Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction.

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique's effectiveness is confirmed by a fair comparison to existing procedures.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN.

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country's economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named "C19D-Net", to detect "COVID-19" infection from "Chest X-Ray" (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model ("C19D-Net") and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of "precision", "accuracy", "F1-score" and "recall" in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed "C19D-Net" can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

Perioperative management of a patient with left ventricular dysfunction and anomalous coronary arteries.

Anesthetic management of patients with coronary artery disease undergoing noncardiac surgery is quite challenging. Such patients are at increased risk of perioperative cardiac complications and death. We report an illustrative case of a 62-year-old male with ischemic heart disease and anomalous coronary arteries for obstructed paraumbilical hernia repair.

Three dimensional structure prediction and proton nuclear magnetic resonance analysis of toxic pesticides in human blood plasma.

The purpose of this study was to investigate the nuclear magnetic resonance (NMR) assignments of hydrolyzed products extracted from human blood plasma. The correlations between chemical, functional and structural properties of highly toxic pesticides were investigated using the PreADME analysis. We observed that toxic pesticides possessed higher molecular weight and, more hydrogen bond donors and acceptors when compared with less toxic pesticides. The occurrence of functional groups and structural properties was analyzed using (1)H-NMR. The (1)H-NMR spectra of the phosphomethoxy class of pesticides were characterized by methyl resonances at 3.7-3.9 ppm (δ) with the coupling constants of 11-16 Hz (JP-CH3 ). In phosphoethoxy pesticides, the methyl resonance was about 1.4 ppm (δ) with the coupling constant of 10 Hz (JP-CH2 ) and the methylene resonances was 4.2-4.4 ppm (δ) with the coupling constant of 0.8 Hz (JP-CH3 ), respectively. Our study shows that the values of four parameters such as chemical shift, coupling constant, integration and relaxation time correlated with the concentration of toxic pesticides, and can be used to characterise the proton groups in the molecular structures of toxic pesticides.

Computational interaction analysis of organophosphorus pesticides with different metabolic proteins in humans.

Pesticides have the potential to leave harmful effects on humans, animals, other living organisms, and the environment. Several human metabolic proteins inhibited after exposure to organophosphorus pesticides absorbed through the skin, inhalation, eyes and oral mucosa, are most important targets for this interaction study. The crystal structure of five different proteins, PDBIDs: 3LII, 3NXU, 4GTU, 2XJ1 and 1YXA in Homo sapiens (H. sapiens), interact with organophosphorus pesticides at the molecular level. The 3-D structures were found to be of good quality and validated through PROCHECK, ERRAT and ProSA servers. The results show that the binding energy is maximum -45.21 relative units of cytochrome P450 protein with phosmet pesticide. In terms of H-bonding, methyl parathion and parathion with acetylcholinesterase protein, parathion, methylparathion and phosmet with protein kinase C show the highest interaction. We conclude that these organophosphorus pesticides are more toxic and inhibit enzymatic activity by interrupting the metabolic pathways in H. sapiens.