Showing papers in "Neuroscience Informatics in 2022"

TL;DR: In this article , a convolutional neural network (CNN) was used to segment brain tumours from 2D Magnetic Resonance brain Images (MRI) followed by traditional classifiers and deep learning methods.

TL;DR: In this article , a Leaky Relu activated Deep Neural Network (LRA-DNN) was proposed for emotion extraction from text, which comes under four categories, such as pre-processing, feature extraction, ranking and classification.

TL;DR: In this article , the authors present a systematic review of smart health monitoring system along with recent advancements in SHM with existing challenges and propose a solution to the outbreak and emergences diseases.

TL;DR: In this article , four neural network-based deep learning architectures namely MLP, CNN, RNN and RNN with LSTM, and two supervised machine learning techniques such as SVM and LR are implemented to investigate and compare their suitability to track the mental depression from EEG Data.

TL;DR: In this article , the performance of all EfficientNet variants on this imbalanced multiclass classification task using metrics such as Precision, Recall, Accuracy, F1 Score, and Confusion Matrices was evaluated.

TL;DR: In this article , the authors proposed a simple way to achieve this objective using some fundamental Machine Learning tools such as TensorFlow, Keras, OpenCV and Scikit-learn.

TL;DR: In this paper , a fuzzy min-max neural network is used to classify normal and abnormal tissues such as GM, CSF, WM, OCS, and OSS, which helps to classify endocrine tissues.

TL;DR: In this paper , a novel deep triplet network was employed as a metric learning approach to brain MRI analysis and Alzheimer's detection, which used a conditional loss function to overcome the lack of limited samples and improved the accuracy of the model.

TL;DR: In this article , the authors used CNNs to identify variations in chest CT scans, with accuracies ranging from 91% to 98% and achieved 98.38% accuracy, respectively.

TL;DR: In this article , the authors presented a secure blockchain security module for BCI with multimedia life cycle framework (MLCF) (BSM-BCIMLCF), which homogenizes a Blockchain-based distributed permission network approach to overcome existing challenges.

TL;DR: In this paper , a Convolutional Neural Network (CNN) was used for digitization of Devanagari handwritten text recognition (DHTR) using 46 classes of characters and each class has two thousand different images.

TL;DR: In this paper , the authors proposed the dynamic architecture of multilevel layer modelling in Faster R-CNN (MLL-CNN) approach based on feature weight factor and relative description model to build the selected features.

TL;DR: In this paper , the authors describe some of the possible influences of artificial intelligence technologies on pediatric physiotherapy practice, and the subsequent ways in which physiotherapy education will need to change to graduate professionals who are fit for practice in the 21st century health system for promoting safe and effective use of Artificial Intelligence and the delivery of Pediatric Physical Therapy care to people.

TL;DR: In this paper , the authors have observed the variation in shape and size for different precursor (0.45 M - Zn acetate dihydrate, Zn nitrate hexahydrate) with aloe vera extract ZnO nanoparticles, data analytics have been prepared with annealing at 650 ∘C.

TL;DR: In this article , a rule-based approach predicated on picture fuzzy sets is proposed to enable intelligent clinical decision support system which builds on previous research to create an approach based on Picture Fuzzy Sets.

TL;DR: A survey of deep learning techniques for the diagnosis of osteoarthritis and rheumatoid arthritis can be found in this article , where the authors identify open problems and research gaps.

TL;DR: In this article , several approaches such as RCNN (Region-based Convolutional Neural-Network), Fast R-CNN (Fast Region-based CNN), Faster R-RCNN (Faster Region-CNN with Region proposal Network), YOLO (You Only Look Once), SSD (Single-Shot Multibox Detector) and Efficient-Det are listed which can be used for stroke localization and classification.

TL;DR: In this paper , the authors used the winner algorithm of the Brain Tumor Segmentation challenge 2018, trained on the BraTS 2020 dataset, with the objective to segment necrotic core, peritumoral edema, and enhancing tumor.

TL;DR: In this article , a one dimensional convolutional neural network (CNN) was proposed as a model among supervised deep learning for HAR data classification, and seven approaches based on metaheuristic algorithms were investigated.

TL;DR: A short review of invasive and non-invasive brain temperature monitoring sensors and tools is presented in this article , where the authors discuss the type of temperature sensors that can be integrated with probes.

TL;DR: Wang et al. as discussed by the authors proposed a new model Cuckoo Search with Invasive weed optimization based Extreme Learning Machine (CSIWO-ELM) to optimize input weight and hidden neurons.

TL;DR: A stereotactic multi-sequences of neuroimages of the dissected brain with non-dissected 3D structures/systems of interest is presented in the NOWinBRAIN 3D neuroimage repository as mentioned in this paper .

TL;DR: In this paper , a review of the literature related to PD diagnosis, its stages, and its management using data mining techniques (DMT) was performed by exploring the Scopus indexed literature using the query containing the keywords data-mining and Parkinson's disease.

TL;DR: In this paper , the Savitzky-Golay (SG) filter was used to record and analyze the data from 19 EEG channels and the EEG signals of the patients with epileptic seizures were then separated from those of the control individuals using decision tree and random forest (RF) classifiers.

TL;DR: In this article , a multistage Dual-Path Interactive Refinement Network (DPIRef-Net) is proposed for segmenting the vascular maps of arteries and veins from the retinal surface.

TL;DR: In this paper , a practical and easy-to-follow Zipfel's classification for Dural arteriovenous fistula (DAVF) based on new natural history data was evaluated.

TL;DR: In this article , electroglottography is employed instead of microphones as a source for pitch-tracking, which improves both the accuracy of pitch evaluation and the ease of voice information processing; it provides a direct measurement of the vocal folds' activity and bypasses the interferences caused by external sound sources.

TL;DR: In this article , the properties of combustion-formed mixed sulfide solids were investigated in solutions which are supersaturated simultaneously with dysprosium and erbium, based on the interactions between thiourea.

TL;DR: In this article , a new approach for feature extraction called Feature Extraction Based on Region of Mines (FE_mines) is presented that includes three versions to deal with different medical images; this approach obtains multiple formulas for each image using the signal and image processing, then data distribution skew is used to calculate three statistical measurements that include the hidden features, which leads to increased discrimination among classes to build powerful models with better performance.