Feature selection for inductive generalization

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data.     

Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease

This paper presents an optimized cuttlefish algorithm for feature selection based on the traditional cuttlefish algorithm, which can be used for diagnosis of Parkinson’s disease at its early stage. Parkinson is a central nervous system disorder, caused due to the loss of brain cells. Parkinson's disease is incurable and could eventually lead to death but medications can help to control symptoms and elongate the patient's life to some extent. The proposed model uses the traditional cuttlefish algorithm as a search strategy to ascertain the optimal subset of features. The decision tree and k-nearest neighbor classifier as a judgment on the selected features. The Parkinson speech with multiple types of sound recordings and Parkinson Handwriting sample’s datasets are used to evaluate the proposed model. The proposed algorithm can be used in predicting the Parkinson’s disease with an accuracy of approximately 94% and help individual to have proper treatment at early stage. The experimental result reveals that the proposed bio-inspired algorithm finds an optimal subset of features, maximizing the accuracy, minimizing number of features selected and is more stable.     

EEG decoding of semantic category reveals distributed representations for single concepts

Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100 ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon.     

Classification of dog barks: a machine learning approach

In this study we analyzed the possible context-specific and individual-specific features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six different communicative situations was used as the sound sample. The algorithm's task was to learn which acoustic features of the barks, which were recorded in different contexts and from different individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identified contexts by identified dogs. After the best feature set had been obtained (with which the highest identification rate was achieved), the efficiency of the algorithm was tested in a classification task in which unknown barks were analyzed. The recognition rates we found were highly above chance level: the algorithm could categorize the barks according to their recorded situation with an efficiency of 43% and with an efficiency of 52% of the barking individuals. These findings suggest that dog barks have context-specific and individual-specific acoustic features. In our opinion, this machine learning method may provide an efficient tool for analyzing acoustic data in various behavioral studies.     

Diagnosis of Parkinson’s disease using modified grey wolf optimization

This paper presents the Modified Grey Wolf Optimization (MGWO) algorithm which helps with the identification of the symptoms of Parkinson’s disease at a premature stage. Parkinson disease is kind of a movement malady, which if not cured timely can prove to be fatal.Thus it becomes significant to identify Parkinson’s disease at its premature phase so proper medications can provide longevity to patient by controlling the symptoms. In this work, a new model named Modified Grey Wolf Optimization (MGWO) has been proposed grounded on the traditional Grey Wolf Optimizer (GWO), which acts as a search strategy for feature selection. GWO is a meta-heuristic algorithm which is enthused by hunt down behavior of wolves. Random forest, k-nearest neighbor classifier and decision tree espy on selected features. The proposed model is evaluated using various types of datasets of voice, handwriting (spiral and meander) and speech. The put forward algorithm helps in the prediction of Parkinson disease with an estimated accuracy of 94.83%, detection rate of 98.28%, false alarm rate of 16.03% and further aid the individuals to receive a functional treatment at an early stage. The proposed bio-inspired algorithm is stable enough to find out the optimal subset of features. At last the results derived from the evaluation of proposed algorithm on datasets are compared with the results of Optimized Cuttlefish Algorithm (OCFA). The experimental results depict that the proposed algorithm helps in maximizing the accurateness and minimizing the number of features selected.     

Valorisation de la recherche en santé humaine et protection des données à l’ère du numérique

Medical research collects a huge number of medical data sheltered in Data Centers. An European regulation rule (GDRP) or General Data Protection Regulation aims to give an ethic frame to protect personal data and delegate responsability to citizens.     

L’ADN,la reine des preuves imparfaites

Due to the progress of science and the stakes of inquiry and sentence, the DNA analysis is subject of a substantial development within the area of criminal procedure. However, the DNA is by no means a perfect evidence and it faces scientific, ethic and legal limits which result in reconsidering the balance between the stakes of punishment and the protection of Fundamental Freedoms.     

Le malade génétique responsable « La loi des suspects »

A patient suffers of a genetical disease. He does not wish to inform his family. Is he responsible for a lost of chances of his parents, chances to prevent the disease, or to receive care? Is there a second class of patients?     

An extended application ‘Brain Q’ processing EEG and MEG data of finger stimulation extended from ‘Zeffiro’ based on machine learning and signal processing

GoalTo apply signal processing and machine learning skills and knowledge in processing the EEG and MEG signal and further localize and evaluate the source of the finger stimulation.MethodsCognitive control is usually applied in information processing and behavioral response. In the preprocessing, baseline correction is implemented to analyze the pre-stimuli, combining ERP to mark the event related potential, studying the time-locked only behavior. Z-score transform, coherence and spec trum are calculated and analyzed in the functional connectivity analysis.In addition to the functional analysis, Bayes Optimizer evaluates the neuro imaging according to the hierarchical Bayes. The introduction of the application is described from both user and developer’s prospects. Results: Introduction of both user and developers aspects, on its modules from pre-processing, functional analysis and results visualization and evaluation is conducted with one specific clinical data case, including the correlation is higher especially on gamma band and the MVAR coherence on the whole source space depicting the relation between different regions, especially on somatosensory (compared by thalamus) when stimulated by finger activity, phase-lock property of the E/MEG signal and etc. Compared to a manual selection, the scaling parameter prediction can be improved with support vector machine (SVM). The evaluation results with Bayes Optimization, location prediction is superior in the somatosensory area and in the thalamus, the total reconstructed source space is larger, one of the realization of cognitive system comparing different kernels and classifiers. The SVM and discriminant classifier gives similar results evaluating the dipole localization and the parameter choice related as well to the shape parameter, noise level, hyperprior and etc.ConclusionApproaches of Brain Q are found to be suitable for pre-processing for the EEG and MEG data. The system is capable of functional analysis including coherence and spectral related computation. Machine learning techniques are conducted as well to analyze and evaluate the result of the dipole reconstruction and help to predict the better model parameters and the localization of the origin dipoles. A case on finger stimulation clinical data is conducted and the results of the analysis temporarily and spatially manifests its functionality for users and potential extensions for developers.     

Reading and reflecting sacred texts in the presence of the other. An empirical study on the use of ‘scriptural reasoning’ in teacher education

In this article the method of Scriptural Reasoning (SR), a text-based approach to interreligious dialogue between participants of the three Abrahamic religions, was implemented for a teacher education setting at a German university. Not only students with an outspoken religious conviction but also agnostic and atheist students, preparing themselves to become teachers in public schools, were invited into the conversation. The article documents and discusses the qualitative-empirical research in which the SR meetings were embedded. The aim of the article is not to create a hermeneutical theory for SR but rather to explore how SR as a method, with its specific learning tool of text-work, can be turned into a broader didactical model which can be transferred to other learning environments and which can in the long run provide empirical evidence on successful teacher education in multi-religious and multi-worldview societies and schools.