Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.

A coevolutionary framework based on temporal and spatial ecology of host-parasite interactions:A missing link in studies of brood parasitism

A central tenet of coevolutionary theory,including theory of the coevolutionary relationship between brood parasites and their hosts,is that temporal and spatial patterns may reveal important information about ecological and evolutionary dynamics.For instance,level of genetic structure of populations provides important information about the role of genetics and gene ow in determining local patterns of selection on hosts due to parasitism(i.e.,egg rejection) and on parasites due to selection by hosts(i.e.,egg mimicry).Furthermore,abiotic(i.e.,climatic conditions) and biotic(phenotypic characteristics of animals) factors that also vary spatially may directly or indirectly a ect populations of hosts and brood parasites and,therefore,their interaction.By reviewing the literature,we found considerable evidence for an e ect of the spatially and temporally structured abiotic environment on the phenotype of both parasite and host eggs and the degree of mimicry.Moreover,we found examples suggesting that speci c life history characteristics of hosts that vary geographically and/or temporally may a ect the probability of initial colonization of a new host species and the direction and the speed of coevolution.We provide an exhaustive review of studies investigating temporal and spatial patterns of the interaction between brood parasites and their hosts.Such temporal and spatial trends in parasite and host traits are,together with genetic information on rejection and signi cant e ects of gene ow,consistent with coevolutionary dynamics.However,gene ow and changes in the temporal and spatial patterns of abundance of both parasites and hosts may result in frequent cases of counter-intuitive relationships between the phenotype of the parasite and that of the host(i.e.,poor or no mimicry),which may suggest limits to the degree of adaptation.We provide a list of scienti c questions in need of further investigation,concluding that studies of brood parasites and their hosts may play a central role in testing the geographic theory of coevolution and several alternative hypotheses.

Crack identification method of highway tunnel based on image processing

In this paper,the images of tunnel surface are obtained by tunnel lining rapid inspection system,and tunnel crack forest dataset(TCFD)is established.The disaster characteristics of tunnel cracks are analyzed and summarized.Solutions of tunnel crack segmentation(TCS)method are developed for the detection and recognition of cracks on tunnel lining.According to the image features of the tunnel lining and the optical principal of detection equipment,effective image pre-processing steps are carried out before crack extraction.The tunnel image of TCFD is divided into appropriate number of blocks to magnify the local features of tunnel cracks.Local threshold segmentation method is used to traverse the blocks successively,and the first target block with crack is obtained.The seed in the target block were obtained by adaptive localization method and mapped to the whole image.Region growing is performed through crack seed until complete tunnel crack is extracted.The results show that the precision,recall rate and F-measure of tunnel cracks under the TCS method can reach 92.58%,93.07%and 92.82%without strong interference.According to the binary images processed by TCS method,the projection images of different types of tunnel cracks and their respective laws are obtained.Furthermore,the TCS method is implemented and deployed as a GUI software application.