AIMC Topic: Algorithms

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An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

PloS one
This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is pr...

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

IEEE transactions on medical imaging
Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volu...

Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles.

IEEE journal of biomedical and health informatics
Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this paper, we explore the application of different machine learning techniques to the classification of...

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.

PloS one
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover...

Preconditioning 2D Integer Data for Fast Convex Hull Computations.

PloS one
In order to accelerate computing the convex hull on a set of n points, a heuristic procedure is often applied to reduce the number of points to a set of s points, s ≤ n, which also contains the same hull. We present an algorithm to precondition 2D da...

Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition.

IEEE transactions on pattern analysis and machine intelligence
This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and spa...

Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition.

IEEE transactions on pattern analysis and machine intelligence
This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation...

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis.

Bioinformatics (Oxford, England)
MOTIVATION: Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational pot...

Least square neural network model of the crude oil blending process.

Neural networks : the official journal of the International Neural Network Society
In this paper, the recursive least square algorithm is designed for the big data learning of a feedforward neural network. The proposed method as the combination of the recursive least square and feedforward neural network obtains four advantages ove...