Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs

Alzheimer's disease (AD) is a neuro-degenerative disease that can cause dementia and result severe reduction in brain function inhibiting simple tasks especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD induced dementia and unpaid care for people with AD related dementia is valued at $271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for early detection of AD. We then give an overview of our dataset that was from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and propose a deep Convolutional Neural Network (CNN) architecture consisting of 7,866,819 parameters. This model has three different convolutional branches with each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three class accuracy.Comment: 11 pages, 7 figure

Fractional order fuzzy based virtual inertia controller design for frequency stability in isolated hybrid power systems

10.3390/en14061634Energies146163

Design and implementation of frequency controller for wind energy-based hybrid power system using quasi-oppositional harmonic search algorithm

10.3390/en14206459Energies1420645

Carbon sequestration and credit potential of gamhar (Gmelina arborea Roxb.) based agroforestry system for zero carbon emission of India

Abstract The agroforestry system is the best option to achieve the net zero carbon emissions target for India. Keeping this view, carbon sequestration and credit potential of gamhar based agroforestry system has been assessed. The experiment was carried out in randomized block design in seven different treatments with five replications. Gamhar tree biomass accumulation was higher in gamhar based agroforestry system compared to sole gamhar. Among different tree components, stem contributed a maximum to total gamhar tree biomass followed by roots, leaves and branches. The average contributions of stems, roots, leaves and branches in total tree biomass in two annual cycles (2016–17 and 2017–18) varied between 50 and 60, 19.8 and 20, 19.2 and 20, and 10.7 and 12.7 percent, respectively. In case of crops, above ground, below ground and total biomass was significantly higher in sole intercrops than gamhar based agroforestry system. Total (Tree + interrops + Soil) carbon stock, carbon sequestration, carbon credit and carbon price were significantly affected by treatments, and was maximum in Sole Greengram-Mustard. Net carbon emission was also recorded lowest in Sole Greengram-Mustard for which the values were 811.55% and 725.24% and 760.69% lower than Sole Gamhar in 2016–17, 2017–18 and in pooled data, respectively

Data_Sheet_1_Multi-location evaluation of mungbean (Vigna radiata L.) in Indian climates: Ecophenological dynamics, yield relation, and characterization of locations.doc

Crop yield varies considerably within agroecology depending on the genetic potential of crop cultivars and various edaphic and climatic variables. Understanding site-specific changes in crop yield and genotype × environment interaction are crucial and needs exceptional consideration in strategic breeding programs. Further, genotypic response to diverse agro-ecologies offers identification of strategic locations for evaluating traits of interest to strengthen and accelerate the national variety release program. In this study, multi-location field trial data have been used to investigate the impact of environmental conditions on crop phenological dynamics and their influence on the yield of mungbean in different agroecological regions of the Indian subcontinent. The present attempt is also intended to identify the strategic location(s) favoring higher yield and distinctiveness within mungbean genotypes. In the field trial, a total of 34 different mungbean genotypes were grown in 39 locations covering the north hill zone (n = 4), northeastern plain zone (n = 6), northwestern plain zone (n = 7), central zone (n = 11) and south zone (n = 11). The results revealed that the effect of the environment was prominent on both the phenological dynamics and productivity of the mungbean. Noticeable variations (expressed as coefficient of variation) were observed for the parameters of days to 50% flowering (13%), days to maturity (12%), reproductive period (21%), grain yield (33%), and 1000-grain weight (14%) across the environments. The genotype, environment, and genotype × environment accounted for 3.0, 54.2, and 29.7% of the total variation in mungbean yield, respectively (p 0.05) for all the genotypes except PM 14-11. Results revealed that the south zone environment initiated early flowering and an extended reproductive period, thus sustaining yield with good seed size. While in low rainfall areas viz., Sriganganagar, New Delhi, Durgapura, and Sagar, the yield was comparatively low irrespective of genotypes. Correlation results and PCA indicated that rainfall during the crop season and relative humidity significantly and positively influenced grain yield. Hence, the present study suggests that the yield potential of mungbean is independent of crop phenological dynamics; rather, climatic variables like rainfall and relative humidity have considerable influence on yield. Further, HA-GGE biplot analysis identified Sagar, New Delhi, Sriganganagar, Durgapura, Warangal, Srinagar, Kanpur, and Mohanpur as the ideal testing environments, which demonstrated high efficiency in the selection of new genotypes with wider adaptability.</p