Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs.It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image fusion.In this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images.First,the preprocessing was performed on the data.Then,the data augmentation techniques are used to handle overfitting.Also,the skull is removed to lead to good classification.In the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of each.Magnetic resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific organs.Positron emission tomography images can provide functional information and the metabolisms of particular tissues.This characteristic helps clinicians detect diseases and tumor progression at an early stage.Third,the feature extraction of fused images is extracted using a convolutional neural network.In the case of late fusion,the features are extracted first and then fused.Finally,the proposed system performs XGB to classify Alzheimer’s disease.The system’s performance was evaluated using accuracy,specificity,and sensitivity.All medical data were retrieved in the 2D format of 256×256 pixels.The classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was 2.The best number of trees for the random forest was 60;for the support vector machine,the maximum depth was 4,and the kernel gamma was 0.01.The system achieved an accuracy of 98.06%,specificity of 94.32%,and sensitivity of 97.02%in the case of early fusion.Also,if the system achieved late fusion,accuracy was 99.22%,specificity was 96.54%,and sensitivity was 99.54%.

Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

Timing and Classication of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

:Surface electromyogram(sEMG)processing and classication can assist neurophysiological standardization and evaluation and provide habitational detection.The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals.Understanding muscle activation timing allows identication of muscle locations and feature validation for precise modeling.This work aims to develop a predictive model to investigate and interpret Patellofemoral(PF)osteoarthritis based on features extracted from the sEMG signal using pattern classication.To this end,sEMG signals were acquired from ve core muscles over about 200 reads from healthy adult patients while they were going upstairs.Onset,offset,and time duration for the Transversus Abdominus(TrA),Vastus Medialis Obliquus(VMO),Gluteus Medius(GM),Vastus Lateralis(VL),and Multidus Muscles(ML)were acquired to construct a classication model.The proposed classication model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space.The activation feature space of muscle timing is used to train several large margin classiers to modulate muscle activations and account for such activation measurements.The fast large margin classier achieved higher performance and faster convergence than support vector machines(SVMs)and other state-of-the-art classiers.The proposed sEMG classication framework achieved an average accuracy of 98.8%after 7 s training time,improving other classication techniques in previous literature.

Crystal structure guided machine learning for the discovery and design of intrinsically hard materials

In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal chemistry.For this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning process.Based on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(XGB)algorithm.Experimental validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and Ta),Al_(2)O_(3),and FeB_(4),showed that the XGB model was able to reproduce load-dependent hardness behaviors of these compounds.In addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.