Network Learning Research Articles

Muhammad ibn Musa al-Khwarizmi: The Pioneer of Algorithms and His Enduring Legacy in Artificial Intelligence

Muhammad ibn Musa al-Khwarizmi, a pivotal figure of the Islamic Golden Age, established foundational principles for modern computation and artificial intelligence (AI). This article examines his transformative contributions to mathematics, particularly his seminal work, The Compendious Book on Calculation by Completion and Balancing, which introduced algebra as a systematic discipline. Al-Khwarizmi’s development of algorithms—structured problem-solving methods—shaped medieval and contemporary computational sciences. His introduction of the Hindu-Arabic numeral system, including the concept of zero, revolutionized arithmetic, enabling advancements in science, engineering, and technology. The article highlights the profound connection between Al-Khwarizmi’s methodologies and modern AI applications, such as machine learning and neural networks, which rely on systematic algorithms. It also explores the intellectual ecosystem of the Abbasid Caliphate’s House of Wisdom in Baghdad, emphasizing its role in fostering innovation and interdisciplinary collaboration. This study challenges Eurocentric narratives, shedding light on the contributions of non-Western civilizations to modern technological paradigms. Keywords: Al-Khwarizmi, Algorithms, Algebra, Artificial Intelligence, Islamic Golden Age.

Label credibility correction based on cell morphological differences for cervical cells classification

Cervical cancer is one of the deadliest cancers that pose a significant threat to women’s health. Early detection and treatment are commonly used methods to prevent cervical cancer. The use of pathological image analysis techniques for the automatic interpretation of cervical cells in pathological slides is a prominent area of research in the field of digital medicine. According to The Bethesda System, cervical cytology necessitates further classification of precancerous lesions based on positive interpretations. However, clinical definitions among different categories of lesion are complex and often characterized by fuzzy boundaries. In addition, pathologists can deduce different criteria for judgment based on The Bethesda System, leading to potential confusion during data labeling. Noisy labels due to this reason are a great challenge for supervised learning. To address the problem caused by noisy labels, we propose a method based on label credibility correction for cervical cell images classification network. Firstly, a contrastive learning network is used to extract discriminative features from cell images to obtain more similar intra-class sample features. Subsequently, these features are fed into an unsupervised method for clustering, resulting in unsupervised class labels. Then unsupervised labels are corresponded to the true labels to separate confusable and typical samples. Through a similarity comparison between the cluster samples and the statistical feature centers of each class, the label credibility analysis is carried out to group labels. Finally, a cervical cell images multi-class network is trained using synergistic grouping method. In order to enhance the stability of the classification model, momentum is incorporated into the synergistic grouping loss. Experimental validation is conducted on a dataset comprising approximately 60,000 cells from multiple hospitals, showcasing the effectiveness of our proposed approach. The method achieves 2-class task accuracy of 0.9241 and 5-class task accuracy of 0.8598. Our proposed method achieves better performance than existing classification networks on cervical cancer.

Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning

Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning

Dual-phase neural networks for feature extraction and ensemble learning for recognizing human health activities

Dual-phase neural networks for feature extraction and ensemble learning for recognizing human health activities

Progressive Feature Reconstruction Network for Zero-shot Learning

Progressive Feature Reconstruction Network for Zero-shot Learning

TDU-DLNet: A transformer-based deep unfolding network for dictionary learning

TDU-DLNet: A transformer-based deep unfolding network for dictionary learning

SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for incomplete multimodal learning in conversational emotion recognition

SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for incomplete multimodal learning in conversational emotion recognition

Granular DeGroot dynamics – A model for robust naive learning in social networks

Granular DeGroot dynamics – A model for robust naive learning in social networks

Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks

Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks

Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

The diagnosis of tumors in the female reproductive system is crucial for effective treatment and patient outcomes. The advent of artificial intelligence (AI) has introduced new possibilities for enhancing diagnostic accuracy and efficiency. A comprehensive search across PubMed, Scopus, and Web of Science for articles published from 2018 to 2023 on artificial intelligence (AI), machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) in diagnosing cancers of the female reproductive system yielded 15,900 articles. After a rigorous screening process excluding conference proceedings, book chapters, reports, non-English publications, and duplicates, 98 unique peer-reviewed journal articles remained. These were further assessed for relevance and quality, resulting in the final inclusion of 29 high-quality articles. The review includes a summary of various AI methodologies used, their diagnostic accuracy, and comparative performance against traditional diagnostic methods. The findings indicate a significant improvement in diagnostic precision and efficiency when AI is employed. AI holds substantial promise for enhancing the diagnosis of tumors in the female reproductive system. Future research should focus on larger-scale studies and the integration of AI into clinical workflows to fully realize its potential

Optimising source code vulnerability detection using deep learning and deep graph network

To enhance the effectiveness of vulnerability detection in software developed using C and C++ programming languages, our study introduces a novel correlation calculation method for analyzing and evaluating Code Property Graphs (CPG). The intelligent computation method proposed in this study comprises three key stages. In the first stage, we present a method for extracting features from the CPG source code. To accomplish this, we integrate three distinct data exploration methods: employing Graph Convolutional Neural (GCN) to extract node features from CPG, utilizing Convolutional Neural Network (CNN) to extract edge features from CPG, and finally employing the Doc2vec natural language processing algorithm to extract source code from CPG nodes. The second stage involves proposing a method for synthesizing CPG source code features. Building on the features acquired in the first stage, our paper introduces a synthesis and construction method to generate feature vectors for the source code. The final stage, stage three, executes the detection of source code vulnerabilities. The experimental results demonstrate that our proposed model in this study achieves higher efficiency compared to other studies, with an improvement ranging from 3% to 4%.

A Hierarchical Multi Robot Coverage Strategy for Large Maps With Reinforcement Learning and Dense Segmented Siamese Network

A Hierarchical Multi Robot Coverage Strategy for Large Maps With Reinforcement Learning and Dense Segmented Siamese Network

ISFORS-MIX: Multi-agent reinforcement learning with Importance-Sampling-Free Off-policy learning and Regularized-Softmax Mixing network

ISFORS-MIX: Multi-agent reinforcement learning with Importance-Sampling-Free Off-policy learning and Regularized-Softmax Mixing network

Manifold Learning and Deep Generative Networks for Heterogeneous Change Detection From Hyperspectral and Synthetic Aperture Radar Images

Manifold Learning and Deep Generative Networks for Heterogeneous Change Detection From Hyperspectral and Synthetic Aperture Radar Images

Integrating machine learning and neural networks for particle image reconstruction in high-energy physics experiments

Integrating machine learning and neural networks for particle image reconstruction in high-energy physics experiments

Bearing fault diagnosis based on capsule network and transfer learning

Bearing fault diagnosis based on capsule network and transfer learning

Compensatory Data-driven Networked Iterative Learning Control with Communication Constraints and DoS Attacks

Compensatory Data-driven Networked Iterative Learning Control with Communication Constraints and DoS Attacks

Fine-Grained Ship Recognition With Spatial-Aligned Feature Pyramid Network and Adaptive Prototypical Contrastive Learning

Fine-Grained Ship Recognition With Spatial-Aligned Feature Pyramid Network and Adaptive Prototypical Contrastive Learning

Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.

Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction. The framework effectively solves the problem that existing models may lose some hidden spatial dependencies in the data when dealing with the dynamic graph structure of the network due to human mobility. We performed multi-wave infectious disease prediction in multiple regions based on real epidemic data. The results show that the framework is capable of performing high-dimensional parameter estimation and accurately predicting epidemic transmission dynamics in multiple regions even with low data quality. In addition, we retrospectively extrapolate the temporal evolution patterns of contact rate under different interventions implemented in different regions, reflecting the dynamics of intervention intensity and the need for flexibility in adjusting interventions in different regions. To provide early warning of infectious disease transmission, we retrospectively predicted the arrival time of infectious diseases using data from the early stages of outbreaks.

Enhancing Medical Image Diagnosis Using Convolutional Neural Network and Transfer Learning

Medical image diagnosis is crucial for early disease detection and effective treatment planning. Traditional diagnostic methods, while effective, often struggle with the complexities and variability inherent in medical images. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs) and transfer learning, have shown promise in overcoming these challenges. This study investigates the application of CNNs combined with transfer learning to improve diagnostic accuracy and efficiency. We employed several state-of-the-art CNN architectures, including VGG16 and ResNet, and utilized pre-trained models to leverage existing knowledge and reduce the need for extensive training data. Our approach was validated using a comprehensive dataset of [specific type of medical images, e.g., chest X-rays, MRI scans], and performance was evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results demonstrated a significant improvement in diagnostic performance, with the CNN model augmented by transfer learning achieving an accuracy of [specific accuracy, e.g., 95.2%], compared to [previous methods' accuracy, e.g., 85.3%] for traditional methods. This research highlights the potential of integrating advanced deep learning techniques in medical imaging, offering a robust solution for enhancing diagnostic precision and efficiency. These findings contribute to the growing body of evidence supporting the use of CNNs and transfer learning as transformative tools in medical image analysis.