With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments effectively, we construct a model of Multi-Filters Convolutional Neural Network (MFCNN) for opinion spam detection. MFCNN is designed with a fixed-length sequence input and an improved activation function to avoid the gradient vanishing problem in spam opinion detection. Moreover, convolution filters with different widths are used in MFCNN to represent the sentences and documents. Our experimental results show that MFCNN outperforms current stateof-the-art methods on standard spam detection benchmarks.

Rice is a staple food for nearly half the global population and, with rising living standards, the demand for high-quality grain is increasing. Chalkiness, a key determinant of appearance quality, requires accurate detection for effective quality evaluation. While traditional 2D imaging has been used for chalkiness detection, its inherent inability to capture complete 3D morphology limits its suitability for precision agriculture and breeding. Although micro-CT has shown promise in 3D chalk phenotype analysis, high-throughput automated 3D detection for multiple grains remains a challenge, hindering practical applications. To address this, we propose a high-throughput 3D chalkiness detection method using micro-CT and VSE-UNet. Our method begins with non-destructive 3D imaging of grains using micro-CT. For the accurate segmentation of kernels and chalky regions, we propose VSE-UNet, an improved VGG-UNet with an SE attention mechanism for enhanced feature learning. Through comprehensive training optimization strategies, including the Dice focal loss function and dropout technique, the model achieves robust and accurate segmentation of both kernels and chalky regions in continuous CT slices. To enable high-throughput 3D analysis, we developed a unified 3D detection framework integrating isosurface extraction, point cloud conversion, DBSCAN clustering, and Poisson reconstruction. This framework overcomes the limitations of single-grain analysis, enabling simultaneous multi-grain detection. Finally, 3D morphological indicators of chalkiness are calculated using triangular mesh techniques. Experimental results demonstrate significant improvements in both 2D segmentation (7.31% improvement in chalkiness IoU, 2.54% in mIoU, 2.80% in mPA) and 3D phenotypic measurements, with VSE-UNet achieving more accurate volume and dimensional measurements compared with the baseline. These improvements provide a reliable foundation for studying chalkiness formation and enable high-throughput phenotyping.

In this paper, we propose a novel hybrid control approach for solving the multi-robot motion planning (MRMP) problem in the Internet of Things (IoT) environment, where the closed workspace is “abstracted” as a discrete event system (DES) that is modeled as a timed Petri net. In the DES, robot’s movement between adjacent confliction zones is viewed as a “transition”, by taking the robot kinematics into consideration. To ensure safety and efficiency, robots should not appear at the same conflict position at the same time and should not block each other. To this end, an online IoT-based approach is provided to control robots to travel in the closed workspace safely (without collisions) and efficiently (without deadlocks). Specifically, a high-level supervisory controller whose commands are in the form of transitions, is used to control robots to travel in the closed workspace. At the low-level, we explicitly capture the time-driven robot kinematics, and commands from the supervisory controller are translated into appropriate input signals to the actuators of the robots, which in turn influence states of the high-level DES. Simulation results demonstrate the expressiveness of the proposed model and the effectiveness and efficiency of the proposed algorithm.

Addressing the challenges posed by the small scale, diverse morphology, and severe occlusion of pest and disease targets on lotus leaves in complex aquatic environments—as well as the difficulty of existing methods in automatically quantifying disease severity—this paper proposes an approach for the identification of lotus leaf pests and diseases and quantitative grading of leaf spot disease severity. First, by integrating high-altitude canopy imagery captured by unmanned aerial vehicles (UAVs) with high-definition ground-level data, a multi-perspective dataset comprising object detection bounding box annotations and pixel-level segmentation annotations is constructed. Second, the YOLOv12n-DFFN object detection model is proposed; this model enhances interaction between deep and shallow features through a dynamic feature feedback mechanism, thereby improving the ability to localize disease targets against complex aquatic backgrounds. Finally, taking typical leaf spot disease as the subject, YOLOv12n-DFFN is used to detect and extract diseased leaf regions, while a VGG-UNet semantic segmentation model is employed to achieve pixel-level fine-grained segmentation of leaf and lesion areas. By calculating the ratio of lesion area to total leaf area, automatic quantitative grading of disease severity is realized. Experimental results show that YOLOv12n-DFFN achieved a precision of 93.58%, representing an improvement of 4.37 percentage points over the YOLOv12n baseline, with an mAP50 of 83.87%; the segmentation model attained an average intersection-over-union of 90.47%, and the overall accuracy of the two-stage framework for leaf spot disease severity grading reached 96.0%. Through a “detection first, segmentation second” two-stage strategy, this framework enables the intelligent identification and severity quantification of lotus leaf diseases in complex aquatic environments, providing an effective approach for the intelligent monitoring and precision management of aquatic crop diseases.