scholarly journals Video Classification Using Deep Learning

2020 ◽  
pp. 406-413
Author(s):  
Sheshang Degadwala ◽  
Harsh Parekh ◽  
Nirav Ghodadra ◽  
Harsh Chauhan ◽  
Mashkoor Hussaini
Keyword(s):  
Deep Learning ◽  
Human Movement ◽  
Point Of View ◽  
High Dimensional ◽  
Video Classification ◽  
Wide Spread ◽  
Dynamic Scene ◽  
The Mind

Video order has been comprehensively investigated in PC vision in view of its wide spread applications. In any case, it remains a surprising task by virtue of the mind boggling troubles in fruitful segment extraction and successful arrangement with high-dimensional video depictions. Video groupings present uncommon irregularity as a result of monster scope changes, point of view assortment, and camera development, which pose fantastic challenges for both video depictions and characterization. With the phenomenal accomplishment of significant learning, convolutional neural frameworks (CNNs) and their 3-D varieties have been considered in the video territory for an immense grouping of order assignments. Video depictions have accepted a basically noteworthy activity in video examination, which authentically impact a complete execution of video characterization. Both the spatial and brief information should be gotten and encoded for extensive and educational depiction of video progressions. Significant Learning feature level blend plans have exceptional capacity of video depictions for improving the introduction of video grouping. We have driven wide assessment on four going after for video arrangement including human movement affirmation and dynamic scene grouping.

Non–human primate epidural ECoG analysis using explainable deep learning technology

2021 ◽  
Author(s):  
Hoseok Choi ◽  
Seokbeen Lim ◽  
Kyeongran Min ◽  
Kyoung-ha Ahn ◽  
Kyoung-Min Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Hand Movement ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Technology ◽  
3D Information

Abstract Objective: With the development in the field of neural networks, Explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results. Approach: We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment. Main results: The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements. Significance: As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.

Beérkezett könyvek

2008 ◽  
Vol 63 (4) ◽  
pp. 769-770
Author(s):  
Csaba Pléh
Keyword(s):  
New York ◽  
Point Of View ◽  
Cambridge University ◽  
New Science ◽  
Oxford University ◽  
History Of ◽  
The Mind ◽  
Oxford University Press

Danziger, Kurt: Marking the mind. A history of memory . Cambridge University Press, Cambridge, 2008Farkas, Katalin: The subject’s point of view. Oxford University Press, Oxford, 2008MosoninéFriedJudités TolnaiMárton(szerk.): Tudomány és politika. Typotex, Budapest, 2008Iacobini, Marco: Mirroring people. The new science of how we connect with others. Farrar, Straus and Giroux, New York, 2008Changeux, Jean-Pierre. Du vrai, du beau, du bien.Une nouvelle approche neuronale. Odile Jacob, PárizsGazzaniga_n

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

scholarly journals Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 222
Author(s):  
Juan C. Laria ◽  
M. Carmen Aguilera-Morillo ◽  
Enrique Álvarez ◽  
Rosa E. Lillo ◽  
Sara López-Taruella ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Variable Selection ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
A Priori ◽  
Simulated Data ◽  
Point Of View ◽  
High Dimensional ◽  
Whole Genome ◽  
Genome Context

Over the last decade, regularized regression methods have offered alternatives for performing multi-marker analysis and feature selection in a whole genome context. The process of defining a list of genes that will characterize an expression profile remains unclear. It currently relies upon advanced statistics and can use an agnostic point of view or include some a priori knowledge, but overfitting remains a problem. This paper introduces a methodology to deal with the variable selection and model estimation problems in the high-dimensional set-up, which can be particularly useful in the whole genome context. Results are validated using simulated data and a real dataset from a triple-negative breast cancer study.

scholarly journals Lower Limb Kinematics in Individuals with Hip Osteoarthritis during Gait: A Focus on Adaptative Strategies and Interlimb Symmetry

2021 ◽  
Vol 8 (4) ◽  
pp. 47
Author(s):  
Micaela Porta ◽  
Massimiliano Pau ◽  
Bruno Leban ◽  
Michela Deidda ◽  
Marco Sorrentino ◽  
...  
Keyword(s):  
Lower Limb ◽  
Functional Limitations ◽  
Hip Osteoarthritis ◽  
Movement Analysis ◽  
Human Movement ◽  
Point Of View ◽  
Negative Effects ◽  
Gait Patterns ◽  
Limb Kinematics

Among the functional limitations associated with hip osteoarthritis (OA), the alteration of gait capabilities represents one of the most invalidating as it may seriously compromise the quality of life of the affected individual. The use of quantitative techniques for human movement analysis has been found valuable in providing accurate and objective measures of kinematics and kinetics of gait in individuals with hip OA, but few studies have reported in-depth analyses of lower limb joint kinematics during gait and, in particular, there is a scarcity of data on interlimb symmetry. Such aspects were investigated in the present study which tested 11 individuals with hip OA (mean age 68.3 years) and 11 healthy controls age- and sex-matched, using 3D computerized gait analysis to perform point-by-point comparisons of the joint angle trends of hip, knee, and ankle. Angle-angle diagrams (cyclograms) were also built to compute several parameters (i.e., cyclogram area and orientation and Trend Symmetry) from which to assess the degree of interlimb symmetry. The results show that individuals with hip OA exhibit peculiar gait patterns characterized by severe modifications of the physiologic trend at hip level even in the unaffected limb (especially during the stance phase), as well as minor (although significant) alterations at knee and ankle level. The symmetry analysis also revealed that the effect of the disease in terms of interlimb coordination is present at knee joint as well as hip, while the ankle joint appears relatively preserved from specific negative effects from this point of view. The availability of data on such kinematic adaptations may be useful in supporting the design of specific rehabilitative strategies during both preoperative and postoperative periods.

Are we Ready for a “Microbiome-Guided Behaviour” Approach?

2019 ◽  
Vol 28 (04) ◽  
pp. 708-724
Author(s):  
ANDREA LAVAZZA ◽  
VITTORIO A. SIRONI
Keyword(s):  
Fecal Microbiota Transplantation ◽  
Negative Attitude ◽  
Well Being ◽  
Fecal Microbiota ◽  
Point Of View ◽  
Brain Functioning ◽  
The Central Nervous System ◽  
The Individual ◽  
The Right ◽  
The Mind

Abstract:The microbiome is proving to be increasingly important for human brain functioning. A series of recent studies have shown that the microbiome influences the central nervous system in various ways, and consequently acts on the psychological well-being of the individual by mediating, among others, the reactions of stress and anxiety. From a specifically neuroethical point of view, according to some scholars, the particular composition of the microbiome—qua microbial community—can have consequences on the traditional idea of human individuality. Another neuroethical aspect concerns the reception of this new knowledge in relation to clinical applications. In fact, attention to the balance of the microbiome—which includes eating behavior, the use of psychobiotics and, in the treatment of certain diseases, the use of fecal microbiota transplantation—may be limited or even prevented by a biased negative attitude. This attitude derives from a prejudice related to everything that has to do with the organic processing of food and, in general, with the human stomach and intestine: the latter have traditionally been regarded as low, dirty, contaminated and opposed to what belongs to the mind and the brain. This biased attitude can lead one to fail to adequately consider the new anthropological conceptions related to the microbiome, resulting in a state of health, both physical and psychological, inferior to what one might have by paying the right attention to the knowledge available today. Shifting from the ubiquitous high-low metaphor (which is synonymous with superior-inferior) to an inside-outside metaphor can thus be a neuroethical strategy to achieve a new and unbiased reception of the discoveries related to the microbiome.

scholarly journals Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts

Complexity ◽  
2003 ◽  
Vol 8 (4) ◽  
pp. 39-50 ◽  
Author(s):  
Stefan Häusler ◽  
Henry Markram ◽  
Wolfgang Maass
Keyword(s):  
Point Of View ◽  
High Dimensional ◽  
Low Dimensional

Еnactivist Criticism of the Neurobiological Theory of Consciousness

2021 ◽  
pp. 76-80
Author(s):  
Anastasia O. Shabalina ◽  
Keyword(s):  
Philosophy Of Mind ◽  
Point Of View ◽  
Science Methods ◽  
Neural Processes ◽  
Neural Substrate ◽  
Critical Revision ◽  
Concept Of Information ◽  
The Mind ◽  
The Brain ◽  
Person Experience

The article considers the main arguments against the neurobiological theory of consciousness from the point of view of the enactivist approach within the philosophy of mind. The neurobiological theory of consciousness, which reduces consciousness to neural activity, is currently the dominant approach to the mind-body problem. The neurobiological theory emerged as a result of advances in research on the phenomena of consciousness and through the development of technologies for visualizing the internal processes of mind. However, at the very heart of this theory, there is a number of logical contradictions. The non-reductive enactivist approach to consciousness, introduced in this article, contributes to the existing argumentation against the reduction of consciousness to neural processes with remonstrations that take into account the modern neuroscientific data. The article analyzes the argumentation of the sensorimotor enactivism developed by A. Noe and offers the account of the teleosemantic approach to the concept of information provided by R. Cao. The key problems of the neurobiological theory of consciousness are highlighted, and the objections emerging within the framework of the enactivist approach are analyzed. Since the main concepts on which the neural theory is based are the concepts of neural substrate, cognition as representation, and information as a unit of cognition, the author of the article presents three key enactivist ideas that oppose them. First, the enactivist concept of cognition as action allows us to consider the first-person experience as a mode of action, and not as a state of the brain substrate. Second, the article deals with the “explanatory externalism” argument proposed by Noe, who refutes the image of cognition as a representation in the brain. Finally, in order to critically revise the concept of information as a unit of cognition, the author analyzes Cao’s idea, which represents a teleosemantic approach, but is in line with the general enactivist argumentation. Cao shows that the application of the concept “information” to neural processes is problematic: no naturalized information is found in the brain as a physical substrate. A critical revision of beliefs associated with the neural theory of consciousness leads us to recognize that there are not enough grounds for reducing consciousness to processes that take place in the brain. That is why Noe calls expectations that the visualization of processes taking place in the brain with the help of the modern equipment will be able to depict the experience of consciousness the “new phrenology”, thus indicating the naive character of neural reduction. The article concludes that natural science methods are insufficient for the study of consciousness.

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