scholarly journals Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images

2016 ◽  
Author(s):  
Muhammad Yousefnezhad ◽  
Daoqiang Zhang
Keyword(s):  
Human Brain ◽  
Pattern Analysis ◽  
Binary Classification ◽  
Visual Stimuli ◽  
Experimental Studies ◽  
Active Regions ◽  
Superior Performance ◽  
Class Prediction ◽  
Feature Extraction Method ◽  
Adaboost Algorithm

AbstractA universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noises in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.

scholarly journals Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains

2017 ◽  
Author(s):  
Muhammad Yousefnezhad ◽  
Daoqiang Zhang
Keyword(s):  
Visual Stimuli ◽  
Experimental Studies ◽  
Active Regions ◽  
Neural Representation ◽  
Superior Performance ◽  
Fmri Data ◽  
Data Sets ◽  
Wide Range ◽  
The Cost ◽  
Novel Model

AbstractMultivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and the cost of brain studies. In overcoming these challenges, this paper proposes a novel model of neural representation, which can automatically detect the active regions for each visual stimulus and then utilize these anatomical regions for visualizing and analyzing the functional activities. Therefore, this model provides an opportunity for neuroscientists to ask this question: what is the effect of a stimulus on each of the detected regions instead of just study the fluctuation of voxels in the manually selected ROIs. Moreover, our method introduces analyzing snapshots of brain image for decreasing sparsity rather than using the whole of fMRI time series. Further, a new Gaussian smoothing method is proposed for removing noise of voxels in the level of ROIs. The proposed method enables us to combine different fMRI data sets for reducing the cost of brain studies. Experimental studies on 4 visual categories (words, consonants, objects and nonsense photos) confirm that the proposed method achieves superior performance to state-of-the-art methods.

scholarly journals Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques

2021 ◽  
Vol 3 (2) ◽  
pp. 294-312
Author(s):  
Muhammad E. H. Chowdhury ◽  
Tawsifur Rahman ◽  
Amith Khandakar ◽  
Mohamed Arselene Ayari ◽  
Aftab Ullah Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Experimental Studies ◽  
Plant Diseases ◽  
Superior Performance ◽  
World Population ◽  
Comparative Performance ◽  
Learning Techniques ◽  
Segmented Images ◽  
Segmentation Models

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature.

scholarly journals Tomato Leaf Diseases Detection Using Deep Learning Technique

2021 ◽  
Author(s):  
Muhammad E.H. Chowdhury ◽  
Tawsifur Rahman ◽  
Amith Khandakar ◽  
Nabil Ibtehaz ◽  
Aftab Ullah Khan ◽  
...  
Keyword(s):  
Binary Classification ◽  
Experimental Studies ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Superior Performance ◽  
Disease Monitoring ◽  
World Population ◽  
Network Architectures ◽  
Comparative Performance ◽  
Human Monitoring

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. DenseNet201 also outperform for six-class classification with an accuracy of 97.99%. Finally, DenseNet201 achieved an accuracy of 98.05% for ten-class classification. It can be concluded that deep architectures performed better at classifying the diseases for the three experiments. The performance of each of the experimental studies reported in this work outperforms the existing literature.

Protein Structural Class Prediction Based on Distance-related Statistical Features from Graphical Representation of Predicted Secondary Structure

2019 ◽  
Vol 16 (4) ◽  
pp. 317-324
Author(s):  
Liang Kong ◽  
Lichao Zhang ◽  
Xiaodong Han ◽  
Jinfeng Lv
Keyword(s):  
Feature Extraction ◽  
Secondary Structure ◽  
Protein Sequence ◽  
Function Analysis ◽  
Superior Performance ◽  
Support Vector ◽  
Chaos Game Representation ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class

Protein structural class prediction is beneficial to protein structure and function analysis. Exploring good feature representation is a key step for this prediction task. Prior works have demonstrated the effectiveness of the secondary structure based feature extraction methods especially for lowsimilarity protein sequences. However, the prediction accuracies still remain limited. To explore the potential of secondary structure information, a novel feature extraction method based on a generalized chaos game representation of predicted secondary structure is proposed. Each protein sequence is converted into a 20-dimensional distance-related statistical feature vector to characterize the distribution of secondary structure elements and segments. The feature vectors are then fed into a support vector machine classifier to predict the protein structural class. Our experiments on three widely used lowsimilarity benchmark datasets (25PDB, 1189 and 640) show that the proposed method achieves superior performance to the state-of-the-art methods. It is anticipated that our method could be extended to other graphical representations of protein sequence and be helpful in future protein research.

Automatic construction of academic profile: A case of information science domain

2021 ◽  
pp. 016555152199804
Author(s):  
Qian Geng ◽  
Ziang Chuai ◽  
Jian Jin
Keyword(s):  
Information Science ◽  
Binary Classification ◽  
Learning To Rank ◽  
Semantic Distance ◽  
Background Information ◽  
Training Dataset ◽  
Initial Vector ◽  
Specific Domain ◽  
Adaboost Algorithm ◽  
Domain Specific

To provide junior researchers with domain-specific concepts efficiently, an automatic approach for academic profiling is needed. First, to obtain personal records of a given scholar, typical supervised approaches often utilise structured data like infobox in Wikipedia as training dataset, but it may lead to a severe mis-labelling problem when they are utilised to train a model directly. To address this problem, a new relation embedding method is proposed for fine-grained entity typing, in which the initial vector of entities and a new penalty scheme are considered, based on the semantic distance of entities and relations. Also, to highlight critical concepts relevant to renowned scholars, scholars’ selective bibliographies which contain massive academic terms are analysed by a newly proposed extraction method based on logistic regression, AdaBoost algorithm and learning-to-rank techniques. It bridges the gap that conventional supervised methods only return binary classification results and fail to help researchers understand the relative importance of selected concepts. Categories of experiments on academic profiling and corresponding benchmark datasets demonstrate that proposed approaches outperform existing methods notably. The proposed techniques provide an automatic way for junior researchers to obtain organised knowledge in a specific domain, including scholars’ background information and domain-specific concepts.

Author Reputation Measurement on Question and Answer Sites by the Classification of Author-Generated Content

2021 ◽  
Vol 31 (10) ◽  
pp. 1421-1445
Author(s):  
Erhan Sezerer ◽  
Samet Tenekeci ◽  
Ali Acar ◽  
Bora Baloğlu ◽  
Selma Tekir
Keyword(s):  
Software Engineering ◽  
Design Patterns ◽  
Binary Classification ◽  
Grey Literature ◽  
Language Models ◽  
Superior Performance ◽  
Reputation Measurement ◽  
Objective Quality ◽  
Question And Answer ◽  
Dataset Size

In the field of software engineering, practitioners’ share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors’ mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves [Formula: see text] accuracy in binary classification in SO design patterns tag and [Formula: see text] accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system’s predicted reputation labels match the quality of provided answers.

scholarly journals ‘The awesome power of yeast’ in Alzheimer’s disease research

2021 ◽  
Vol 42 (3) ◽  
pp. 130
Author(s):  
Sudip Dhakal
Keyword(s):  
Simple Model ◽  
Animal Models ◽  
Human Brain ◽  
Human Cell ◽  
Cell Biology ◽  
Experimental Studies ◽  
Disease Models ◽  
Disease Research ◽  
Research Findings ◽  
Technological Platforms

The difficulties in performing experimental studies related to diseases of the human brain have fostered a range of disease models from highly expensive and complex animal models to simple, robust, unicellular yeast models. Yeast models have been used in numerous studies to understand Alzheimer’s disease (AD) pathogenesis and to search for drugs targeting AD. Thanks to the conservation of fundamental eukaryotic processes including ageing and the availability of appropriate technological platforms, budding yeast are a simple model eukaryote to assist with understanding human cell biology, offering a platform to study human diseases. This article aims to provide insights from yeast models on the contributions of amyloid beta, a causative agent in AD, and recent research findings on AD chemoprevention.

Development of Attention in Preterm Infants

PEDIATRICS ◽  
1976 ◽  
Vol 58 (5) ◽  
pp. 669-674
Author(s):  
Maureen Hack ◽  
Ann Mostow ◽  
Simon B. Miranda
Keyword(s):  
Human Brain ◽  
Preterm Infants ◽  
Visual Stimuli ◽  
Sensory Stimulation ◽  
Progressive Increase ◽  
Visual Fixation ◽  
Awake State

The quality of the awake state and attention in preterm infants has been evaluated by rating indices of attention such as widening of the eye, type of fixation, brightening, scanning, and cessation of sucking measured during visual fixation of patterns. Twenty-six infants ranging from 28 to 32 weeks' gestation at birth (mean, 31 weeks) were tested from one to four weeks postnatally until 36 weeks' gestation. Indices of attention were rated on a scale of 4 with an optimal mean index of 4. A progressive increase in behaviors associated with fixation of visual stimuli has been shown from 32 to 36 weeks of conceptual age. Mean scores ranged from 0.7 at 31 weeks' gestation to 1.8 at 34 weeks' and 2.7 at 36 weeks' gestation. The possibility therefore exists that by as early as 31 to 32 weeks from conception the human brain may be capable of waking states and thus able to process some sensory stimulation.

Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashutosh Shankhdhar ◽  
Pawan Kumar Verma ◽  
Prateek Agrawal ◽  
Vishu Madaan ◽  
Charu Gupta
Keyword(s):  
Human Brain ◽  
Network Architecture ◽  
Neural Control ◽  
Human Life ◽  
Binary Classification ◽  
Sampling Rate ◽  
Quality Analysis ◽  
Eeg Signal ◽  
Content Type

PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

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