This research presents a novel energy-efficient task sequencing method for manufacturing operations involving multiple processing points, such as precision drilling and contact welding. The problem is formulated as a multi-dimensional weighted variant of the Traveling Salesman Problem (TSP), and solved using a Multi-gate Mixture of Experts (MMOE) neural architecture. Unlike previous approaches that require separate models for each TSP size, our method employs a single neural network to handle TSPs of all sizes, significantly improving scalability and reducing training overhead. With an uncertainty-based loss weighting strategy, the model effectively balances multiple learning objectives. Experiments show that MMOE-9 achieves performance comparable to state-of-the-art methods with only one-third of the parameters of NAR4TSP, and its training time is similar to that of a single TSP100 model. Further, we extend the model to cover 91 TSP sizes (from 10 to 100) within the same unified framework, demonstrating strong generalization across scales.

The widespread deployment of Internet of Things (IoT) devices in intelligent school environments provides abundant data for enhancing video recognition systems with advanced behavior recognition technologies, which is crucial for ensuring campus safety. Traditional models primarily rely on visual cues and often struggle to identify complex behaviors against dynamic backgrounds. This study addresses these challenges by introducing a novel Context-Embedded Contrastive Learning (CECL) approach, which leverages data collected from IoT devices to synergistically integrate textual and visual modalities through contrastive learning, thereby refining behavior recognition capabilities. In addition, the model incorporates a Context-Embedded Transformer Encoder that utilizes temporal and geographic context cues captured by IoT sensors, processed through the proposed Context-Aware Multi-Head Attention mechanism. This enhancement significantly improves the model’s ability to discern subtle behavioral nuances by harnessing the rich contextual data from IoT-based surveillance systems, which conventional methods frequently overlook. Through this integration, the model achieves a more sophisticated understanding and recognition of abnormal behaviors in campus settings, as evidenced by the CABRH8 dataset. The CECL model has demonstrated outstanding performance, achieving a Top-1 accuracy of 80.26% and a Top-5 accuracy of 99.38% on the challenging CABRH8 dataset, thereby outperforming existing models reliant solely on visual data. Its robustness and adaptability have also been validated across additional datasets, including CABR50, UCF-101, and HMDB-51, demonstrating its potential for widespread deployment in various IoT-enabled surveillance applications.

Abstract The adoption of automated crop harvesting system based on machine vision may improve productivity and optimize the operational cost. The scope of this study is to obtain visual information at the plantation which is crucial in developing an intelligent automated crop harvesting system. This paper aims to develop an automatic detection system with high accuracy performance, low computational cost and lightweight model. Considering the advantages of YOLOv3 tiny, an optimized YOLOv3 tiny network namely YOLO‐P is proposed to detect and localize three objects at palm oil plantation which include fresh fruit bunch, grabber and palm tree under various environment conditions. The proposed YOLO‐P model incorporated lightweight backbone based on densely connected neural network, multi‐scale detection architecture and optimized anchor box size. The experimental results demonstrated that the proposed YOLO‐P model achieved good mean average precision and F1 score of 98.68% and 0.97 respectively. Besides, the proposed model performed faster training process and generated lightweight model of 76 MB. The proposed model was also tested to identify fresh fruit bunch of various maturities with accuracy of 98.91%. The comprehensive experimental results show that the proposed YOLO‐P model can effectively perform robust and accurate detection at the palm oil plantation.

Tropical wood recognition is a very challenging task due to the lack of discriminative features among some species of the wood, and also some very discriminative features among inter class species. Moreover, noises due to illuminations, or the uncontrolled environment as well as the wood features such as the size of pores, the density of pores, etc., which depend very much on the age, weather and other factors, contributing to the irregularities of the features. In this paper, we explore the use of feature extraction techniques, classification techniques for better accuracy of the system. In particular, we explore the use of one of the deep learning method residual network based CNN (Res-Net), noting the capability of the network to learn the features of images and its ability of generalization. Results have shown that good feature extraction methods can give a much better accuracy for all the datasets tested, and Res-Net performed badly due to lack of data, which cause the problem of overfitting.