This research proposes a novel framework for sugarcane leaf disease classification by integrating Swin-GAN U-Net, DenseNet-201 with Triple Attention, Salmon Salar Optimization (SSO), and an agricultural knowledge-based large language model (LLM). The proposed framework achieves an accuracy of 93.89
In this paper, a hybrid optimization approach to allocating resources in IoT networks with the help of SDN and NFV is developed. Resource allocation for IoT networks with SDN and NFV technologies is optimized simultaneously in terms of VNF placement, load balancing, resource consolidation, and network slicing. The cooperation of LP and GA is structured in a two-level way. At first, LP calculates a feasible allocation of VNFs that meets the constraints on server capacity and link bandwidth and provides a minimum of communication overhead, excess server capacity, and service response cost. The obtained allocation is further submitted to GA to find another feasible allocation to improve energy efficiency, resource utilization, and performance. Thus, LP provides a deterministic initial solution that meets all the constraints, while GA performs a global optimization inside the combinatorial space. It was found that the proposed algorithm decreased server utilization by 15
Increasing use of artificial intelligence (AI) in English language learning has created the need for reliable predictive models that can evaluate learner outcomes in adaptive educational settings. This study introduces a hybrid Bidirectional Encoder Representation from Transformer-Gated Recurrent Unit (BERT- GRU) framework that combines BERT-based contextual semantic embedding with GRU-based temporal sequence modeling to predict personalized learning effectiveness. The model is trained on the Adaptive English Learning Dataset, which contains 4,931 classroom interaction sessions and 26 multimodal features, including learner behavior, instructor attributes, and environmental conditions. The dataset captures various learning-related signals like engagement patterns, performance indicators, and instructional context, allowing for thorough modeling of learner progress. The proposed architecture merges deep contextual language representations with sequential learning dynamics to improve prediction reliability and strength. BERT extracts rich contextual embeddings from learner interactions, while GRU captures time dependencies across sequential learning sessions. The main technical contributions of this study include a hybrid embedding, sequence fusion strategy, a structured feature engineering pipeline, and an optimized BERT, GRU architecture for educational outcome prediction. These elements work together to effectively combine semantic and temporal information, enhancing predictive performance in adaptive learning environments. Experimental results show strong performance with an accuracy of 95.17
The demand for personalized, visually rich content in digital advertising has led to the increased adoption of AI technologies such as Generative Adversarial Networks and Natural Language Processing. Yet, finding an accurate match of textual descriptions with high-quality, contextually relevant images is still a challenge, particularly where emotions and creativity are part of the process in advertising. This paper proposes a new framework that integrates DCGANs with natural language processing techniques-sentiment analysis by Text CNN-and generates high-quality, contextually appropriate images from text descriptions for digital advertising. It utilizes the “Stable Diffusion—Image to Prompts” dataset from Kaggle, together with real and AI-generated images. Text CNN conducts sentiment analysis to capture the emotional tones in the input text. The evaluation metrics used for the model include but are not limited to Inception Score (IS), Frechet Inception Distance, and Latent Space Interpolation. The Inception Score demonstrates better performance with the proposed framework, coming to 4.0 against the score of 2.5 in AttnGAN; it has a lower FID of 80 versus 125.98 in AttnGAN, and also presents high-quality and smooth transitions in latent space interpolation. These improvements manifest better emotional alignment in generated images and creativity, outperforming the performance of traditional models. This research points out the scope of integrating GANs and NLP for automating the creation of personalized, high-quality digital ads and thereby offering a scalable solution for modern marketing needs.
Traditional university Physical Education (PE) faces problems such as limited visibility of movement demonstrations and delayed feedback. This research examines the employment of multimedia network teaching platforms in university PE. According to the teaching needs, it gives an opportunity to access through an Internet/Intranet hybrid architecture and selects a High Efficiency Video Coding (H.265/HEVC) standard for coding. A layered platform structure is established, which includes the user access layer, network transmission layer, and core service layer. A multimedia teaching information transmission coding module is developed, which includes functions such as sports multimedia resource information and teaching information transmission coding. This module utilizes a multimedia teaching resource encoding rate control method based on Deep Neural Networks (DNN) to adaptively adjust the resource encoding rate to ensure the transmission quality of teaching resources. The sports multimedia teaching module uses a multimedia vision-based motion recognition method to extract human three-dimensional coordinate motion features and input them into a hybrid kernel Support Vector Machine (SVM) model for motion discrimination, thereby realizing intelligent teaching management of university sports multimedia networks. Experimental results indicate that, under the 5G network environment, the average Quality of Experience (QoE) of the platform’s multimedia teaching videos remains stable at above 0.90, with minimal fluctuation throughout the entire period. The bitrate control method based on DNN can keep the bitrate basically stable at above 3000 Kb · s−1 when the number of online users reaches 70, thus avoiding blurring and freezing of multimedia teaching images. The motion recognition technology can achieve quantitative error correction of student movements.
