Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and more.Protein secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure prediction.Protein secondary structures are classified into two categories:3-state category and 8-state category.Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,respectively.The 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,Q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on Q3 prediction.In this paper,we develop an ensemble Machine Learning(ML)approach for Q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in Q8 PSSP.The ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),RF(Random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in Q8 PSSP.The novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP problem.The experimental results confirmed that ensemble ML models are more accurate than individual ML models.They also indicated that features extracted by LDA are more effective than those extracted by PCA.

Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm

Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.

Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs

BACKGROUND Deep learning,a form of artificial intelligence,has shown promising results for interpreting radiographs.In order to develop this niche machine learning(ML)program of interpreting orthopedic radiographs with accuracy,a project named deep learning algorithm for orthopedic radiographs was conceived.In the first phase,the diagnosis of knee osteoarthritis(KOA)as per the standard Kellgren-Lawrence(KL)scale in medical images was conducted using the deep learning algorithm for orthopedic radiographs.AIM To compare efficacy and accuracy of eight different transfer learning deep learning models for detecting the grade of KOA from a radiograph and identify the most appropriate ML-based model for the detecting grade of KOA.METHODS The study was performed on 2068 radiograph exams conducted at the Department of Orthopedic Surgery,Sir HN Reliance Hospital and Research Centre(Mumbai,India)during 2019-2021.Three orthopedic surgeons reviewed these independently,graded them for the severity of KOA as per the KL scale and settled disagreement through a consensus session.Eight models,namely ResNet50,VGG-16,InceptionV3,MobilnetV2,EfficientnetB7,DenseNet201,Xception and NasNetMobile,were used to evaluate the efficacy of ML in accurately classifying radiographs for KOA as per the KL scale.Out of the 2068 images,70%were used initially to train the model,10%were used subsequently to test the model,and 20%were used finally to determine the accuracy of and validate each model.The idea behind transfer learning for KOA grade image classification is that if the existing models are already trained on a large and general dataset,these models will effectively serve as generic models to fulfill the study’s objectives.Finally,in order to benchmark the efficacy,the results of the models were also compared to a first-year orthopedic trainee who independently classified these models according to the KL scale.RESULTS Our network yielded an overall high accuracy for detecting KOA,ranging from 54%to 93%.The most successful of these was the DenseNet model,with accuracy up to 93%;interestingly,it even outperformed the human first-year trainee who had an accuracy of 74%.CONCLUSION The study paves the way for extrapolating the learning using ML to develop an automated KOA classification tool and enable healthcare professionals with better decision-making.

Fast and Robust DCNN Based Lithography SEM Image Contour Extraction Models

Scanning electron microscope(SEM)metrology is critical in semiconductor manufacturing for patterning process quality assessment and monitoring.Besides feature width and feature-feature space dimension measurements from critical dimension SEM(CDSEM)images,visual inspection of SEM image also offers rich information on the quality of patterning.However,visual inspection alone leaves considerable room of ambiguity regarding patterning quality.To narrow the room of ambiguity and to obtain more statistically quantitative information on patterning quality,SEM-image contours are often extracted to serve such purposes.From contours,important information such as critical dimension and resist sidewall angle at any location can be estimated.Those geometrical information can be used for optical proximity correction(OPC)model verification and lithography hotspot detection,etc.Classical contour extraction algorithms based on local information have insufficient capability in dealing with noisy and low contrast images.To achieve reliable contours from noisy and low contrast images,information beyond local should be made use of as much as possible.In this regard,deep convolutional neural network(DCNN)has proven its great capability,as manifested in various computer vision tasks.Taking the full advantages of this maturing technology,we have designed a DCNN network and applied it to the task of extracting contours from noisy and low contrast SEM images.It turns out that the model is capable of separating the resist top and bottom contours reliably.In addition,the model does not generate false contours,it also can suppress the generation of broken contours when ambiguous area for contour extraction is small and non-detrimental.With advanced image alignment algorithm with sub-pixel accuracy,contours from different exposure fields of same process condition can be superposed to estimate process variation band,furthermore,stochastic effect induced edge placement variation statistics can easily be inferred from the extracted contours.