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A Neural Network Framework for Classifying Educational Content Aligned with Learning Outcomes

Procedia computer science 2025年 257卷 529-536页

作者： Adel Alkhalil Department of Software Engineering College of Computer Science and Engineering University of Ha’il Ha’il 81481 Saudi Arabia

Rapid growth of digital educational content necessitates efficient and accurate methods for organizing and mapping resources to ensure well-alignment with targeted learning outcomes, academic standards, and competency frameworks. Traditional text classification approaches, including rule-based and classical machine learning techniques, often fail to address the semantic diversity and scalability demands of modern educational systems. This study investigates the application of neural networks for text classification to automate the mapping of educational content into predefined categories. Leveraging state-of-the-art architectures such as Long Short-Term Memory (LSTM) networks, and transformers like BERT, we present an architecture of a systematic Classification of educational materials. We discuss the implications of this work for adaptive learning environments, emphasizing the potential of neural networks to enhance the efficiency and scalability of content mapping. This study contributes to the growing body of research in artificial intelligence for education and sets the stage for further exploration into multilingual and domain-specific content classification methods.

关键词： Neural Networks Text Classification Educational Content Curriculum Mapping Transformer Models Learning Outcomes

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Sentiment Analysis Scheme for Image-Text Comments based on Multimodal Deep Learning and Spatiotemporal Attention 6

Sentiment Analysis Scheme for Image-Text Comments based on M...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Fanchang TangYaLing Yadav, Amit Sah, Abhishek Pratap Khan, Asif Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (纸本)9798331522667

A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implementation details, and cutting-edge theoretical algorithms in graphic and textual sentiment analysis schemes. The proposed model has clear layering including data preprocessing layer, modal encoding layer, modal fusion layer, sentiment classification layer, loss function and optimizer, evaluation and feedback. The implementation details of each layer are introduced. The entire scheme model incorporates Multimodal Fusion Neural Network (MFNN) deep learning and spatiotemporal attention mechanism, which makes the scheme perform well in terms of security, robustness and performance, making up for the shortcomings of existing research schemes. © 2025 IEEE.

关键词： Contrastive Learning

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Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 IEEE.

关键词： coordinate network cross attention flow pyramid learned video compression

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Predicting Plain Text Imageability for Faithful Prompt-Conditional Image Generation 21st

Predicting Plain Text Imageability for Faithful Prompt-Cond...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Chen, Yufeng Yue, Guanghui Liu, Weide Lv, Chenlei Wang, Ruomei Zhou, Fan Zhao, Baoquan School of Artificial Intelligence Sun Yat-sen University Zhuhai China School of Biomedical Engineering Shenzhen University Shenzhen China Harvard Medical School Harvard University Boston United States College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789819601219

Text imageability is often used to quantize the ease with which a natural language description can invoke a mental image in a reader. With the proliferation of artificial intelligence powered text-to-image generation models, it will likely play an even more significant role in bridging the gap between language and visual representation. Unfortunately, automatically suggesting proper imageable textural prompts from a piece of plain text has scarcely been systematically investigated. In this paper, we narrow the gap by introducing a novel framework for text imageability assessment to automatically predict whether a piece of plain text and a prompt is highly imageable to be fed into a text-to-image model for faithful image generation. We have also developed a new visual-text dataset, named Ted1.6k, to facilitate model training and validation. Experiment results demonstrate the effectiveness of the proposed method in promoting prompt-guided image generation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image representation

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Improving Adaptive Runoff Forecasts in Data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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Few-shot Bearing Fault Diagnosis using Adaptive Detail Convolution and Global KAN-Transformer with Mahalanobis Distance

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Journal of Vibration engineering and Technologies 2025年第5期13卷 1-16页

作者： Zhao, Xinyu Zhao, Zhanshan Cui, Xiubin Yin, Jiao Cao, Jinli Wang, Hua School of Computer Science and Technology Tiangong University Tianjin 300387 China School of Software Engineering Tiangong University Tianjin 300387 China School of Computer Science China University of Geosciences Wuhan 430078 China Institute for Sustainable Industries Liveable Cities Victoria University Melbourne 3011 VIC Australia Department of Computer Science and Information Technology La Trobe University Melbourne 3086 VIC Australia

Background: In bearing failure diagnosis, accurate fault detection is essential for ensuring the safe operation of machinery. However, acquiring enough labeled fault data for training is expensive and challenging. Objective: This paper aims to address key challenges in few-shot fault diagnosis by proposing a novel framework incorporating an Adaptive Detail Convolution and a dual-branch architecture. Methods: The proposed framework consists of three main components: an Adaptive Detail Convolution module for enhanced feature extraction, a Global KAN-Transformer learning branch to model long-range dependencies between global features, and an adaptive-regularized Mahalanobis distance module to measure the similarity of local features between support and query samples. Results: Experimental results on the CWRU dataset show that the proposed framework significantly improves classification performance in terms of accuracy, robustness, and efficiency. Conclusion: The proposed solution offers an effective approach for few-shot bearing fault diagnosis and outperforms existing methods in both accuracy and computational efficiency. © Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive Detail Convolution Bearing Fault Diagnosis Few-shot Learning Global KAN-Transformer Mahalanobis Distance

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Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1031-1044页

作者： Sun, Haoyang Jiang, Cong Ye, Xun Feng, Dan Tan, Benjamin Ma, Yuzhe Liu, Kang Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada With Microelectronics Thrust Guangzhou510530 China

As the technology node develops toward its physical limit, lithographic hotspot detection has become increasingly important and ever-challenging in the computer-aided design (CAD) flow. In recent years, convolutional neural networks (CNNs) have achieved great success in hotspot detection. However, the interpretability of their hotspot prediction has yet to be considered. Compared with conventional lithography simulation and pattern matching-based methods, the black-box nature of CNNs wavers their practical applications with confidence. In this article, we propose the first interpretable CNN-based hotspot detector capable of providing high-detection accuracy and reliable explanations for hotspot identification. Specifically, we augment the training dataset with expanded error markers obtained and preprocessed from lithography simulation, which are then learned by an encoder-decoder architecture as intermediate features. We additionally introduce coordinate attention in the encoder to facilitate better-feature extraction. By learning these error markers and part of their surrounding metals as root cause hotspot features, our architecture achieves the highest-hotspot accuracy of 99.78% and the lowest-false positive rate of 5.29% compared to all prior work. Moreover, our method demonstrates the best visual and quantitative interpretability results when applying CNN interpretation methods. © 2024 IEEE.

关键词： Pattern matching

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Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Shiyu Chen, Delong Liu, Fan Zhang, Chuanyi Gu, Yanhui Guo, Shengjie Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9798350368741

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks. © 2025 IEEE.

关键词： Remote sensing Semantic contour extraction Vision-language learning

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A novel surrogate model with deep learning for predicting spacial-temporal pressure in coalbed methane reservoirs

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Natural Gas Industry B 2025年第2期12卷 219-233页

作者： Dong, Yukun Zhang, Xiaodong Zhang, Jiyuan Wu, Kuankuan Liu, Shuaiwei Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China College of Petroleum Engineering China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China

Coalbed methane (CBM) is a vital unconventional energy resource, and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies. This paper proposes a novel deep learning–based data-driven surrogate model, AxialViT-ConvLSTM, which integrates AxialAttention Vision Transformer, ConvLSTM, and an enhanced loss function to predict pressure dynamics in CBM reservoirs. The results showed that the model achieves a mean square error of 0.003, a learned perceptual image patch similarity of 0.037, a structural similarity of 0.979, and an R² of 0.982 between predictions and actual pressures, indicating excellent performance. The model also demonstrates strong robustness and accuracy in capturing spatial–temporal pressure features. © 2025 Sichuan Petroleum Administration

关键词： AxialAttention Vision Transformer Coalbed methane ConvLSTM Deep learning Spatial-temporal pressure prediction Surrogate models

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