AIMC Topic: Neural Networks, Computer

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AffiGrapher: Contrastive Heterogeneous Graph Learning with Aromatic Virtual Nodes for RNA-Small Molecule Binding Affinity Prediction.

Journal of chemical information and modeling
RNA molecules exhibit diverse structures and functions, making them promising drug targets. However, predicting RNA-small molecule binding affinity remains challenging due to limited experimental data and the structural variability introduced by mult...

A comparative study of fully automatic and semi-automatic methods for oil spill detection using Sentinel-1 data.

Environmental monitoring and assessment
The oil spill detection and assessment study conducted in the Banten Province of Indonesia involves the application of Sentinel-1 satellite data and machine learning tools in the year 2024. Synthetic Aperture Radar (SAR) data were used with VV polari...

A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection.

PloS one
Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal brain activity, which can severely affects people's normal lives. To improve the lives of these patients, it is necessary to develop accurate methods to predic...

A novel twin time series network for building energy consumption predicting.

PloS one
Energy consumption prediction in buildings is crucial for optimizing energy management. The latest research faces three critical challenges: (1) Insufficient temporal correlation extraction and prediction accuracy, hindering widespread adoption and a...

The application of artificial intelligence techniques in predicting game outcomes of professional basketball league: A systematic review.

PloS one
BACKGROUND: Predicting basketball game outcomes is a critical area in sports science and data analysis, providing concrete benefits for optimizing coaching strategies, improving team management, and informing betting decisions.

PRformer: Pyramidal recurrent transformer for multivariate time series forecasting.

Neural networks : the official journal of the International Neural Network Society
The self-attention mechanism in Transformer architecture, invariant to sequence order, necessitates positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positional embeddings restricts the Transform...

Detection of Polystyrene Microplastics up to the Single Nanoparticle Limit Using SERS and Advanced ANN Design (KANformer).

ACS sensors
Due to uncontrolled release, gradual accumulation, low degradation rate, and potential negative impact on human health, microplastics (MPs) pose a serious environmental and healthcare risk. Thus, the spread of MPs should be at least carefully monitor...

Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm.

Environmental monitoring and assessment
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood man...

Epilepsy Prediction via Time-Frequency Features and Multi-Scale Hybrid Neural Networks.

Journal of medical systems
The prediction of epileptic seizures heavily depends on the precise embedding and classification of complex, multi-dimensional electroencephalogram (EEG) signals. Due to individual variability and the dynamic non-linear nature of EEG signals, extract...

CATransU-Net: Cross-attention TransU-Net for field rice pest detection.

PloS one
Accurate detection of rice pests in field is a key problem in field pest control. U-Net can effectively extract local image features, and Transformer is good at dealing with long-distance dependencies. A Cross-Attention TransU-Net (CATransU-Net) mode...