Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the ...

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• A new approach to representing ligand and drug 3D structures is introduced and evaluated.
• A novel algorithm calculating cross TSR keys is developed for enabling comparisons of drug/ligand - target complexes.
• The results demonstrate ...

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's ...

Human motion tracking is of great significance for sports training and rehabilitation treatment. Wearable sensors are the preferred device for motion detection, but they often have characteristics such as high cost and poor wearing comfort. In order to ...

Accurately forecasting renewable energy capacity is crucial for informing national energy policies and promoting sustainable socio-economic growth. This study proposes a novel dynamic fractional-order discrete grey model (DFDGM (1,1)) for ...

Creating a well-defined Bayesian network (BN) is helpful for developing effective graph neural networks (GNNs) that exploit annotated labels in multilabel classification (MLC) tasks. Obtaining correct BNs can be challenging when the labels are ...

• Creating Bayesian networks is useful for developing effective graph neural networks
• Obtaining Bayesian networks is challenging when data are unbalanced and sparse
• A novel Bayesian scoring method is proposed to address data ...

Generic Boundary Detection (GBD) aims at locating the general boundaries that divide videos into semantically coherent and taxonomy-free units, and could serve as an important pre-processing step for long-form video understanding. Previous works often ...

With the rapid development of China's economy, wind resource development has important practical significance for alleviating environmental pollution problems in China. As China's clean energy province and western economic center, Sichuan's wind ...

Aiming at the problem of low accuracy of two-dimensional preference information aggregation, this paper takes two-dimensional interval grey numbers as an example to define its preference information mapping rules. This rule maps preference ...

• DNN-based MLC suffers two critical problems: data imbalance and label correlation

Multilabel classification (MLC) is a challenging task in real-world applications, such as project document classification which led us to conduct this research. In the past decade, deep neural networks (DNNs) have been explored in MLC ...

To fulfill the standard recommendation requirements of the enterprise's standard information system, this paper proposes a combination strategy standard recommendation algorithm, which is based on factors such as standard name, standard classification ...

• Machine-learning methods are developed to label river restoration project documents.

River restoration projects are widely implemented around the world. Such projects are usually complicated endeavors. Engineering, environmental, and social impacts must be considered under various constraints. Gaining lessons from ...

This paper aims to improve the accuracy of software defect prediction by using a prediction model based on grey incidence analysis and Naive Bayes algorithm. The model employs the Naïve Bayes as the basic classifier of the software defect prediction ...

In view of the present situation that most aggregation methods of fuzzy preference information are extended or mixed by classical aggregation operators, which leads to the aggregation accuracy is not high. The purpose of this paper is to develop a novel ...

The interaction with the public and the coordinated change with the environment are more important in public environmental art sculpture. To be more accessible to the public, public environmental art sculptures should be integrated with the environment ...

• Propose the spatial optimal aggregation method for decision maker preference.
• Construct the spatial optimal aggregation model based on the Steiner-Weber point.
• Develop a novel MADM method based on the proposed aggregation method.

The purpose of this paper is to provide a novel approach for the spatial aggregation of decision maker preference information. The optimal aggregation method of decision maker preference information based on spatial Steiner-Weber point can ...

• Propose a new integrated ranking approach for DEA cross-efficiency.
• Adopt ...

As a significant extension of data envelopment analysis studies, cross-efficiency has been commenly adopted to rank the performances of decision-making units (DMUs). Interval cross-efficiency techniques can solve the nonuniqueness ...

• A two-phase representation method is presented to improve classification accuracy.

Sparse representation methods have exhibited promising performance for pattern recognition. However, these methods largely rely on the data sparsity available in advance and are usually sensitive to noise in the training samples. To ...

With the rapid progress of AI in both academia and industry, Deep Learning has been widely introduced into various areas in drug discovery to accelerate its pace and cut R&D costs. Among all the problems in drug discovery, molecular property prediction ...

In complex group decision-making, decision makers and decision attributes are the core of the relevant activities. Targeting the problem of scheme ranking and behavioural characteristics that exist in group decision-making, from the ...

• Construct a two-step optimization model to solve for the group consensus ideal scheme and its measure value matrix.

Observing the recent progress in Deep Learning, the employment of AI is surging to accelerate drug discovery and cut R&D costs in the last few years. However, the success of deep learning is attributed to large-scale clean high-quality labeled data, ...