AI Medical Compendium Topic:
Models, Theoretical

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How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

Journal of biomedical informatics
It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future...

Extreme learning machine based optimal embedding location finder for image steganography.

PloS one
In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location wi...

Data-driven system to predict academic grades and dropout.

PloS one
Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help ...

H state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through Fading channels.

Neural networks : the official journal of the International Neural Network Society
In this paper, the H state estimation problem is investigated for a class of uncertain discrete-time neural networks subject to infinitely distributed delays and fading channels. Randomly occurring uncertainties (ROUs) are introduced to reflect the r...

Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

Environmental science and pollution research international
Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling resul...

Ranking Support Vector Machine with Kernel Approximation.

Computational intelligence and neuroscience
Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-a...

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PloS one
Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such ...

Adaptive low-rank subspace learning with online optimization for robust visual tracking.

Neural networks : the official journal of the International Neural Network Society
In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for ...

Clinical Word Sense Disambiguation with Interactive Search and Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Resolving word ambiguity in clinical text is critical for many natural language processing applications. Effective word sense disambiguation (WSD) systems rely on training a machine learning based classifier with abundant clinical text that is accura...

Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.

AMIA ... Annual Symposium proceedings. AMIA Symposium
An important aspect of post-marketing drug surveillance involves identifying potential side-effects utilizing adverse drug event (ADE) reporting systems and/or Electronic Health Records. These data are noisy, necessitating identified drug/ADE associa...