Improved convolution neural network integrating attention based deep sparse auto encoder for network intrusion detection: Improved convolution neural network integrating attention based deep sparse auto encoder for network intrusion detection
Network intrusion detection (NID) is seen as a pivotal technology in the network security which can detect malicious threats occurring in the network and lend stabilized services for expanding the network environments. However, Network-based ...
An fMRI-based auditory decoding framework combined with convolutional neural network for predicting the semantics of real-life sounds from brain activity
Semantic decoding, understood as predicting the semantic information carried by stimuli presented to subjects based on neural signals, is an active area of research. Previous studies have mainly focused on the visual perception process, with ...
A distributionally robust risk-aware approach to chance constrained sustainable development model under unknown distribution: A distributionally robust risk-aware approach to chance constrained...
This paper presents a novel distributionally robust risk-aware approach that aims to tackle the increasingly complex sustainable development model by taking into account job creation and economic growth. To alleviate the inherent conservatism in ...
Monitoring african geopolitics: a multilingual sentiment and public attention framework: Monitoring african geopolitics: a...
In this paper, we present a framework for assessing geopolitical news based on local sentiment and public attention. Our approach uses data from social media and local online press in Kenya, Nigeria, Senegal, and South Africa, considering both ...
Machine learning-based assessment of diabetes risk: Machine learning-based assessment of diabetes risk
Currently, diabetes is one of the most dangerous diseases in modern society. Prevention is an extremely important aspect in the field of medicine, and the field of artificial intelligence and the healthcare industry are penetrating and integrating ...
Enhancing explainability in medical image classification and analyzing osteonecrosis X-ray images using shadow learner system: Enhancing explainability in medical image classification and analyzing osteonecrosis X-ray images using shadow learner system
Numerous applications have explored medical image classification using deep learning models. With the emergence of Explainable AI (XAI), researchers have begun to recognize its potential in validating the authenticity and correctness of results ...
RKHS reconstruction based on manifold learning for high-dimensional data: RKHS reconstruction based on manifold learning...
Kernel trick has achieved remarkable success in various machine learning tasks, especially those with high-dimensional non-linear data. In addition, these data usually tend to have compact representation that cluster in a low-dimensional subspace. ...
Detection and pose measurement of underground drill pipes based on GA-PointNet++ : Detection and pose measurement of underground drill pipes based on GA-PointNet++
Drilling for gas extraction, a common method in coal mine gas control, involves tedious loading and uploading of drill pipes. This study aims to design a method for detecting and measuring pose drill pipes using point cloud data. We present an ...
Manifold and patch-based unsupervised deep metric learning for fine-grained image retrieval: Manifold and patch-based unsupervised deep metric learning for fine-grained image retrieval
Accurately and swiftly retrieving from fine-grained images is a critical and challenging task. As the key technology for fine-grained image retrieval, deep metric learning aims to learn a mapping space, where samples exhibit two properties: ...
Reversible data hiding in encrypted images based on Lasso regression predictor and dynamic secret sharing: Reversible data hiding in encrypted images based...
Reversible data hiding in encrypted images (RDH-EI) integrates encryption with information hiding, enabling the embedding of additional data while ensuring full recovery of the original image, widely used in multimedia data protection and ...
Neural network-based adaptive reinforcement learning for optimized backstepping tracking control of nonlinear systems with input delay: Neural network-based adaptive reinforcement learning for optimized...
In this paper, the problem of adaptive optimized tracking control design is addressed for a class of nonlinear systems in strict-feedback form. The system under consideration contains input delay and has unmeasurable and restricted states within ...
Multi-agent dual actor-critic framework for reinforcement learning navigation: Multi-agent dual actor-critic framework for reinforcement learning navigation
Multi-Agent navigation task remains a fundamental challenge in robotics and autopilots. Reinforcement learning approaches to navigation often struggle to address the value overestimation in dynamic environments, multi-agent interactions, and ...
MultiGranDTI: an explainable multi-granularity representation framework for drug-target interaction prediction: MultiGranDTI: an explainable multi-granularity representation framework...
Drug-target interaction (DTI) prediction is a tough task with critical applications in drug repurposing and design scenarios, as it significantly reduces resource consumption and accelerates the drug discovery process. With the proliferation of ...
Towards adaptive information propagation and aggregation in hypergraph model for node classification
In recent years, hypergraph models have gained widespread attention in the hypergraph node classification task due to their ability to capture high-order node relationships. Nevertheless, most previous models are unaware of the potential pairwise ...
Bias reduction via cooperative bargaining in synthetic graph dataset generation: Bias reduction via cooperative bargaining...
In general, to draw robust conclusions from a dataset, all the analyzed population must be represented on said dataset. Having a dataset that does not fulfill this condition normally leads to selection bias. This problem can affect any dataset, ...
Multi-optimization scheme for in-situ training of memristor neural network based on contrastive learning: Multi-optimization scheme for in-situ training of memristor...
Memristor and its crossbar structure have been widely studied and proven to be naturally suitable for implementing vector-matrix multiplier (VMM) operation in neural networks, making it one of the ideal underlying hardware when deploying models on ...
Semi-supervised batch active learning based on mutual information: Semi-supervised batch active learning based on mutual information
Active learning reduces the annotation cost of machine learning by selecting and querying informative unlabeled samples. Semi-supervised active learning methods can considerably utilize the regional information of unlabeled samples, and thus, more ...
SFE-SLAM: an effective LiDAR SLAM based on step-by-step feature extraction: SFE-SLAM: an effective LiDAR SLAM based on step-by-step feature extraction
LiDAR Simultaneous Localization and Mapping (SLAM) plays a crucial role in intelligent robotics, finding extensive applications in autonomous driving and exploration. The traditional feature-based LiDAR SLAM holds a prominent position due to its ...
An original model for multi-target learning of logical rules for knowledge graph reasoning: An original model for multi-target learning...
Large-scale knowledge graphs are crucial for structuring human knowledge; however, they often remain incomplete. This paper tackles the challenge of completing missing factual triples in knowledge graphs using through rule reasoning. Current rule ...
Autoregressive multimodal transformer for zero-shot sales forecasting of fashion products with exogenous data: Autoregressive multimodal transformer...
Predicting future sales volumes of fashion industry products is challenging due to rapid market changes and limited historical sales data for recent products. As traditional forecasting methods and machine learning models often fail to address ...
DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable time series prediction: DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable...
Although time series prediction research among engineering and technology has made breakthrough progress in performance, challenges remain in modeling complex dynamic interactions between variables and interpretability. To address these two ...
Knowledge graph embeddings based on 2d convolution and self-attention mechanisms for link prediction: Knowledge graph embeddings based on 2d convolution...
Link prediction refers to using existing facts in the knowledge graph to predict missing facts. This process can enhance the integrity of the knowledge graph and facilitate various downstream applications. However, existing link prediction models ...
Communication-efficient federated learning based on compressed sensing and ternary quantization: Communication-efficient federated learning based on...
Most existing work on Federated Learning (FL) transmits full-precision weights, which contain a significant amount of redundant information, leading to a substantial communication burden. This issue is particularly pronounced with the growing ...