An Enhanced CNN-based ELM Classification for Disease Prediction in the Rice Crop

To meet the demands of a constantly expanding population, intensive farming is becoming more popular in the modern day. This strategy, meanwhile, increases the possibility of a wider range of plant illnesses. By reducing crop productivity in terms of both quantity and quality, these infections represent a threat to food production and ultimately result in a fall in the economy. Fortunately, new opportunities for early diagnosis of such epidemics have emerged because of technological improvements, which are advantageous for society as a whole. The difficulties created by technology and bio-mutations create a potential for additional breakthroughs, notwithstanding the significant contributions made by researchers in the field of agricultural disease diagnosis. The suggested framework comprises three key phases: preprocessing, feature extraction, and the classification of leaf diseases. To optimize computational resources and memory utilization, the input image undergoes pre-processing as a preliminary step. Afterward, a Convolutional Neural Network (CNN) is utilized on an extensive dataset of labeled images to capture pertinent features for the diagnosis of rice leaf diseases. The suggested model utilizes an Efficient Selective Pruning of Hidden Nodes (ELM) classifier based on the RBF kernel to classify the input data

An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks

Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization

Inhibition of cathepsin B activity attenuates extracellular matrix degradation and inflammatory breast cancer invasion

Abstract Introduction Inflammatory breast cancer (IBC) is an aggressive, metastatic and highly angiogenic form of locally advanced breast cancer with a relatively poor three-year survival rate. Breast cancer invasion has been linked to proteolytic activity at the tumor cell surface. Here we explored a role for active cathepsin B on the cell surface in the invasiveness of IBC. Methods We examined expression of the cysteine protease cathepsin B and the serine protease urokinase plasminogen activator (uPA), its receptor uPAR and caveolin-1 in two IBC cell lines: SUM149 and SUM190. We utilized a live cell proteolysis assay to localize in real time the degradation of type IV collagen by IBC cells. IBC patient biopsies were examined for expression of cathepsin B and caveolin-1. Results Both cell lines expressed comparable levels of cathepsin B and uPA. In contrast, levels of caveolin-1 and uPAR were greater in SUM149 cells. We observed that uPA, uPAR and enzymatically active cathepsin B were colocalized in caveolae fractions isolated from SUM149 cells. Using a live-cell proteolysis assay, we demonstrated that both IBC cell lines degrade type IV collagen. The SUM149 cells exhibit predominantly pericellular proteolysis, consistent with localization of proteolytic pathway constitutents to caveolar membrane microdomains. A functional role for cathepsin B was confirmed by the ability of CA074, a cell impermeable and highly selective cathepsin B inhibitor, to significantly reduce pericellular proteolysis and invasion by SUM149 cells. A statistically significant co-expression of cathepsin B and caveolin-1 was found in IBC patient biopsies, thus validating our in vitro data. Conclusion Our study is the first to show that the proteolytic activity of cathepsin B and its co-expression with caveolin-1 contributes to the aggressiveness of IBC

Conventional and Molecular Breeding Approaches for Biofortification of Pearl Millet

Pearl millet [Pennisetum glaucum (L.) R. Br.] is an essential diet of more than 90 million people in the semi-arid tropics of the world where droughts and low fertility of soils cause frequent failures of other crops. It is an important nutri-rich grain cereal in the drier regions of the world grown on 26 mha by millions of farmers (IFAD 1999; Yadav and Rai 2013). This makes pearl millet the sixth most important crop in the world and fourth most important food crop of the India, next to rice, wheat, and maize with annual cultivation over an area of ~8 mha. Pearl millet is also primary food crop in sub-Saharan Africa and is grown on 15 mha (Yadav and Rai 2013). The significant increase in productivity of pearl millet in India is attributed to development and adoption of hybrids of early to medium duration maturity. More than 120 diverse hybrids/varieties have been released till date for various production environments. The heterosis breeding and improved crop management technologies increased productivity substantially achieving higher increased production of 9.80 mt in 2016–2017 from 2.60 mt in 1950–1951 in spite of declined of area under the crop by 20–30% over last two decades (Yadav et al. 2012)

Building convolutional neural network parameters using genetic algorithm for the croup cough classification problem

Croup cough is an infection in the upper airway typically occurs in children from age six month to 3 years. Symptoms of croup cough begin with a normal cold, fever and loud barking makes the child difficult to breath. These symptoms are relatively similar with a recent pandemic SARS-COV2. So, the common symptoms of croup cough and SARS-COV2 is urges the physicians to diagnose the infection at early stage. Typically, clinical professions Computer Aided Diagnose system (CADS) for detecting the abnormalities from chest X-Ray (PA View) and CT images of infants. Most of CADS adopted the deep learning technique for classification of radiograph images due to the its ability in term of accuracy rate. Classification accuracy of deep learning techniques like Convolution Neural Network (CNN) highly relays on the weights of convolution filters and fully connected layer. In this work, we propose the optimized CNN using Genetic algorithm (GA) for classification of croup cough images. This work includes optimizing weights of CNN with different batch size and iterations using genetic algorithm to identify the best weights for the classifier to generate maximum accuracy. The experiments were carried out with croup cough image dataset, and we show the promising performance of proposed method of 88.32% accuracy rate with smaller amount of dataset

Design and Development of Optimal and Deep-Learning-Based Demand Response Technologies for Residential Hybrid Renewable Energy Management System

The principal goal of this study is to conduct a techno-economic analysis of hybrid energy generation designs for residential-form houses in urban areas. Various possibilities for a form house electrification system are created and simulated in order to determine an optimum ideal configuration for meeting residential load demand with an increase in energy capacity and minimal investment. Using NREL’s HOMER optimization tool, a case-study-based virtual HRE model is developed. Pre-assessment data and relevant operation constraints are used to build the system’s objective functions. The instantaneous energy balance algorithm technique is used to solve the multi-objective function. The overall optimization procedure is sandwiched between two supporting advanced approaches, pre- and post-operations. The development of an optimal techno-economic hybrid energy generation system for the smooth fulfillment of urban load demand is aided by novel deep belief network (NDBN)-based pre-stage load demand predictions and an analysis of the necessary demand side management (DSM)practicing code for utility efficiency improvements in post-stage simulations