Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a graph, and the framework presents a graph classification model that learns and propagates relationships between nodes, improving global contextual understanding. By combining the strengths of CNN-based feature extraction and graph-based relational learning, the method achieves higher accuracy, faster training speeds, and greater robustness in image classification tasks. Experimental evaluations on four agricultural datasets demonstrate the proposed model’s superior performance, achieving accuracy rates of 96.57%, 99.63%, 95.19%, and 90.00% on Tomato Leaf Disease, Dragon Fruit, Tomato Ripeness, and Dragon Fruit and Leaf datasets, respectively. The model consistently outperforms conventional CNN (89.27%–94.23% accuracy), VIT (89.45%–99.77% accuracy), VGG16 (93.97%–99.52% accuracy), and ResNet50 (86.67%–99.26% accuracy) methods across all datasets, with particularly significant improvements on challenging datasets such as Tomato Ripeness (95.19% vs. 86.67%–94.44%) and Dragon Fruit and Leaf (90.00% vs. 82.22%–83.97%). The compact superpixel representation and efficient feature propagation mechanism further accelerate learning compared to traditional CNN and graph-based approaches.

Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development of management platforms and systems based on the Internet and cutting-edge information communication technologies. Mining the time series data including time series prediction has many practical applications. Many new techniques were developed for use with various types of time series data in the prediction problem. Among those, this work suggests a unique strategy to enhance predicting quality on time-series datasets that the time-cycle matters by fusing deep learning methods with fuzzy theory. In order to increase forecasting accuracy on such type of time-series data, this study proposes integrating deep learning approaches with fuzzy logic. Particularly, it combines the long short-term memory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model (CFLSTM). The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory (LSTM) technique to have greater power for processing time series data. Experiments on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposed model’s utility compared to other well-known forecasting models. The results of the comparisons supported the applicability of our proposed strategy for forecasting time series data.