed in this study breaks through the limitations of the isolated development of traditional humanoid robots and generative AI, through the strong integration of deep perception, decision-making and interaction. It significantly improves the intelligence level of robots, hasbroad application prospects, and provides new ideas and technical support for promoting industrial intelligent transformation and cross-domain application innovation.

Keywords: Deep learning, Humanoid robot, Generative Artificial Intelligence, Multimodal Perception, Deep Q network, Generative Pre-Trained transformer

Cite As

H. Sun, J. Shen, "Construction of Humanoid Robot and Generative Artificial Intelligence Information System Based on Deep Learning", Engineering Intelligent Systems, vol. 34 no. 3, pp. 331-340, 2026.

AgriSpot-Trans: Fine-Grained Micro-Lesion Segmentation in Complex Unstructured Agricultural Environments via Occlusion-Aware Hierarchical Transformer

2026-10-09T23:24:26-07:00

Accurate pixel-level segmentation of early-stage micro-lesions in unstructured agricultural environments is critical for precision agriculture, yet it remains fundamentally challenged by severe field occlusions, optical interferences, and fuzzy biological boundaries. To address these issues, we proposeAgriSpot-Trans, a novel end-to-end occlusion-aware hierarchicalTransformer. Unlike conventional hard-cropping pipelines that disrupt global canopy topology, our architecture introduces an Environment-AwareToken Routing (E-Decoupler) module. Driven by a Gumbel-Softmax mechanism, it softly suppresses background noise and specular reflections in the feature space. Furthermore, to overcome the morphological mismatch of standard
square-window attention, we design Morphological Radial Attention (MRA), which dynamically aligns its star-shaped receptive field with the radial diffusion gradients characteristic of biological tissue damage. Finally, a Level-Set Signed Distance Function (SDF) Decoder, optimized by a Boundary Aware Active Contour loss, is employed to achieve continuous, sub-pixel contour evolution. Extensive experiments on PlantVillage, RoCoLe, and our newly curated Complex-Field-Pest (CFP) benchmark demonstrate that AgriSpot-Trans achieves a state-of-the-art mIoU of 78.2% in complex field scenarios—a 9.5% absolute improvement over the strong SegFormer baseline (p < 0.01). Most notably, it boosts the recall for extreme micro-targets
(= 5 px) by an unprecedented 23.7%, delivering a highly robust and edge-efficient visual perception framework for real-time, variable-rate pesticide application.

Keywords: Precision agriculture, phytopathology, micro-lesion segmentation, vision transformer, level-set method, unstructured environments, active contour.

Cite As

X. Qu, H. Peng, X. Zhu, X. Shen, X. Chen, J. Luo, R. Zhou, "AgriSpot-Trans: Fine-Grained Micro-Lesion
Segmentation in Complex Unstructured Agricultural Environments via Occlusion-Aware Hierarchical
Transformer", Engineering Intelligent Systems, vol. 34 no. 3, pp. 289-303, 2026.

Automated Generation and Optimization Methods for Smart Contract Vulnerability Code Integrating Large Models and RAG Framework

2026-10-10T15:25:00-07:00

The application of big models to vulnerability generation for smart contracts is hampered by a lack of knowledge within the field, regularly producing difficulties such as logical anomalies, conceptual misunderstandings, and poor concealment within the generation results. In order to overcome the discrepancy between the poor quality of generated results and the important role played by big models within the field, the article proposes a retrieval-enhanced generation optimization strategy that combines a hybrid retrieval strategy and a strategy for a prompt and programmatic validation cycle constrained by pre-defined generation objectives. The results demonstrate that the strategy effectively enhances the authenticity level from 0.342 for the benchmark model CodeLlama (Code Large Language Model Meta AI)-13B to 0.867 and enhances the concealment score from 0.106 to
0.289, which contributes to improving the recognition capability level within the field by improving the quality and concealment level of generation, supplying a useful model for the proper use of big models within a relevant field. This research work presents a systematic optimization strategy for automatically optimizing the generation of data concerning smart contract security tests and adversarial examples. In addition to optimizing generation quality and concealment level, it functions as a relevant model for big models within a relevant field.

Keywords: Smart contract security; vulnerability code generation; large language model; search enhanced generation; automated testing

Cite As

Z. Chen, Z. Peng, Y. Dou, N. He, W. Li, "Automated Generation and Optimization Methods for Smart Contract Vulnerability Code Integrating Large Models and RAG Framework", Engineering Intelligent Systems, vol. 34 no. 3, pp. 305-318, 2026.

Construction of Computer Vision Security Model Based on Machine Learning and Artificial Intelligence Networks

2026-10-10T15:35:15-07:00

Computer vision is being widely used today, but due to the inadequacies of computer vision systems, security issues have become a serious challenge. In this current study, a computer vision security model based on machine learning and artificial intelligence (AI) networks was constructed to improve the security and robustness of existing computer vision models, and provide effective solutions for computer vision problems in practical application scenarios. Moreover, the technology application of machine learning and AI network in the construction of computer vision security model was analysed to build a computer vision security model with four parts: data acquisition, data pre-processing, network construction and model training. Three real-life cases were selected for experimental analysis to verify the advantages of the proposed model. The accuracy, precision and recall of
100 facial samples using a computer vision security model constructed using machine learning and AI network technology were compared with those using traditional computer vision security models. According to the results, the accuracy, precision and recall of the proposed computer vision security model improved by 18%, 16% and about 18.2%, respectively. These findings indicate that the computer vision security model based on machine learning and AI networks constructed in this paper offers significant advantages in terms of object detection and good performance. The proposed model is of great relevance for the current security issues facing computer vision systems, providing new ideas and methods for research and practice in computer vision security.

Keywords: machine learning, artificial intelligence network, face recognition, computer vision security model, model construction

Cite As

N. Si, L. Tan, L. Yin, Z. Liu, "Construction of Computer Vision Security Model Based on Machine Learning and Artificial Intelligence Networks", Engineering Intelligent Systems, vol. 34 no. 3, pp. 319-329, 2026.